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S.cloneElement(l,c)},{component:"SignOutButton",renderWhileLoading:!0});St(({clerk:e,children:t,...n})=>{const{appearance:r,unsafeMetadata:o,fallbackRedirectUrl:a,forceRedirectUrl:l,signInFallbackRedirectUrl:u,signInForceRedirectUrl:s,mode:c,initialValues:d,oauthFlow:m,...g}=n;t=ku(t,"Sign up");const C=wu(t)("SignUpButton"),p=()=>{const x={fallbackRedirectUrl:a,forceRedirectUrl:l,signInFallbackRedirectUrl:u,signInForceRedirectUrl:s,initialValues:d,oauthFlow:m};return c==="modal"?e.openSignUp({...x,appearance:r,unsafeMetadata:o}):e.redirectToSignUp({...x,signUpFallbackRedirectUrl:a,signUpForceRedirectUrl:l})},h={...g,onClick:async x=>(C&&typeof C=="object"&&"props"in C&&await Au(C.props.onClick)(x),p())};return S.cloneElement(C,h)},{component:"SignUpButton",renderWhileLoading:!0});var oI=()=>({fields:{identifier:null,password:null,code:null},raw:null,global:null}),aI=()=>({fields:{firstName:null,lastName:null,emailAddress:null,phoneNumber:null,password:null,username:null,code:null,captcha:null,legalAccepted:null},raw:null,global:null}),iI=class{constructor(e){this.isomorphicClerk=e,this.signInSignalProxy=this.buildSignInProxy(),this.signUpSignalProxy=this.buildSignUpProxy()}signInSignal(){return this.signInSignalProxy}signUpSignal(){return this.signUpSignalProxy}buildSignInProxy(){const e=this.gateProperty.bind(this),t=()=>this.client.signIn.__internal_future;return{errors:oI(),fetchStatus:"idle",signIn:{status:"needs_identifier",availableStrategies:[],isTransferable:!1,get id(){return e(t,"id",void 0)},get supportedFirstFactors(){return e(t,"supportedFirstFactors",[])},get supportedSecondFactors(){return e(t,"supportedSecondFactors",[])},get secondFactorVerification(){return 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loaded")}})},create:this.gateMethod(t,"create"),password:this.gateMethod(t,"password"),sso:this.gateMethod(t,"sso"),finalize:this.gateMethod(t,"finalize"),emailCode:this.wrapMethods(()=>t().emailCode,["sendCode","verifyCode"]),emailLink:this.wrapStruct(()=>t().emailLink,["sendLink","waitForVerification"],["verification"],{verification:null}),resetPasswordEmailCode:this.wrapMethods(()=>t().resetPasswordEmailCode,["sendCode","verifyCode","submitPassword"]),phoneCode:this.wrapMethods(()=>t().phoneCode,["sendCode","verifyCode"]),mfa:this.wrapMethods(()=>t().mfa,["sendPhoneCode","verifyPhoneCode","verifyTOTP","verifyBackupCode"]),ticket:this.gateMethod(t,"ticket"),passkey:this.gateMethod(t,"passkey"),web3:this.gateMethod(t,"web3")}}}buildSignUpProxy(){const e=this.gateProperty.bind(this),t=this.gateMethod.bind(this),n=this.wrapMethods.bind(this),r=()=>this.client.signUp.__internal_future;return{errors:aI(),fetchStatus:"idle",signUp:{get id(){return e(r,"id",void 0)},get 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e(r,"isTransferable",!1)},create:t(r,"create"),update:t(r,"update"),sso:t(r,"sso"),password:t(r,"password"),ticket:t(r,"ticket"),web3:t(r,"web3"),finalize:t(r,"finalize"),verifications:n(()=>r().verifications,["sendEmailCode","verifyEmailCode","sendPhoneCode","verifyPhoneCode"])}}}__internal_effect(e){throw new Error("__internal_effect called before Clerk is loaded")}__internal_computed(e){throw new Error("__internal_computed called before Clerk is loaded")}get client(){const e=this.isomorphicClerk.client;if(!e)throw new Error("Clerk client not ready");return e}gateProperty(e,t,n){return!Vl()||!this.isomorphicClerk.loaded?n:e()[t]}gateMethod(e,t){return(async(...n)=>{if(!Vl())return vr.throw(`Attempted to call a method (${t}) that is not supported on the server.`);this.isomorphicClerk.loaded||await new Promise(o=>this.isomorphicClerk.addOnLoaded(o));const r=e();return r[t].apply(r,n)})}wrapMethods(e,t){return Object.fromEntries(t.map(n=>[n,this.gateMethod(e,n)]))}wrapStruct(e,t,n,r){const o={};for(const a of t)o[a]=this.gateMethod(e,a);for(const a of n)Object.defineProperty(o,a,{get:()=>this.gateProperty(e,a,r[a]),enumerable:!0});return o}};typeof globalThis.__BUILD_DISABLE_RHC__>"u"&&(globalThis.__BUILD_DISABLE_RHC__=!1);var lI={name:"@clerk/clerk-react",version:"5.61.3",environment:"production"},vl,Wo,Uo,Ur,fr,Cl,qr,Ya,bl,Sp=class Mp{constructor(t){Wr(this,Ya),this.clerkjs=null,this.preopenOneTap=null,this.preopenUserVerification=null,this.preopenEnableOrganizationsPrompt=null,this.preopenSignIn=null,this.preopenCheckout=null,this.preopenPlanDetails=null,this.preopenSubscriptionDetails=null,this.preopenSignUp=null,this.preopenUserProfile=null,this.preopenOrganizationProfile=null,this.preopenCreateOrganization=null,this.preOpenWaitlist=null,this.premountSignInNodes=new Map,this.premountSignUpNodes=new Map,this.premountUserAvatarNodes=new Map,this.premountUserProfileNodes=new Map,this.premountUserButtonNodes=new Map,this.premountOrganizationProfileNodes=new Map,this.premountCreateOrganizationNodes=new Map,this.premountOrganizationSwitcherNodes=new Map,this.premountOrganizationListNodes=new Map,this.premountMethodCalls=new Map,this.premountWaitlistNodes=new Map,this.premountPricingTableNodes=new Map,this.premountAPIKeysNodes=new Map,this.premountOAuthConsentNodes=new Map,this.premountTaskChooseOrganizationNodes=new Map,this.premountTaskResetPasswordNodes=new Map,this.premountTaskSetupMFANodes=new Map,this.premountAddListenerCalls=new Map,this.loadedListeners=[],Wr(this,vl,"loading"),Wr(this,Wo),Wr(this,Uo),Wr(this,Ur),Wr(this,fr,rI()),Wr(this,Cl),this.buildSignInUrl=o=>{const a=()=>{var l;return((l=this.clerkjs)==null?void 0:l.buildSignInUrl(o))||""};if(this.clerkjs&&this.loaded)return a();this.premountMethodCalls.set("buildSignInUrl",a)},this.buildSignUpUrl=o=>{const a=()=>{var l;return((l=this.clerkjs)==null?void 0:l.buildSignUpUrl(o))||""};if(this.clerkjs&&this.loaded)return a();this.premountMethodCalls.set("buildSignUpUrl",a)},this.buildAfterSignInUrl=(...o)=>{const a=()=>{var l;return((l=this.clerkjs)==null?void 0:l.buildAfterSignInUrl(...o))||""};if(this.clerkjs&&this.loaded)return a();this.premountMethodCalls.set("buildAfterSignInUrl",a)},this.buildAfterSignUpUrl=(...o)=>{const a=()=>{var l;return((l=this.clerkjs)==null?void 0:l.buildAfterSignUpUrl(...o))||""};if(this.clerkjs&&this.loaded)return a();this.premountMethodCalls.set("buildAfterSignUpUrl",a)},this.buildAfterSignOutUrl=()=>{const o=()=>{var a;return((a=this.clerkjs)==null?void 0:a.buildAfterSignOutUrl())||""};if(this.clerkjs&&this.loaded)return o();this.premountMethodCalls.set("buildAfterSignOutUrl",o)},this.buildNewSubscriptionRedirectUrl=()=>{const o=()=>{var a;return((a=this.clerkjs)==null?void 0:a.buildNewSubscriptionRedirectUrl())||""};if(this.clerkjs&&this.loaded)return o();this.premountMethodCalls.set("buildNewSubscriptionRedirectUrl",o)},this.buildAfterMultiSessionSingleSignOutUrl=()=>{const o=()=>{var a;return((a=this.clerkjs)==null?void 0:a.buildAfterMultiSessionSingleSignOutUrl())||""};if(this.clerkjs&&this.loaded)return o();this.premountMethodCalls.set("buildAfterMultiSessionSingleSignOutUrl",o)},this.buildUserProfileUrl=()=>{const o=()=>{var a;return((a=this.clerkjs)==null?void 0:a.buildUserProfileUrl())||""};if(this.clerkjs&&this.loaded)return o();this.premountMethodCalls.set("buildUserProfileUrl",o)},this.buildCreateOrganizationUrl=()=>{const o=()=>{var a;return((a=this.clerkjs)==null?void 0:a.buildCreateOrganizationUrl())||""};if(this.clerkjs&&this.loaded)return o();this.premountMethodCalls.set("buildCreateOrganizationUrl",o)},this.buildOrganizationProfileUrl=()=>{const o=()=>{var a;return((a=this.clerkjs)==null?void 0:a.buildOrganizationProfileUrl())||""};if(this.clerkjs&&this.loaded)return o();this.premountMethodCalls.set("buildOrganizationProfileUrl",o)},this.buildWaitlistUrl=()=>{const o=()=>{var a;return((a=this.clerkjs)==null?void 0:a.buildWaitlistUrl())||""};if(this.clerkjs&&this.loaded)return o();this.premountMethodCalls.set("buildWaitlistUrl",o)},this.buildTasksUrl=()=>{const o=()=>{var a;return((a=this.clerkjs)==null?void 0:a.buildTasksUrl())||""};if(this.clerkjs&&this.loaded)return o();this.premountMethodCalls.set("buildTasksUrl",o)},this.buildUrlWithAuth=o=>{const a=()=>{var l;return((l=this.clerkjs)==null?void 0:l.buildUrlWithAuth(o))||""};if(this.clerkjs&&this.loaded)return a();this.premountMethodCalls.set("buildUrlWithAuth",a)},this.handleUnauthenticated=async()=>{const o=()=>{var a;return(a=this.clerkjs)==null?void 0:a.handleUnauthenticated()};this.clerkjs&&this.loaded?o():this.premountMethodCalls.set("handleUnauthenticated",o)},this.on=(...o)=>{var a;if((a=this.clerkjs)!=null&&a.on)return this.clerkjs.on(...o);Dt(this,fr).on(...o)},this.off=(...o)=>{var a;if((a=this.clerkjs)!=null&&a.off)return this.clerkjs.off(...o);Dt(this,fr).off(...o)},this.addOnLoaded=o=>{this.loadedListeners.push(o),this.loaded&&this.emitLoaded()},this.emitLoaded=()=>{this.loadedListeners.forEach(o=>o()),this.loadedListeners=[]},this.beforeLoad=o=>{if(!o)throw new Error("Failed to hydrate latest Clerk JS")},this.hydrateClerkJS=o=>{var a,l;if(!o)throw new Error("Failed to hydrate latest Clerk JS");return this.clerkjs=o,this.premountMethodCalls.forEach(u=>u()),this.premountAddListenerCalls.forEach((u,s)=>{u.nativeUnsubscribe=o.addListener(s)}),(a=Dt(this,fr).internal.retrieveListeners("status"))==null||a.forEach(u=>{this.on("status",u,{notify:!0})}),(l=Dt(this,fr).internal.retrieveListeners("queryClientStatus"))==null||l.forEach(u=>{this.on("queryClientStatus",u,{notify:!0})}),this.preopenSignIn!==null&&o.openSignIn(this.preopenSignIn),this.preopenCheckout!==null&&o.__internal_openCheckout(this.preopenCheckout),this.preopenPlanDetails!==null&&o.__internal_openPlanDetails(this.preopenPlanDetails),this.preopenSubscriptionDetails!==null&&o.__internal_openSubscriptionDetails(this.preopenSubscriptionDetails),this.preopenSignUp!==null&&o.openSignUp(this.preopenSignUp),this.preopenUserProfile!==null&&o.openUserProfile(this.preopenUserProfile),this.preopenUserVerification!==null&&o.__internal_openReverification(this.preopenUserVerification),this.preopenOneTap!==null&&o.openGoogleOneTap(this.preopenOneTap),this.preopenOrganizationProfile!==null&&o.openOrganizationProfile(this.preopenOrganizationProfile),this.preopenCreateOrganization!==null&&o.openCreateOrganization(this.preopenCreateOrganization),this.preOpenWaitlist!==null&&o.openWaitlist(this.preOpenWaitlist),this.preopenEnableOrganizationsPrompt&&o.__internal_openEnableOrganizationsPrompt(this.preopenEnableOrganizationsPrompt),this.premountSignInNodes.forEach((u,s)=>{o.mountSignIn(s,u)}),this.premountSignUpNodes.forEach((u,s)=>{o.mountSignUp(s,u)}),this.premountUserProfileNodes.forEach((u,s)=>{o.mountUserProfile(s,u)}),this.premountUserAvatarNodes.forEach((u,s)=>{o.mountUserAvatar(s,u)}),this.premountUserButtonNodes.forEach((u,s)=>{o.mountUserButton(s,u)}),this.premountOrganizationListNodes.forEach((u,s)=>{o.mountOrganizationList(s,u)}),this.premountWaitlistNodes.forEach((u,s)=>{o.mountWaitlist(s,u)}),this.premountPricingTableNodes.forEach((u,s)=>{o.mountPricingTable(s,u)}),this.premountAPIKeysNodes.forEach((u,s)=>{o.mountAPIKeys(s,u)}),this.premountOAuthConsentNodes.forEach((u,s)=>{o.__internal_mountOAuthConsent(s,u)}),this.premountTaskChooseOrganizationNodes.forEach((u,s)=>{o.mountTaskChooseOrganization(s,u)}),this.premountTaskResetPasswordNodes.forEach((u,s)=>{o.mountTaskResetPassword(s,u)}),this.premountTaskSetupMFANodes.forEach((u,s)=>{o.mountTaskSetupMFA(s,u)}),typeof this.clerkjs.status>"u"&&Dt(this,fr).emit(qi.Status,"ready"),this.emitLoaded(),this.clerkjs},this.__experimental_checkout=(...o)=>{var a;return(a=this.clerkjs)==null?void 0:a.__experimental_checkout(...o)},this.__unstable__updateProps=async o=>{const a=await Is(this,Ya,bl).call(this);if(a&&"__unstable__updateProps"in a)return a.__unstable__updateProps(o)},this.setActive=o=>this.clerkjs?this.clerkjs.setActive(o):Promise.reject(),this.openSignIn=o=>{this.clerkjs&&this.loaded?this.clerkjs.openSignIn(o):this.preopenSignIn=o},this.closeSignIn=()=>{this.clerkjs&&this.loaded?this.clerkjs.closeSignIn():this.preopenSignIn=null},this.__internal_openCheckout=o=>{this.clerkjs&&this.loaded?this.clerkjs.__internal_openCheckout(o):this.preopenCheckout=o},this.__internal_closeCheckout=()=>{this.clerkjs&&this.loaded?this.clerkjs.__internal_closeCheckout():this.preopenCheckout=null},this.__internal_openPlanDetails=o=>{this.clerkjs&&this.loaded?this.clerkjs.__internal_openPlanDetails(o):this.preopenPlanDetails=o},this.__internal_closePlanDetails=()=>{this.clerkjs&&this.loaded?this.clerkjs.__internal_closePlanDetails():this.preopenPlanDetails=null},this.__internal_openSubscriptionDetails=o=>{this.clerkjs&&this.loaded?this.clerkjs.__internal_openSubscriptionDetails(o):this.preopenSubscriptionDetails=o??null},this.__internal_closeSubscriptionDetails=()=>{this.clerkjs&&this.loaded?this.clerkjs.__internal_closeSubscriptionDetails():this.preopenSubscriptionDetails=null},this.__internal_openReverification=o=>{this.clerkjs&&this.loaded?this.clerkjs.__internal_openReverification(o):this.preopenUserVerification=o},this.__internal_closeReverification=()=>{this.clerkjs&&this.loaded?this.clerkjs.__internal_closeReverification():this.preopenUserVerification=null},this.__internal_openEnableOrganizationsPrompt=o=>{this.clerkjs&&this.loaded?this.clerkjs.__internal_openEnableOrganizationsPrompt(o):this.preopenEnableOrganizationsPrompt=o},this.__internal_closeEnableOrganizationsPrompt=()=>{this.clerkjs&&this.loaded?this.clerkjs.__internal_closeEnableOrganizationsPrompt():this.preopenEnableOrganizationsPrompt=null},this.openGoogleOneTap=o=>{this.clerkjs&&this.loaded?this.clerkjs.openGoogleOneTap(o):this.preopenOneTap=o},this.closeGoogleOneTap=()=>{this.clerkjs&&this.loaded?this.clerkjs.closeGoogleOneTap():this.preopenOneTap=null},this.openUserProfile=o=>{this.clerkjs&&this.loaded?this.clerkjs.openUserProfile(o):this.preopenUserProfile=o},this.closeUserProfile=()=>{this.clerkjs&&this.loaded?this.clerkjs.closeUserProfile():this.preopenUserProfile=null},this.openOrganizationProfile=o=>{this.clerkjs&&this.loaded?this.clerkjs.openOrganizationProfile(o):this.preopenOrganizationProfile=o},this.closeOrganizationProfile=()=>{this.clerkjs&&this.loaded?this.clerkjs.closeOrganizationProfile():this.preopenOrganizationProfile=null},this.openCreateOrganization=o=>{this.clerkjs&&this.loaded?this.clerkjs.openCreateOrganization(o):this.preopenCreateOrganization=o},this.closeCreateOrganization=()=>{this.clerkjs&&this.loaded?this.clerkjs.closeCreateOrganization():this.preopenCreateOrganization=null},this.openWaitlist=o=>{this.clerkjs&&this.loaded?this.clerkjs.openWaitlist(o):this.preOpenWaitlist=o},this.closeWaitlist=()=>{this.clerkjs&&this.loaded?this.clerkjs.closeWaitlist():this.preOpenWaitlist=null},this.openSignUp=o=>{this.clerkjs&&this.loaded?this.clerkjs.openSignUp(o):this.preopenSignUp=o},this.closeSignUp=()=>{this.clerkjs&&this.loaded?this.clerkjs.closeSignUp():this.preopenSignUp=null},this.mountSignIn=(o,a)=>{this.clerkjs&&this.loaded?this.clerkjs.mountSignIn(o,a):this.premountSignInNodes.set(o,a)},this.unmountSignIn=o=>{this.clerkjs&&this.loaded?this.clerkjs.unmountSignIn(o):this.premountSignInNodes.delete(o)},this.mountSignUp=(o,a)=>{this.clerkjs&&this.loaded?this.clerkjs.mountSignUp(o,a):this.premountSignUpNodes.set(o,a)},this.unmountSignUp=o=>{this.clerkjs&&this.loaded?this.clerkjs.unmountSignUp(o):this.premountSignUpNodes.delete(o)},this.mountUserAvatar=(o,a)=>{this.clerkjs&&this.loaded?this.clerkjs.mountUserAvatar(o,a):this.premountUserAvatarNodes.set(o,a)},this.unmountUserAvatar=o=>{this.clerkjs&&this.loaded?this.clerkjs.unmountUserAvatar(o):this.premountUserAvatarNodes.delete(o)},this.mountUserProfile=(o,a)=>{this.clerkjs&&this.loaded?this.clerkjs.mountUserProfile(o,a):this.premountUserProfileNodes.set(o,a)},this.unmountUserProfile=o=>{this.clerkjs&&this.loaded?this.clerkjs.unmountUserProfile(o):this.premountUserProfileNodes.delete(o)},this.mountOrganizationProfile=(o,a)=>{this.clerkjs&&this.loaded?this.clerkjs.mountOrganizationProfile(o,a):this.premountOrganizationProfileNodes.set(o,a)},this.unmountOrganizationProfile=o=>{this.clerkjs&&this.loaded?this.clerkjs.unmountOrganizationProfile(o):this.premountOrganizationProfileNodes.delete(o)},this.mountCreateOrganization=(o,a)=>{this.clerkjs&&this.loaded?this.clerkjs.mountCreateOrganization(o,a):this.premountCreateOrganizationNodes.set(o,a)},this.unmountCreateOrganization=o=>{this.clerkjs&&this.loaded?this.clerkjs.unmountCreateOrganization(o):this.premountCreateOrganizationNodes.delete(o)},this.mountOrganizationSwitcher=(o,a)=>{this.clerkjs&&this.loaded?this.clerkjs.mountOrganizationSwitcher(o,a):this.premountOrganizationSwitcherNodes.set(o,a)},this.unmountOrganizationSwitcher=o=>{this.clerkjs&&this.loaded?this.clerkjs.unmountOrganizationSwitcher(o):this.premountOrganizationSwitcherNodes.delete(o)},this.__experimental_prefetchOrganizationSwitcher=()=>{const o=()=>{var a;return(a=this.clerkjs)==null?void 0:a.__experimental_prefetchOrganizationSwitcher()};this.clerkjs&&this.loaded?o():this.premountMethodCalls.set("__experimental_prefetchOrganizationSwitcher",o)},this.mountOrganizationList=(o,a)=>{this.clerkjs&&this.loaded?this.clerkjs.mountOrganizationList(o,a):this.premountOrganizationListNodes.set(o,a)},this.unmountOrganizationList=o=>{this.clerkjs&&this.loaded?this.clerkjs.unmountOrganizationList(o):this.premountOrganizationListNodes.delete(o)},this.mountUserButton=(o,a)=>{this.clerkjs&&this.loaded?this.clerkjs.mountUserButton(o,a):this.premountUserButtonNodes.set(o,a)},this.unmountUserButton=o=>{this.clerkjs&&this.loaded?this.clerkjs.unmountUserButton(o):this.premountUserButtonNodes.delete(o)},this.mountWaitlist=(o,a)=>{this.clerkjs&&this.loaded?this.clerkjs.mountWaitlist(o,a):this.premountWaitlistNodes.set(o,a)},this.unmountWaitlist=o=>{this.clerkjs&&this.loaded?this.clerkjs.unmountWaitlist(o):this.premountWaitlistNodes.delete(o)},this.mountPricingTable=(o,a)=>{this.clerkjs&&this.loaded?this.clerkjs.mountPricingTable(o,a):this.premountPricingTableNodes.set(o,a)},this.unmountPricingTable=o=>{this.clerkjs&&this.loaded?this.clerkjs.unmountPricingTable(o):this.premountPricingTableNodes.delete(o)},this.mountAPIKeys=(o,a)=>{this.clerkjs&&this.loaded?this.clerkjs.mountAPIKeys(o,a):this.premountAPIKeysNodes.set(o,a)},this.unmountAPIKeys=o=>{this.clerkjs&&this.loaded?this.clerkjs.unmountAPIKeys(o):this.premountAPIKeysNodes.delete(o)},this.__internal_mountOAuthConsent=(o,a)=>{this.clerkjs&&this.loaded?this.clerkjs.__internal_mountOAuthConsent(o,a):this.premountOAuthConsentNodes.set(o,a)},this.__internal_unmountOAuthConsent=o=>{this.clerkjs&&this.loaded?this.clerkjs.__internal_unmountOAuthConsent(o):this.premountOAuthConsentNodes.delete(o)},this.mountTaskChooseOrganization=(o,a)=>{this.clerkjs&&this.loaded?this.clerkjs.mountTaskChooseOrganization(o,a):this.premountTaskChooseOrganizationNodes.set(o,a)},this.unmountTaskChooseOrganization=o=>{this.clerkjs&&this.loaded?this.clerkjs.unmountTaskChooseOrganization(o):this.premountTaskChooseOrganizationNodes.delete(o)},this.mountTaskResetPassword=(o,a)=>{this.clerkjs&&this.loaded?this.clerkjs.mountTaskResetPassword(o,a):this.premountTaskResetPasswordNodes.set(o,a)},this.unmountTaskResetPassword=o=>{this.clerkjs&&this.loaded?this.clerkjs.unmountTaskResetPassword(o):this.premountTaskResetPasswordNodes.delete(o)},this.mountTaskSetupMFA=(o,a)=>{this.clerkjs&&this.loaded?this.clerkjs.mountTaskSetupMFA(o,a):this.premountTaskSetupMFANodes.set(o,a)},this.unmountTaskSetupMFA=o=>{this.clerkjs&&this.loaded?this.clerkjs.unmountTaskSetupMFA(o):this.premountTaskSetupMFANodes.delete(o)},this.addListener=o=>{if(this.clerkjs)return this.clerkjs.addListener(o);{const a=()=>{var l;const u=this.premountAddListenerCalls.get(o);u&&((l=u.nativeUnsubscribe)==null||l.call(u),this.premountAddListenerCalls.delete(o))};return this.premountAddListenerCalls.set(o,{unsubscribe:a,nativeUnsubscribe:void 0}),a}},this.navigate=o=>{const a=()=>{var l;return(l=this.clerkjs)==null?void 0:l.navigate(o)};this.clerkjs&&this.loaded?a():this.premountMethodCalls.set("navigate",a)},this.redirectWithAuth=async(...o)=>{const a=()=>{var l;return(l=this.clerkjs)==null?void 0:l.redirectWithAuth(...o)};if(this.clerkjs&&this.loaded)return a();this.premountMethodCalls.set("redirectWithAuth",a)},this.redirectToSignIn=async o=>{const a=()=>{var l;return(l=this.clerkjs)==null?void 0:l.redirectToSignIn(o)};if(this.clerkjs&&this.loaded)return a();this.premountMethodCalls.set("redirectToSignIn",a)},this.redirectToSignUp=async o=>{const a=()=>{var l;return(l=this.clerkjs)==null?void 0:l.redirectToSignUp(o)};if(this.clerkjs&&this.loaded)return a();this.premountMethodCalls.set("redirectToSignUp",a)},this.redirectToUserProfile=async()=>{const o=()=>{var a;return(a=this.clerkjs)==null?void 0:a.redirectToUserProfile()};if(this.clerkjs&&this.loaded)return o();this.premountMethodCalls.set("redirectToUserProfile",o)},this.redirectToAfterSignUp=()=>{const o=()=>{var a;return(a=this.clerkjs)==null?void 0:a.redirectToAfterSignUp()};if(this.clerkjs&&this.loaded)return o();this.premountMethodCalls.set("redirectToAfterSignUp",o)},this.redirectToAfterSignIn=()=>{const o=()=>{var a;return(a=this.clerkjs)==null?void 0:a.redirectToAfterSignIn()};this.clerkjs&&this.loaded?o():this.premountMethodCalls.set("redirectToAfterSignIn",o)},this.redirectToAfterSignOut=()=>{const o=()=>{var a;return(a=this.clerkjs)==null?void 0:a.redirectToAfterSignOut()};this.clerkjs&&this.loaded?o():this.premountMethodCalls.set("redirectToAfterSignOut",o)},this.redirectToOrganizationProfile=async()=>{const o=()=>{var a;return(a=this.clerkjs)==null?void 0:a.redirectToOrganizationProfile()};if(this.clerkjs&&this.loaded)return o();this.premountMethodCalls.set("redirectToOrganizationProfile",o)},this.redirectToCreateOrganization=async()=>{const o=()=>{var a;return(a=this.clerkjs)==null?void 0:a.redirectToCreateOrganization()};if(this.clerkjs&&this.loaded)return o();this.premountMethodCalls.set("redirectToCreateOrganization",o)},this.redirectToWaitlist=async()=>{const o=()=>{var a;return(a=this.clerkjs)==null?void 0:a.redirectToWaitlist()};if(this.clerkjs&&this.loaded)return o();this.premountMethodCalls.set("redirectToWaitlist",o)},this.redirectToTasks=async o=>{const a=()=>{var l;return(l=this.clerkjs)==null?void 0:l.redirectToTasks(o)};if(this.clerkjs&&this.loaded)return a();this.premountMethodCalls.set("redirectToTasks",a)},this.handleRedirectCallback=async o=>{var a;const l=()=>{var u;return(u=this.clerkjs)==null?void 0:u.handleRedirectCallback(o)};this.clerkjs&&this.loaded?(a=l())==null||a.catch(()=>{}):this.premountMethodCalls.set("handleRedirectCallback",l)},this.handleGoogleOneTapCallback=async(o,a)=>{var l;const u=()=>{var s;return(s=this.clerkjs)==null?void 0:s.handleGoogleOneTapCallback(o,a)};this.clerkjs&&this.loaded?(l=u())==null||l.catch(()=>{}):this.premountMethodCalls.set("handleGoogleOneTapCallback",u)},this.handleEmailLinkVerification=async o=>{const a=()=>{var l;return(l=this.clerkjs)==null?void 0:l.handleEmailLinkVerification(o)};if(this.clerkjs&&this.loaded)return a();this.premountMethodCalls.set("handleEmailLinkVerification",a)},this.authenticateWithMetamask=async o=>{const a=()=>{var l;return(l=this.clerkjs)==null?void 0:l.authenticateWithMetamask(o)};if(this.clerkjs&&this.loaded)return a();this.premountMethodCalls.set("authenticateWithMetamask",a)},this.authenticateWithCoinbaseWallet=async o=>{const a=()=>{var l;return(l=this.clerkjs)==null?void 0:l.authenticateWithCoinbaseWallet(o)};if(this.clerkjs&&this.loaded)return a();this.premountMethodCalls.set("authenticateWithCoinbaseWallet",a)},this.authenticateWithBase=async o=>{const a=()=>{var l;return(l=this.clerkjs)==null?void 0:l.authenticateWithBase(o)};if(this.clerkjs&&this.loaded)return a();this.premountMethodCalls.set("authenticateWithBase",a)},this.authenticateWithOKXWallet=async o=>{const a=()=>{var l;return(l=this.clerkjs)==null?void 0:l.authenticateWithOKXWallet(o)};if(this.clerkjs&&this.loaded)return a();this.premountMethodCalls.set("authenticateWithOKXWallet",a)},this.authenticateWithSolana=async o=>{const a=()=>{var l;return(l=this.clerkjs)==null?void 0:l.authenticateWithSolana(o)};if(this.clerkjs&&this.loaded)return a();this.premountMethodCalls.set("authenticateWithSolana",a)},this.authenticateWithWeb3=async o=>{const a=()=>{var l;return(l=this.clerkjs)==null?void 0:l.authenticateWithWeb3(o)};if(this.clerkjs&&this.loaded)return a();this.premountMethodCalls.set("authenticateWithWeb3",a)},this.authenticateWithGoogleOneTap=async o=>(await Is(this,Ya,bl).call(this)).authenticateWithGoogleOneTap(o),this.__internal_loadStripeJs=async()=>(await Is(this,Ya,bl).call(this)).__internal_loadStripeJs(),this.createOrganization=async o=>{const a=()=>{var l;return(l=this.clerkjs)==null?void 0:l.createOrganization(o)};if(this.clerkjs&&this.loaded)return a();this.premountMethodCalls.set("createOrganization",a)},this.getOrganization=async o=>{const a=()=>{var l;return(l=this.clerkjs)==null?void 0:l.getOrganization(o)};if(this.clerkjs&&this.loaded)return a();this.premountMethodCalls.set("getOrganization",a)},this.joinWaitlist=async o=>{const a=()=>{var l;return(l=this.clerkjs)==null?void 0:l.joinWaitlist(o)};if(this.clerkjs&&this.loaded)return a();this.premountMethodCalls.set("joinWaitlist",a)},this.signOut=async(...o)=>{const a=()=>{var l;return(l=this.clerkjs)==null?void 0:l.signOut(...o)};if(this.clerkjs&&this.loaded)return a();this.premountMethodCalls.set("signOut",a)},this.__internal_attemptToEnableEnvironmentSetting=o=>{const a=()=>{var l;return(l=this.clerkjs)==null?void 0:l.__internal_attemptToEnableEnvironmentSetting(o)};if(this.clerkjs&&this.loaded)return a();this.premountMethodCalls.set("__internal_attemptToEnableEnvironmentSetting",a)};const{Clerk:n=null,publishableKey:r}=t||{};mo(this,Ur,r),mo(this,Uo,t?.proxyUrl),mo(this,Wo,t?.domain),this.options=t,this.Clerk=n,this.mode=Vl()?"browser":"server",mo(this,Cl,new iI(this)),this.options.sdkMetadata||(this.options.sdkMetadata=lI),Dt(this,fr).emit(qi.Status,"loading"),Dt(this,fr).prioritizedOn(qi.Status,o=>mo(this,vl,o)),Dt(this,Ur)&&this.loadClerkJS()}get publishableKey(){return Dt(this,Ur)}get loaded(){var t;return((t=this.clerkjs)==null?void 0:t.loaded)||!1}get status(){var t;return this.clerkjs?((t=this.clerkjs)==null?void 0:t.status)||(this.clerkjs.loaded?"ready":"loading"):Dt(this,vl)}static getOrCreateInstance(t){return(!Vl()||!Dt(this,qr)||t.Clerk&&Dt(this,qr).Clerk!==t.Clerk||Dt(this,qr).publishableKey!==t.publishableKey)&&mo(this,qr,new Mp(t)),Dt(this,qr)}static clearInstance(){mo(this,qr,null)}get domain(){return typeof window<"u"&&window.location?ws(Dt(this,Wo),new URL(window.location.href),""):typeof Dt(this,Wo)=="function"?vr.throw(Es):Dt(this,Wo)||""}get proxyUrl(){return typeof window<"u"&&window.location?ws(Dt(this,Uo),new URL(window.location.href),""):typeof Dt(this,Uo)=="function"?vr.throw(Es):Dt(this,Uo)||""}__internal_getOption(t){var n,r;return(n=this.clerkjs)!=null&&n.__internal_getOption?(r=this.clerkjs)==null?void 0:r.__internal_getOption(t):this.options[t]}get sdkMetadata(){var t;return((t=this.clerkjs)==null?void 0:t.sdkMetadata)||this.options.sdkMetadata||void 0}get instanceType(){var t;return(t=this.clerkjs)==null?void 0:t.instanceType}get frontendApi(){var t;return((t=this.clerkjs)==null?void 0:t.frontendApi)||""}get isStandardBrowser(){var t;return((t=this.clerkjs)==null?void 0:t.isStandardBrowser)||this.options.standardBrowser||!1}get __internal_queryClient(){var t;return(t=this.clerkjs)==null?void 0:t.__internal_queryClient}get isSatellite(){return typeof window<"u"&&window.location?ws(this.options.isSatellite,new URL(window.location.href),!1):typeof this.options.isSatellite=="function"?vr.throw(Es):!1}async loadClerkJS(){var t;if(!(this.mode!=="browser"||this.loaded)){typeof window<"u"&&(window.__clerk_publishable_key=Dt(this,Ur),window.__clerk_proxy_url=this.proxyUrl,window.__clerk_domain=this.domain);try{if(this.Clerk){let n;CM(this.Clerk)?(n=new this.Clerk(Dt(this,Ur),{proxyUrl:this.proxyUrl,domain:this.domain}),this.beforeLoad(n),await n.load(this.options)):(n=this.Clerk,n.loaded||(this.beforeLoad(n),await n.load(this.options))),global.Clerk=n}else if(!__BUILD_DISABLE_RHC__){if(global.Clerk||await KM({...this.options,publishableKey:Dt(this,Ur),proxyUrl:this.proxyUrl,domain:this.domain,nonce:this.options.nonce}),!global.Clerk)throw new Error("Failed to download latest ClerkJS. Contact support@clerk.com.");this.beforeLoad(global.Clerk),await global.Clerk.load(this.options)}return(t=global.Clerk)!=null&&t.loaded?this.hydrateClerkJS(global.Clerk):void 0}catch(n){const r=n;Dt(this,fr).emit(qi.Status,"error"),console.error(r.stack||r.message||r);return}}}get version(){var t;return(t=this.clerkjs)==null?void 0:t.version}get client(){if(this.clerkjs)return this.clerkjs.client}get session(){if(this.clerkjs)return this.clerkjs.session}get user(){if(this.clerkjs)return this.clerkjs.user}get organization(){if(this.clerkjs)return this.clerkjs.organization}get telemetry(){if(this.clerkjs)return this.clerkjs.telemetry}get __unstable__environment(){if(this.clerkjs)return this.clerkjs.__unstable__environment}get isSignedIn(){return this.clerkjs?this.clerkjs.isSignedIn:!1}get billing(){var t;return(t=this.clerkjs)==null?void 0:t.billing}get __internal_state(){return this.loaded&&this.clerkjs?this.clerkjs.__internal_state:Dt(this,Cl)}get apiKeys(){var 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Daha akıllı, daha hızlı inşa edin.",pl:"Uzyskaj dostęp do każdego głównego LLM przez jeden, zintegrowany interfejs. Połącz się z ChatGPT, Claude, Gemini, DeepSeek, Doubao, Qwen i innymi z nieograniczoną równoległością. Buduj mądrzej, szybciej.",nl:"Toegang tot elke belangrijke LLM via één enkele, uniforme interface. Verbind met ChatGPT, Claude, Gemini, DeepSeek, Doubao, Qwen en meer met onbeperkte gelijktijdigheid. Bouw slimmer, sneller.",it:"Accedi a ogni principale LLM attraverso un'unica interfaccia unificata. Connettiti a ChatGPT, Claude, Gemini, DeepSeek, Doubao, Qwen e altro con concorrenza illimitata. Costruisci in modo più intelligente e veloce.",vi:"Truy cập mọi LLM lớn qua một giao diện thống nhất. Kết nối với ChatGPT, Claude, Gemini, DeepSeek, Doubao, Qwen và nhiều hơn nữa với khả năng đồng thời không giới hạn. Xây dựng thông minh hơn, nhanh hơn.",uk:"Отримайте доступ до всіх основних LLM через єдиний, уніфікований інтерфейс. Підключайтеся до ChatGPT, Claude, Gemini, DeepSeek, Doubao, Qwen та інших з необмеженою одночасністю. Створюйте розумніше, швидше.",bg:"Достъп до всеки основен LLM чрез единен интерфейс. Свържете се с ChatGPT, Claude, Gemini, DeepSeek, Doubao, Qwen и още с неограничена паралелност. Създавайте по-умно и по-бързо."},keywords:{en:"LLM API router, OpenAI API gateway, openrouter, ChatGPT API, Claude API, Gemini API,Doubao, DeepSeek API, Qwen API, AI model aggregation, unlimited concurrency, enterprise AI solution","zh-CN":"LLM API router, OpenAI API中转, openrouter, Google Gemini, gemini、DeepSeek、Llama、Doubao、阿里Qwen、Claude、月之暗面、cohere等模型,Claude, 阿里Qwen, LLama, 无限并发, OpenAI API代理","zh-TW":"LLM API 路由器, OpenAI API 閘道, openrouter, ChatGPT API, Claude API, Gemini API, Doubao, DeepSeek API, Qwen API, AI 模型聚合, 無限制並發, 企業 AI 解決方案",ja:"LLM API ルーター, OpenAI API ゲートウェイ, openrouter, ChatGPT API, Claude API, Gemini API, Doubao, DeepSeek API, Qwen API, AIモデル集約, 無制限の同時接続, エンタープライズAIソリューション",ko:"LLM API 라우터, OpenAI API 게이트웨이, openrouter, ChatGPT API, Claude API, Gemini API, Doubao, DeepSeek API, Qwen API, AI 모델 집계, 무제한 동시성, 기업 AI 솔루션",de:"LLM API Router, OpenAI API Gateway, openrouter, ChatGPT API, Claude API, Gemini API, Doubao, DeepSeek API, Qwen API, Aggregation von KI-Modellen, unbegrenzte gleichzeitige Nutzung, Unternehmens-KI-Lösung",es:"router API LLM, puerta de enlace API OpenAI, openrouter, API ChatGPT, API Claude, API Gemini, Doubao, API DeepSeek, API Qwen, agregación de modelos de IA, concurrencia ilimitada, solución de IA empresarial",fr:"routeur API LLM, passerelle API OpenAI, openrouter, API ChatGPT, API Claude, API Gemini, Doubao, API DeepSeek, API Qwen, agrégation de modèles AI, concurrence illimitée, solution AI pour entreprises",pt:"roteador de API LLM, gateway de API OpenAI, openrouter, API ChatGPT, API Claude, API Gemini, Doubao, API DeepSeek, API Qwen, agregação de modelos de IA, concorrência ilimitada, solução de IA para empresas",ru:"маршрутизатор LLM API, шлюз OpenAI API, openrouter, ChatGPT API, Claude API, Gemini API, Doubao, DeepSeek API, Qwen API, агрегация AI моделей, неограниченная параллельность, корпоративное AI решение",tr:"LLM API yönlendirici, OpenAI API geçidi, openrouter, ChatGPT API, Claude API, Gemini API, Doubao, DeepSeek API, Qwen API, AI model toplama, sınırsız eşzamanlılık, kurumsal AI çözümü",pl:"router API LLM, brama API OpenAI, openrouter, API ChatGPT, API Claude, API Gemini, Doubao, API DeepSeek, API Qwen, agregacja modeli AI, nieograniczona równoległość, rozwiązanie AI dla przedsiębiorstw",nl:"LLM API router, OpenAI API gateway, openrouter, ChatGPT API, Claude API, Gemini API, Doubao, DeepSeek API, Qwen API, AI model aggregatie, onbeperkte gelijktijdigheid, enterprise AI-oplossing",it:"router API LLM, gateway API OpenAI, openrouter, API ChatGPT, API Claude, API Gemini, Doubao, API DeepSeek, API Qwen, aggregazione modelli AI, concorrenza illimitata, soluzione AI per imprese",vi:"router API LLM, cổng API OpenAI, openrouter, API ChatGPT, API Claude, API Gemini, Doubao, API DeepSeek, API Qwen, tổng hợp mô hình AI, khả năng đồng thời không giới hạn, giải pháp AI doanh nghiệp",uk:"маршрутизатор LLM API, шлюз OpenAI API, openrouter, ChatGPT API, Claude API, Gemini API, Doubao, DeepSeek API, Qwen API, агрегація AI моделей, необмежена одночасність, корпоративне AI рішення",bg:"LLM API маршрутизатор, OpenAI API шлюз, openrouter, ChatGPT API, Claude API, Gemini API, Doubao, DeepSeek API, Qwen API, агрегация на AI модели, неограничена паралелност, корпоративно AI решение"},canonical:{en:"https://aihubmix.com/","zh-CN":"https://aihubmix.com/?lang=zh-CN","zh-TW":"https://aihubmix.com/?lang=zh-TW",ja:"https://aihubmix.com/?lang=ja",ko:"https://aihubmix.com/?lang=ko",de:"https://aihubmix.com/?lang=de",es:"https://aihubmix.com/?lang=es",fr:"https://aihubmix.com/?lang=fr",pt:"https://aihubmix.com/?lang=pt",ru:"https://aihubmix.com/?lang=ru",tr:"https://aihubmix.com/?lang=tr",pl:"https://aihubmix.com/?lang=pl",nl:"https://aihubmix.com/?lang=nl",it:"https://aihubmix.com/?lang=it",vi:"https://aihubmix.com/?lang=vi",uk:"https://aihubmix.com/?lang=uk",bg:"https://aihubmix.com/?lang=bg"}},models:{path:"/models",ogType:"website",title:{en:"AI Models & Pricing - AIHubMix","zh-CN":"模型价格 - AIHubMix","zh-TW":"AI 模型與定價 - AIHubMix",ja:"AIモデルと価格 - AIHubMix",ko:"AI 모델 및 가격 - AIHubMix",de:"KI-Modelle & Preise - AIHubMix",es:"Modelos de IA y Precios - AIHubMix",fr:"Modèles AI & Tarification - AIHubMix",pt:"Modelos de IA & Preços - AIHubMix",ru:"AI Модели и Цены - AIHubMix",tr:"AI Modelleri & Fiyatlandırma - AIHubMix",pl:"Modele AI i Cennik - AIHubMix",nl:"AI Modellen & Prijzen - AIHubMix",it:"Modelli AI & Prezzi - AIHubMix",vi:"Mô Hình AI & Giá Cả - AIHubMix",uk:"AI Моделі та Ціни - AIHubMix",bg:"AI Модели и Цени - AIHubMix"},description:{en:"Explore our comprehensive AI model marketplace with transparent pricing. Access ChatGPT, Claude, Gemini, DeepSeek, Doubao, Qwen and more with competitive rates and unlimited concurrency.","zh-CN":"浏览我们全面的AI模型市场，透明定价。访问ChatGPT、Claude、Gemini、DeepSeek、Doubao、Qwen等模型，享受竞争性价格和无限并发。","zh-TW":"探索我們全面的 AI 模型市場，透明的定價。以競爭價格和無限制並發訪問 ChatGPT、Claude、Gemini、DeepSeek、Doubao、Qwen 等。",ja:"透明な価格設定の包括的なAIモデルマーケットプレイスを探索。競争力のある料金と無制限の同時接続で、ChatGPT、Claude、Gemini、DeepSeek、Doubao、Qwenなどにアクセス。",ko:"투명한 가격으로 포괄적인 AI 모델 마켓플레이스를 탐색하세요. 경쟁력 있는 요금과 무제한 동시성으로 ChatGPT, Claude, Gemini, DeepSeek, Doubao, Qwen 등에 접근하세요.",de:"Entdecken Sie unseren umfassenden Marktplatz für KI-Modelle mit transparenten Preisen. Greifen Sie auf ChatGPT, Claude, Gemini, DeepSeek, Doubao, Qwen und mehr zu, zu wettbewerbsfähigen Preisen und unbegrenzter gleichzeitiger Nutzung.",es:"Explora nuestro completo mercado de modelos de IA con precios transparentes. Accede a ChatGPT, Claude, Gemini, DeepSeek, Doubao, Qwen y más con tarifas competitivas y concurrencia ilimitada.",fr:"Explorez notre marché complet de modèles AI avec des prix transparents. Accédez à ChatGPT, Claude, Gemini, DeepSeek, Doubao, Qwen et plus avec des tarifs compétitifs et une concurrence illimitée.",pt:"Explore nosso abrangente marketplace de modelos de IA com preços transparentes. Acesse ChatGPT, Claude, Gemini, DeepSeek, Doubao, Qwen e mais com tarifas competitivas e concorrência ilimitada.",ru:"Изучите наш обширный рынок AI моделей с прозрачным ценообразованием. Получите доступ к ChatGPT, Claude, Gemini, DeepSeek, Doubao, Qwen и другим по конкурентным ценам и с неограниченной параллельностью.",tr:"Şeffaf fiyatlandırma ile kapsamlı AI model pazarımızı keşfedin. ChatGPT, Claude, Gemini, DeepSeek, Doubao, Qwen ve daha fazlasına rekabetçi fiyatlarla ve sınırsız eşzamanlılıkla erişin.",pl:"Odkryj nasz kompleksowy rynek modeli AI z przejrzystym cennikiem. Uzyskaj dostęp do ChatGPT, Claude, Gemini, DeepSeek, Doubao, Qwen i innych z konkurencyjnymi stawkami i nieograniczoną równoległością.",nl:"Verken onze uitgebreide AI-modelmarktplaats met transparante prijzen. Toegang tot ChatGPT, Claude, Gemini, DeepSeek, Doubao, Qwen en meer met concurrerende tarieven en onbeperkte gelijktijdigheid.",it:"Esplora il nostro marketplace di modelli AI con prezzi trasparenti. Accedi a ChatGPT, Claude, Gemini, DeepSeek, Doubao, Qwen e altro con tariffe competitive e concorrenza illimitata.",vi:"Khám phá thị trường mô hình AI toàn diện của chúng tôi với giá cả minh bạch. Truy cập ChatGPT, Claude, Gemini, DeepSeek, Doubao, Qwen và nhiều hơn nữa với mức giá cạnh tranh và khả năng đồng thời không giới hạn.",uk:"Досліджуйте наш комплексний ринок AI моделей з прозорим ціноутворенням. Отримуйте доступ до ChatGPT, Claude, Gemini, DeepSeek, Doubao, Qwen та інших за конкурентними цінами та з необмеженою одночасністю.",bg:"Изследвайте нашия обширен пазар на AI модели с прозрачни цени. Достъп до ChatGPT, Claude, Gemini, DeepSeek, Doubao, Qwen и още с конкурентни ставки и неограничена паралелност."},keywords:{en:"AI model pricing, ChatGPT pricing, Claude pricing, Gemini pricing, DeepSeek pricing, Doubao pricing, Qwen pricing, AI model comparison, LLM pricing calculator, AI API costs","zh-CN":"AI模型价格, ChatGPT价格, Claude价格, Gemini价格, DeepSeek价格, Doubao价格, Qwen价格, AI模型比较, LLM价格计算器, AI API成本","zh-TW":"AI 模型定價, ChatGPT 定價, Claude 定價, Gemini 定價, DeepSeek 定價, Doubao 定價, Qwen 定價, AI 模型比較, LLM 定價計算器, AI API 成本",ja:"AIモデル価格, ChatGPT価格, Claude価格, Gemini価格, DeepSeek価格, Doubao価格, Qwen価格, AIモデル比較, LLM価格計算機, AI APIコスト",ko:"AI 모델 가격, ChatGPT 가격, Claude 가격, Gemini 가격, DeepSeek 가격, Doubao 가격, Qwen 가격, AI 모델 비교, LLM 가격 계산기, AI API 비용",de:"KI-Modellpreise, ChatGPT Preise, Claude Preise, Gemini Preise, DeepSeek Preise, Doubao Preise, Qwen Preise, KI-Modellvergleich, LLM Preisrechner, KI API Kosten",es:"precios de modelos de IA, precios de ChatGPT, precios de Claude, precios de Gemini, precios de DeepSeek, precios de Doubao, precios de Qwen, comparación de modelos de IA, calculadora de precios LLM, costos de API de IA",fr:"tarification des modèles AI, tarification ChatGPT, tarification Claude, tarification Gemini, tarification DeepSeek, tarification Doubao, tarification Qwen, comparaison de modèles AI, calculateur de prix LLM, coûts API AI",pt:"preços de modelos de IA, preços ChatGPT, preços Claude, preços Gemini, preços DeepSeek, preços Doubao, preços Qwen, comparação de modelos de IA, calculadora de preços de LLM, custos de API de IA",ru:"цены на AI модели, цены на ChatGPT, цены на Claude, цены на Gemini, цены на DeepSeek, цены на Doubao, цены на Qwen, сравнение AI моделей, калькулятор цен LLM, стоимость AI API",tr:"AI model fiyatlandırması, ChatGPT fiyatlandırması, Claude fiyatlandırması, Gemini fiyatlandırması, DeepSeek fiyatlandırması, Doubao fiyatlandırması, Qwen fiyatlandırması, AI model karşılaştırması, LLM fiyatlandırma hesaplayıcı, AI API maliyetleri",pl:"cennik modeli AI, cennik ChatGPT, cennik Claude, cennik Gemini, cennik DeepSeek, cennik Doubao, cennik Qwen, porównanie modeli AI, kalkulator cen LLM, koszty API AI",nl:"AI model prijzen, ChatGPT prijzen, Claude prijzen, Gemini prijzen, DeepSeek prijzen, Doubao prijzen, Qwen prijzen, AI model vergelijking, LLM prijs calculator, AI API kosten",it:"prezzi modelli AI, prezzi ChatGPT, prezzi Claude, prezzi Gemini, prezzi DeepSeek, prezzi Doubao, prezzi Qwen, confronto modelli AI, calcolatore prezzi LLM, costi API AI",vi:"giá mô hình AI, giá ChatGPT, giá Claude, giá Gemini, giá DeepSeek, giá Doubao, giá Qwen, so sánh mô hình AI, máy tính giá LLM, chi phí API AI",uk:"ціни на AI моделі, ціни на ChatGPT, ціни на Claude, ціни на Gemini, ціни на DeepSeek, ціни на Doubao, ціни на Qwen, порівняння AI моделей, калькулятор цін LLM, витрати на AI API",bg:"Цени на AI модели, цени на ChatGPT, цени на Claude, цени на Gemini, цени на DeepSeek, цени на Doubao, цени на Qwen, сравнение на AI модели, калкулатор за цени на LLM, разходи за AI API"},canonical:{en:"https://aihubmix.com/models","zh-CN":"https://aihubmix.com/models?lang=zh-CN","zh-TW":"https://aihubmix.com/models?lang=zh-TW",ja:"https://aihubmix.com/models?lang=ja",ko:"https://aihubmix.com/models?lang=ko",de:"https://aihubmix.com/models?lang=de",es:"https://aihubmix.com/models?lang=es",fr:"https://aihubmix.com/models?lang=fr",pt:"https://aihubmix.com/models?lang=pt",ru:"https://aihubmix.com/models?lang=ru",tr:"https://aihubmix.com/models?lang=tr",pl:"https://aihubmix.com/models?lang=pl",nl:"https://aihubmix.com/models?lang=nl",it:"https://aihubmix.com/models?lang=it",vi:"https://aihubmix.com/models?lang=vi",uk:"https://aihubmix.com/models?lang=uk",bg:"https://aihubmix.com/models?lang=bg"}},autoRouter:{path:"/llm-router/auto",ogType:"website",title:{en:"LLM Router - Smart Routing for LLM APIs | AIHubMix","zh-CN":'智能路由 - model="auto" 自动选择最优大模型 | AIHubMix',"zh-TW":"LLM 路由器 - LLM API 的智能路由 | AIHubMix",ja:"LLMルーター - LLM APIのスマートルーティング | AIHubMix",ko:"LLM 라우터 - LLM API를 위한 스마트 라우팅 | AIHubMix",de:"LLM Router - Intelligentes Routing für LLM APIs | AIHubMix",es:"Router LLM - Enrutamiento Inteligente para APIs LLM | AIHubMix",fr:"Routeur LLM - Routage Intelligent pour APIs LLM | AIHubMix",pt:"Roteador LLM - Roteamento Inteligente para APIs LLM | AIHubMix",ru:"LLM Router - Умный маршрутизатор для LLM API | AIHubMix",tr:"LLM Yönlendirici - LLM API'leri için Akıllı Yönlendirme | AIHubMix",pl:"Router LLM - Inteligentne Routing dla API LLM | AIHubMix",nl:"LLM Router - Slimme Routering voor LLM API's | AIHubMix",it:"Router LLM - Routing Intelligente per API LLM | AIHubMix",vi:"Router LLM - Định Tuyến Thông Minh cho API LLM | AIHubMix",uk:"Маршрутизатор LLM - Розумне маршрутизування для LLM API | AIHubMix",bg:"LLM Маршрутизатор - Умен маршрутизиране за LLM API | AIHubMix"},description:{en:'Set model to "auto" and the AIHubMix LLM Router picks the best LLM for every request — the cheapest that clears the bar, the strongest when it matters. 4 routing strategies, normalized scores across 5 categories and 23 dimensions, continuously updated model pool.',"zh-CN":"把 model 设为 auto，智能路由为每个请求实时选出最优模型——能力达标选最便宜，关键任务交给最强。4 种路由策略，5 大类 23 个子维度归一化评分，模型池持续更新。","zh-TW":"將模型設置為「自動」，AIHubMix LLM 路由器為每個請求選擇最佳 LLM——最便宜的符合標準，最強大的在關鍵時刻。4 種路由策略，5 個類別和 23 個維度的標準化分數，持續更新的模型池。",ja:"モデルを「自動」に設定すると、AIHubMix LLMルーターがすべてのリクエストに最適なLLMを選択 — クリアする最も安価なもの、重要な時に最も強力なもの。4つのルーティング戦略、5つのカテゴリと23の次元にわたる正規化スコア、継続的に更新されるモデルプール。",ko:'"자동"으로 모델을 설정하면 AIHubMix LLM 라우터가 모든 요청에 대해 최적의 LLM을 선택합니다. 기준을 통과하는 가장 저렴한 모델, 중요할 때 가장 강력한 모델. 4가지 라우팅 전략, 5개 카테고리 및 23개 차원에서 정규화된 점수, 지속적으로 업데이트되는 모델 풀.',de:'Stellen Sie das Modell auf "auto" und der AIHubMix LLM Router wählt das beste LLM für jede Anfrage aus — das günstigste, das die Anforderungen erfüllt, das stärkste, wenn es darauf ankommt. 4 Routing-Strategien, normalisierte Bewertungen in 5 Kategorien und 23 Dimensionen, kontinuierlich aktualisierter Modellpool.',es:'Configura el modelo en "auto" y el Router LLM de AIHubMix selecciona el mejor LLM para cada solicitud: el más económico que cumple con los requisitos, el más potente cuando es necesario. 4 estrategias de enrutamiento, puntuaciones normalizadas en 5 categorías y 23 dimensiones, pool de modelos continuamente actualizado.',fr:'Réglez le modèle sur "auto" et le Routeur LLM AIHubMix choisit le meilleur LLM pour chaque demande — le moins cher qui répond aux critères, le plus puissant quand cela compte. 4 stratégies de routage, scores normalisés sur 5 catégories et 23 dimensions, pool de modèles continuellement mis à jour.',pt:'Defina o modelo como "auto" e o Roteador LLM do AIHubMix escolhe o melhor LLM para cada solicitação — o mais barato que atende ao critério, o mais forte quando necessário. 4 estratégias de roteamento, pontuações normalizadas em 5 categorias e 23 dimensões, pool de modelos continuamente atualizado.',ru:'Установите модель на "авто", и маршрутизатор LLM AIHubMix выберет лучший LLM для 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_m},CH=e=>`${O5()}?model=${encodeURIComponent(e)}`,wT={home:"ホーム",policies:"規約・ポリシー",terms:"サービス利用規約",privacy:"プライバシーポリシー",cookies:"Cookieポリシー",payment:"決済に関する通知",zdr:"ZDR規約","asynchronous-generation":"非同期生成規約","auto-recharge":"自動チャージ契約",documents:"法務文書",onThisPage:"このページの内容",sections:"{{count}}項目",documentSections:"文書の各項目",originalContents:"原文の内容",table:"ポリシー参照表",related:"関連する契約・規約",contact:"お問い合わせ",backToTop:"ページの先頭へ",notFound:"ページが見つかりません",backHome:"ホームに戻る",legal:"法務情報",translationDraft:"AI翻訳によるレビュー用ドラフトです。公開済み、または法務承認済みの翻訳ではありません。承認前に英語原文と比較してください。",source:"英語原文",migrationDraft:"移行用のローカルプレビューです。元の文言および日付を保持しています。",publishedSource:"公開済みの原文",draft:"ドラフトのプレビューです。法務レビュー待ちであり、効力はありません。公開日はリリース時に設定されます。",rechargeDraft:"ローカルプレビューです。公開承認待ちです。この契約を閲覧しても、自動チャージは有効になりません。",revisedDraft:"改訂ドラフトです。効力はありません。地域別の分析ルール、本番環境の同意管理、同意撤回へのアクセスおよびGA設定については、引き続き実装または検証が必要です。",loading:"翻訳を読み込み中...",loadFailed:"翻訳を読み込めませんでした。以下に英語原文を表示します。",retry:"再試行",cookiePreferences:"Cookie設定",cookieCopy:"当社は必須Cookieを使用し、お客様の同意を得た場合はサイト改善のためにGoogle AnalyticsのCookieも使用します。広告目的では使用しません。",accept:"許可",reject:"拒否"},kT={home:"홈",policies:"약관 및 정책",terms:"서비스 약관",privacy:"개인정보 처리방침",cookies:"쿠키 정책",payment:"결제 안내",zdr:"ZDR 약관","asynchronous-generation":"비동기 생성 약관","auto-recharge":"자동 충전 계약",documents:"법률 문서",onThisPage:"이 페이지의 내용",sections:"{{count}}개 절",documentSections:"문서의 절",originalContents:"원본 문서 내용",table:"정책 참조 표",related:"관련 계약",contact:"문의하기",backToTop:"맨 위로",notFound:"페이지를 찾을 수 없습니다",backHome:"홈으로 돌아가기",legal:"법률 정보",translationDraft:"AI 번역 검토 초안입니다. 게시되었거나 법적 승인을 받은 번역이 아닙니다. 승인 전에 영어 원문과 비교해 주십시오.",source:"영어 원문",migrationDraft:"로컬 이전 미리보기입니다. 원본 문구와 날짜를 유지했습니다.",publishedSource:"게시된 원문",draft:"초안 미리보기입니다. 법률 검토 대기 중이며 효력이 없습니다. 게시 날짜는 공개 시 설정됩니다.",rechargeDraft:"로컬 미리보기입니다. 게시 승인 대기 중입니다. 본 계약을 열람해도 자동 충전이 활성화되지 않습니다.",revisedDraft:"개정 초안입니다. 효력이 없습니다. 지역별 분석 규칙, 운영 환경의 동의 제어, 동의 철회 접근 경로 및 GA 구성은 아직 구현 또는 검증이 필요합니다.",loading:"번역을 불러오는 중...",loadFailed:"번역을 불러오지 못했습니다. 아래에 영어 원문이 표시됩니다.",retry:"다시 시도",cookiePreferences:"쿠키 기본 설정",cookieCopy:"당사는 필수 쿠키를 사용하며, 귀하의 동의를 받아 광고가 아닌 사이트 개선을 위해 Google Analytics 쿠키를 사용합니다.",accept:"수락",reject:"거부"},AT={home:"Startseite",policies:"Bedingungen und Richtlinien",terms:"Dienstbedingungen",privacy:"Datenschutzerklärung",cookies:"Cookie-Richtlinie",payment:"Zahlungshinweis",zdr:"ZDR-Bedingungen","asynchronous-generation":"Bedingungen für asynchrone Generierung","auto-recharge":"Vereinbarung zur automatischen Aufladung",documents:"Rechtliche Dokumente",onThisPage:"Auf dieser Seite",sections:"{{count}} Abschnitte",documentSections:"Dokumentabschnitte",originalContents:"Inhalt des Originaldokuments",table:"Referenztabelle zur Richtlinie",related:"Zugehörige Vereinbarungen",contact:"Kontakt",backToTop:"Nach oben",notFound:"Seite nicht gefunden",backHome:"Zurück zur Startseite",legal:"RECHTLICHES",translationDraft:"KI-übersetzter Prüfentwurf. Keine veröffentlichte oder rechtlich freigegebene Übersetzung. Vor der Freigabe mit der englischen Quelle vergleichen.",source:"Englische Quelle",migrationDraft:"Lokale Migrationsvorschau. Ursprünglicher Wortlaut und ursprüngliche Datumsangaben beibehalten.",publishedSource:"Veröffentlichte Quelle",draft:"Entwurfsvorschau. Rechtliche Prüfung ausstehend; nicht wirksam. Veröffentlichungsdaten werden bei Freigabe festgelegt.",rechargeDraft:"Lokale Vorschau. Veröffentlichungsfreigabe ausstehend. Das Ansehen dieser Vereinbarung aktiviert die automatische Aufladung nicht.",revisedDraft:"Überarbeiteter Entwurf. Nicht wirksam. Regionale Analyseregeln, Einwilligungskontrollen im Produktivbetrieb, Zugang zum Widerruf und GA-Konfiguration müssen noch umgesetzt oder überprüft werden.",loading:"Übersetzung wird geladen...",loadFailed:"Die Übersetzung konnte nicht geladen werden. Die englische Quelle wird unten angezeigt.",retry:"Erneut versuchen",cookiePreferences:"Cookie-Einstellungen",cookieCopy:"Wir verwenden notwendige Cookies und mit Ihrer Einwilligung Cookies von Google Analytics zur Verbesserung unserer Website, nicht für Werbung.",accept:"Akzeptieren",reject:"Ablehnen"},ST={home:"Inicio",policies:"Condiciones y políticas",terms:"Condiciones del servicio",privacy:"Política de privacidad",cookies:"Política de cookies",payment:"Aviso de pagos",zdr:"Condiciones de ZDR","asynchronous-generation":"Condiciones de generación asíncrona","auto-recharge":"Acuerdo de recarga automática",documents:"Documentos legales",onThisPage:"En esta página",sections:"{{count}} secciones",documentSections:"Secciones del documento",originalContents:"Contenido del documento original",table:"Tabla de referencia de la política",related:"Acuerdos relacionados",contact:"Contacto",backToTop:"Volver arriba",notFound:"Página no encontrada",backHome:"Volver al inicio",legal:"LEGAL",translationDraft:"Borrador de revisión traducido por IA. No es una traducción publicada ni aprobada jurídicamente. Compárelo con el original en inglés antes de su aprobación.",source:"Original en inglés",migrationDraft:"Vista previa local de la migración. Se conservan la redacción y las fechas originales.",publishedSource:"Original publicado",draft:"Vista previa del borrador. Pendiente de revisión jurídica; no está en vigor. Las fechas de publicación se fijarán al publicarse.",rechargeDraft:"Vista previa local. Pendiente de aprobación para su publicación. Consultar este acuerdo no habilita la Recarga Automática.",revisedDraft:"Borrador revisado. No está en vigor. Las normas regionales de analítica, los controles de consentimiento en producción, el acceso a la retirada del consentimiento y la configuración de GA aún requieren implementación o verificación.",loading:"Cargando traducción...",loadFailed:"No se ha podido cargar la traducción. A continuación se muestra el original en inglés.",retry:"Reintentar",cookiePreferences:"Preferencias de cookies",cookieCopy:"Utilizamos cookies esenciales y, con su consentimiento, cookies de Google Analytics para mejorar nuestro sitio, no para publicidad.",accept:"Aceptar",reject:"Rechazar"},MT={home:"Accueil",policies:"Conditions et politiques",terms:"Conditions de service",privacy:"Politique de confidentialité",cookies:"Politique relative aux cookies",payment:"Avis relatif aux paiements",zdr:"Conditions ZDR","asynchronous-generation":"Conditions de génération asynchrone","auto-recharge":"Accord de recharge automatique",documents:"Documents juridiques",onThisPage:"Sur cette page",sections:"{{count}} sections",documentSections:"Sections du document",originalContents:"Contenu du document original",table:"Tableau de référence de la politique",related:"Accords connexes",contact:"Nous contacter",backToTop:"Retour en haut",notFound:"Page introuvable",backHome:"Retour à l’accueil",legal:"INFORMATIONS JURIDIQUES",translationDraft:"Projet traduit par IA pour révision. Il ne s’agit pas d’une traduction publiée ou approuvée juridiquement. Comparer avec la source anglaise avant approbation.",source:"Source anglaise",migrationDraft:"Aperçu local de migration. Formulation et dates d’origine conservées.",publishedSource:"Source publiée",draft:"Aperçu du projet. En attente de révision juridique ; non en vigueur. Les dates de publication seront fixées lors de la publication.",rechargeDraft:"Aperçu local. En attente d’approbation pour publication. La consultation de cet accord n’active pas la Recharge automatique.",revisedDraft:"Projet révisé. Non en vigueur. Les règles régionales d’analyse, les contrôles de consentement en production, l’accès au retrait du consentement et la configuration GA doivent encore être mis en œuvre ou vérifiés.",loading:"Chargement de la traduction...",loadFailed:"La traduction n’a pas pu être chargée. La source anglaise est affichée ci-dessous.",retry:"Réessayer",cookiePreferences:"Préférences de cookies",cookieCopy:"Nous utilisons des cookies essentiels et, avec votre consentement, des cookies Google Analytics pour améliorer notre site, et non à des fins publicitaires.",accept:"Accepter",reject:"Refuser"},IT={home:"Início",policies:"Termos e políticas",terms:"Termos de Serviço",privacy:"Política de Privacidade",cookies:"Política de Cookies",payment:"Aviso de Pagamento",zdr:"Termos de ZDR","asynchronous-generation":"Termos de Geração Assíncrona","auto-recharge":"Acordo de Recarregamento Automático",documents:"Documentos jurídicos",onThisPage:"Nesta página",sections:"{{count}} secções",documentSections:"Secções do documento",originalContents:"Conteúdo do documento original",table:"Tabela de referência da política",related:"Acordos relacionados",contact:"Contacte-nos",backToTop:"Voltar ao topo",notFound:"Página não encontrada",backHome:"Voltar ao início",legal:"JURÍDICO",translationDraft:"Projeto de tradução por IA para revisão. Não é uma tradução publicada nem aprovada juridicamente. Compare com o original inglês antes da aprovação.",source:"Original inglês",migrationDraft:"Pré-visualização local da migração. Redação e datas originais mantidas.",publishedSource:"Original publicado",draft:"Pré-visualização do projeto. A aguardar revisão jurídica; não está em vigor. As datas de publicação serão definidas no lançamento.",rechargeDraft:"Pré-visualização local. A aguardar aprovação para publicação. A consulta deste acordo não ativa o Recarregamento Automático.",revisedDraft:"Projeto revisto. Não está em vigor. As regras regionais de análise, os controlos de consentimento em produção, o acesso à retirada do consentimento e a configuração do GA ainda carecem de implementação ou verificação.",loading:"A carregar tradução...",loadFailed:"Não foi possível carregar a tradução. O original inglês é apresentado abaixo.",retry:"Tentar novamente",cookiePreferences:"Preferências de cookies",cookieCopy:"Utilizamos cookies essenciais e, com o seu consentimento, cookies do Google Analytics para melhorar o nosso sítio, não para publicidade.",accept:"Aceitar",reject:"Rejeitar"},BT={home:"Главная",policies:"Условия и политики",terms:"Условия обслуживания",privacy:"Политика конфиденциальности",cookies:"Политика использования файлов cookie",payment:"Уведомление о платежах",zdr:"Условия ZDR","asynchronous-generation":"Условия асинхронной генерации","auto-recharge":"Соглашение об автоматическом пополнении",documents:"Юридические документы",onThisPage:"На этой странице",sections:"Разделов: {{count}}",documentSections:"Разделы документа",originalContents:"Содержание исходного документа",table:"Справочная таблица политики",related:"Связанные соглашения",contact:"Свяжитесь с нами",backToTop:"Наверх",notFound:"Страница не найдена",backHome:"На главную",legal:"ПРАВОВАЯ ИНФОРМАЦИЯ",translationDraft:"Черновик перевода, выполненного ИИ, для проверки. Не является опубликованным или юридически одобренным переводом. Перед одобрением сравните с английским оригиналом.",source:"Английский оригинал",migrationDraft:"Локальный предварительный просмотр переноса. Исходные формулировки и даты сохранены.",publishedSource:"Опубликованный источник",draft:"Предварительный просмотр черновика. Ожидает юридической проверки; не вступил в силу. Даты публикации будут установлены при выпуске.",rechargeDraft:"Локальный предварительный просмотр. Ожидает одобрения публикации. Просмотр этого соглашения не включает автоматическое пополнение.",revisedDraft:"Измененный черновик. Не вступил в силу. Региональные правила аналитики, средства управления согласием в рабочей среде, доступ к отзыву согласия и конфигурация GA по-прежнему требуют реализации или проверки.",loading:"Загрузка перевода...",loadFailed:"Не удалось загрузить перевод. Ниже показан английский оригинал.",retry:"Повторить",cookiePreferences:"Настройки cookie",cookieCopy:"Мы используем необходимые файлы cookie, а с вашего согласия — файлы cookie Google Analytics для улучшения сайта, не для рекламы.",accept:"Принять",reject:"Отклонить"},PT={home:"Ana Sayfa",policies:"Koşullar ve Politikalar",terms:"Hizmet Koşulları",privacy:"Gizlilik Politikası",cookies:"Çerez Politikası",payment:"Ödeme Bildirimi",zdr:"ZDR Koşulları","asynchronous-generation":"Asenkron Üretim Koşulları","auto-recharge":"Otomatik Bakiye Yükleme Sözleşmesi",documents:"Hukuki belgeler",onThisPage:"Bu sayfada",sections:"{{count}} bölüm",documentSections:"Belge bölümleri",originalContents:"Orijinal belge içeriği",table:"Politika referans tablosu",related:"İlgili sözleşmeler",contact:"Bizimle iletişime geçin",backToTop:"Başa dön",notFound:"Sayfa bulunamadı",backHome:"Ana sayfaya dön",legal:"HUKUKİ",translationDraft:"Yapay zekâ ile çevrilmiş inceleme taslağı. Yayımlanmış veya hukuken onaylanmış bir çeviri değildir. Onaylamadan önce İngilizce kaynakla karşılaştırın.",source:"İngilizce kaynak",migrationDraft:"Yerel taşıma önizlemesi. Orijinal ifadeler ve tarihler korunmuştur.",publishedSource:"Yayımlanmış kaynak",draft:"Taslak önizlemesi. Hukuki inceleme bekleniyor; yürürlükte değildir. Yayımlanma tarihleri yayına alınırken belirlenecektir.",rechargeDraft:"Yerel önizleme. Yayımlanma onayı bekleniyor. Bu sözleşmeyi görüntülemek Otomatik Bakiye Yüklemeyi etkinleştirmez.",revisedDraft:"Revize edilmiş taslak. Yürürlükte değildir. Bölgesel analiz kuralları, üretim ortamındaki rıza kontrolleri, rızayı geri çekme erişimi ve GA yapılandırması hâlâ uygulama veya doğrulama gerektirmektedir.",loading:"Çeviri yükleniyor...",loadFailed:"Çeviri yüklenemedi. İngilizce kaynak aşağıda gösterilmektedir.",retry:"Yeniden dene",cookiePreferences:"Çerez tercihleri",cookieCopy:"Sitemizi iyileştirmek için zorunlu çerezleri ve onayınızla Google Analytics çerezlerini kullanıyoruz; reklam amacıyla kullanmıyoruz.",accept:"Kabul Et",reject:"Reddet"},LT={home:"Strona główna",policies:"Regulaminy i polityki",terms:"Warunki świadczenia usług",privacy:"Polityka prywatności",cookies:"Polityka plików cookie",payment:"Informacja o płatnościach",zdr:"Warunki ZDR","asynchronous-generation":"Warunki generowania asynchronicznego","auto-recharge":"Umowa automatycznych doładowań",documents:"Dokumenty prawne",onThisPage:"Na tej stronie",sections:"Liczba sekcji: {{count}}",documentSections:"Sekcje dokumentu",originalContents:"Oryginalna treść dokumentu",table:"Tabela referencyjna polityki",related:"Powiązane umowy",contact:"Kontakt",backToTop:"Powrót na górę",notFound:"Nie znaleziono strony",backHome:"Powrót do strony głównej",legal:"INFORMACJE PRAWNE",translationDraft:"Wersja robocza przetłumaczona przez AI do weryfikacji. Nie jest opublikowanym ani zatwierdzonym prawnie tłumaczeniem. Przed zatwierdzeniem porównaj z angielskim tekstem źródłowym.",source:"Angielski tekst źródłowy",migrationDraft:"Lokalny podgląd migracji. Zachowano oryginalne brzmienie i daty.",publishedSource:"Opublikowany tekst źródłowy",draft:"Podgląd wersji roboczej. Oczekuje na weryfikację prawną; nie obowiązuje. Daty publikacji zostaną ustalone przy udostępnieniu.",rechargeDraft:"Podgląd lokalny. Oczekuje na zgodę na publikację. Wyświetlenie tej umowy nie włącza Automatycznego doładowania.",revisedDraft:"Zmieniona wersja robocza. Nie obowiązuje. Regionalne zasady analityki, mechanizmy zgody w środowisku produkcyjnym, dostęp do wycofania zgody i konfiguracja GA nadal wymagają wdrożenia lub weryfikacji.",loading:"Wczytywanie tłumaczenia...",loadFailed:"Nie udało się wczytać tłumaczenia. Poniżej wyświetlono angielski tekst źródłowy.",retry:"Spróbuj ponownie",cookiePreferences:"Preferencje plików cookie",cookieCopy:"Używamy niezbędnych plików cookie oraz, za Twoją zgodą, plików cookie Google Analytics w celu ulepszania witryny, a nie do reklam.",accept:"Akceptuj",reject:"Odrzuć"},_T={home:"Startpagina",policies:"Voorwaarden en beleid",terms:"Servicevoorwaarden",privacy:"Privacybeleid",cookies:"Cookiebeleid",payment:"Betalingsmededeling",zdr:"ZDR-voorwaarden","asynchronous-generation":"Voorwaarden voor asynchrone generatie","auto-recharge":"Overeenkomst voor automatisch opwaarderen",documents:"Juridische documenten",onThisPage:"Op deze pagina",sections:"{{count}} onderdelen",documentSections:"Documentonderdelen",originalContents:"Oorspronkelijke documentinhoud",table:"Referentietabel voor het beleid",related:"Gerelateerde overeenkomsten",contact:"Contact opnemen",backToTop:"Terug naar boven",notFound:"Pagina niet gevonden",backHome:"Terug naar de startpagina",legal:"JURIDISCH",translationDraft:"Door AI vertaald concept ter beoordeling. Geen gepubliceerde of juridisch goedgekeurde vertaling. Vergelijk vóór goedkeuring met de Engelse bron.",source:"Engelse bron",migrationDraft:"Lokaal migratievoorbeeld. Oorspronkelijke formuleringen en datums behouden.",publishedSource:"Gepubliceerde bron",draft:"Conceptvoorbeeld. In afwachting van juridische beoordeling; niet van kracht. Publicatiedatums worden bij publicatie vastgesteld.",rechargeDraft:"Lokaal voorbeeld. In afwachting van goedkeuring voor publicatie. Het bekijken van deze overeenkomst schakelt Automatisch opwaarderen niet in.",revisedDraft:"Herzien concept. Niet van kracht. Regionale regels voor analyse, toestemmingsinstellingen in de productieomgeving, toegang tot intrekking en de GA-configuratie moeten nog worden geïmplementeerd of geverifieerd.",loading:"Vertaling laden...",loadFailed:"De vertaling kon niet worden geladen. Hieronder wordt de Engelse bron weergegeven.",retry:"Opnieuw proberen",cookiePreferences:"Cookievoorkeuren",cookieCopy:"Wij gebruiken essentiële cookies en, met uw toestemming, Google Analytics-cookies om onze website te verbeteren, niet voor advertenties.",accept:"Accepteren",reject:"Weigeren"},DT={home:"Pagina iniziale",policies:"Condizioni e informative",terms:"Condizioni di servizio",privacy:"Informativa sulla privacy",cookies:"Informativa sui cookie",payment:"Avviso sui pagamenti",zdr:"Termini ZDR","asynchronous-generation":"Condizioni sulla generazione asincrona","auto-recharge":"Accordo sulla ricarica automatica",documents:"Documenti legali",onThisPage:"In questa pagina",sections:"{{count}} sezioni",documentSections:"Sezioni del documento",originalContents:"Contenuti del documento originale",table:"Tabella di riferimento dell'informativa",related:"Accordi correlati",contact:"Contattaci",backToTop:"Torna all'inizio",notFound:"Pagina non trovata",backHome:"Torna alla pagina iniziale",legal:"INFORMAZIONI LEGALI",translationDraft:"Bozza per revisione tradotta dall'IA. Non è una traduzione pubblicata né approvata legalmente. Confrontala con il testo originale inglese prima dell'approvazione.",source:"Testo originale inglese",migrationDraft:"Anteprima locale della migrazione. Formulazione e date originali mantenute.",publishedSource:"Testo originale pubblicato",draft:"Anteprima della bozza. In attesa di revisione legale; non efficace. Le date di pubblicazione saranno stabilite al momento del rilascio.",rechargeDraft:"Anteprima locale. In attesa di approvazione per la pubblicazione. La visualizzazione del presente accordo non abilita la Ricarica automatica.",revisedDraft:"Bozza modificata. Non efficace. Le regole regionali sulle analisi, i controlli del consenso in produzione, l'accesso alla revoca e la configurazione di GA richiedono ancora implementazione o verifica.",loading:"Caricamento della traduzione...",loadFailed:"Impossibile caricare la traduzione. Di seguito è mostrato il testo originale inglese.",retry:"Riprova",cookiePreferences:"Preferenze sui cookie",cookieCopy:"Utilizziamo cookie essenziali e, con il tuo consenso, cookie di Google Analytics per migliorare il nostro sito, non per la pubblicità.",accept:"Accetta",reject:"Rifiuta"},FT={home:"Trang chủ",policies:"Điều khoản và chính sách",terms:"Điều khoản dịch vụ",privacy:"Chính sách quyền riêng tư",cookies:"Chính sách cookie",payment:"Thông báo thanh toán",zdr:"Điều khoản ZDR","asynchronous-generation":"Điều khoản tạo nội dung bất đồng bộ","auto-recharge":"Thỏa thuận nạp tiền tự động",documents:"Tài liệu pháp lý",onThisPage:"Trên trang này",sections:"{{count}} mục",documentSections:"Các mục trong tài liệu",originalContents:"Nội dung tài liệu gốc",table:"Bảng tham chiếu chính sách",related:"Thỏa thuận liên quan",contact:"Liên hệ với chúng tôi",backToTop:"Về đầu trang",notFound:"Không tìm thấy trang",backHome:"Về trang chủ",legal:"PHÁP LÝ",translationDraft:"Bản nháp do AI dịch để rà soát. Không phải bản dịch đã công bố hoặc được phê duyệt về mặt pháp lý. Hãy đối chiếu với nguồn tiếng Anh trước khi phê duyệt.",source:"Nguồn tiếng Anh",migrationDraft:"Bản xem trước chuyển đổi cục bộ. Giữ nguyên câu chữ và ngày tháng gốc.",publishedSource:"Nguồn đã công bố",draft:"Bản xem trước dự thảo. Đang chờ rà soát pháp lý; chưa có hiệu lực. Ngày công bố sẽ được ấn định khi phát hành.",rechargeDraft:"Bản xem trước cục bộ. Đang chờ phê duyệt công bố. Việc xem thỏa thuận này không bật Nạp tiền tự động.",revisedDraft:"Bản dự thảo đã sửa đổi. Chưa có hiệu lực. Các quy tắc phân tích theo khu vực, biện pháp kiểm soát sự đồng ý trong môi trường vận hành thực tế, khả năng truy cập để rút lại sự đồng ý và cấu hình GA vẫn cần được triển khai hoặc xác minh.",loading:"Đang tải bản dịch...",loadFailed:"Không thể tải bản dịch. 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Не є опублікованим або юридично схваленим перекладом. Перед схваленням порівняйте з англійським оригіналом.",source:"Англійський оригінал",migrationDraft:"Локальний попередній перегляд перенесення. Оригінальні формулювання та дати збережено.",publishedSource:"Опублікований оригінал",draft:"Попередній перегляд чернетки. Очікує юридичної перевірки; не набрала чинності. Дати публікації буде встановлено під час випуску.",rechargeDraft:"Локальний попередній перегляд. Очікує схвалення публікації. Перегляд цієї угоди не вмикає Автоматичне поповнення.",revisedDraft:"Переглянута чернетка. Не набрала чинності. Регіональні правила аналітики, засоби керування згодою в робочому середовищі, доступ до відкликання згоди та конфігурація GA ще потребують впровадження або перевірки.",loading:"Завантаження перекладу...",loadFailed:"Не вдалося завантажити переклад. 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По-долу е показан английският източник.",retry:"Опитайте отново",cookiePreferences:"Предпочитания за бисквитките",cookieCopy:"Използваме основни бисквитки и, с вашето съгласие, бисквитки на Google Analytics, за да подобрим сайта си, а не за реклами.",accept:"Приемам",reject:"Отказвам"},RT={"zh-CN":{home:"首页",policies:"条款与政策",terms:"服务条款",privacy:"隐私政策",cookies:"Cookie 政策",payment:"支付须知",zdr:"ZDR 条款","asynchronous-generation":"异步生成条款","auto-recharge":"自动充值协议",documents:"法律文件",onThisPage:"本页内容",sections:"{{count}} 个章节",documentSections:"文档章节",originalContents:"原始文档内容",table:"政策参考表",related:"相关协议",contact:"联系我们",backToTop:"返回顶部",notFound:"页面未找到",backHome:"返回首页",legal:"法律信息",translationDraft:"AI 翻译审阅草稿。并非已发布或经法律审核批准的译文。批准前请与英文原文对照。",source:"英文原文",migrationDraft:"本地迁移预览。保留原文措辞和日期。",publishedSource:"已发布的原文",draft:"草稿预览。待法律审核，尚未生效。发布日期将在发布时确定。",rechargeDraft:"本地预览。待发布审批。查看本协议不会启用自动充值。",revisedDraft:"修订草稿。尚未生效。地区分析规则、生产环境同意控制、撤回同意入口及 GA 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Permanecem ativados independentemente da sua escolha sobre a análise.",analytics:"Análise do site",analyticsDescription:"O Google Analytics nos ajuda a entender o uso do site e melhorar nossos serviços, não sendo usado para publicidade. Sua escolha não afeta o login, os pagamentos, o acesso à API nem as configurações de ZDR.",regionalDefault:"A análise está ativada por padrão nesta região. Essa é uma configuração regional padrão, não um consentimento que você deu anteriormente.",consentRequired:"A análise permanece desativada até você aceitar. Fechar esta janela não significa aceitar.",accepted:"Aceito",rejected:"Recusado",unset:"Não selecionado"},bR={footer:"Je privacykeuzes",intro:"We gebruiken cookies en vergelijkbare technologieën om AIHubMix te laten werken en onze diensten te verbeteren. Je kunt je voorkeur voor websiteanalyse hier op elk moment wijzigen.",essential:"Noodzakelijke cookies",essentialDescription:"Deze cookies ondersteunen de werking van de website, het inloggen en de beveiliging. Ze blijven ingeschakeld, ongeacht je keuze voor analyse.",analytics:"Websiteanalyse",analyticsDescription:"Google Analytics helpt ons het websitegebruik te begrijpen en onze diensten te verbeteren, niet voor reclamedoeleinden. Je keuze heeft geen invloed op inloggen, betalingen, API-toegang of ZDR-instellingen.",regionalDefault:"Analyse is in deze regio standaard ingeschakeld. Dit is een regionale standaardinstelling, geen toestemming die je eerder hebt gegeven.",consentRequired:"Analyse blijft uitgeschakeld totdat je akkoord gaat. 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Bạn có thể thay đổi tùy chọn phân tích trang web tại đây bất cứ lúc nào.",essential:"Cookie thiết yếu",essentialDescription:"Các cookie này hỗ trợ chức năng trang web, đăng nhập và bảo mật. Chúng luôn được bật bất kể lựa chọn phân tích trang web của bạn.",analytics:"Phân tích trang web",analyticsDescription:"Google Analytics giúp chúng tôi hiểu cách trang web được sử dụng và cải thiện dịch vụ, không dùng cho quảng cáo. Lựa chọn của bạn không ảnh hưởng đến đăng nhập, thanh toán, quyền truy cập API hoặc cài đặt ZDR.",regionalDefault:"Phân tích trang web được bật mặc định tại khu vực này. Đây là cài đặt mặc định theo khu vực, không phải sự đồng ý trước đó của bạn.",consentRequired:"Phân tích trang web vẫn tắt cho đến khi bạn đồng ý. Đóng cửa sổ này không có nghĩa là bạn đồng ý.",accepted:"Đã đồng ý",rejected:"Đã từ chối",unset:"Chưa chọn"},IR={footer:"Налаштування приватності",intro:"Ми використовуємо файли cookie та подібні технології для роботи AIHubMix і покращення наших сервісів. 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It supports a 131K-token context window and flexible cognitive control."}]}]}},{id:"models-2026-09-09-0-0",category:"models",kind:"launch",publishedAt:"2026-09-09T00:00:00+08:00",models:["gpt-image-2.5-flare"],model:"gpt-image-2.5-flare",source:{zh:"https://docs.aihubmix.com/cn/update/News#release-2026-09-09",en:"https://docs.aihubmix.com/en/update/News#release-2026-09-09"},title:{zh:"gpt-image-2.5-flare 上线",en:"gpt-image-2.5-flare now available"},body:{zh:"新增 gpt-image-2.5-flare：OpenAI 图像模型，适合快速完成日常高质量生图，支持文本与图像输入、图像输出，以及 low、medium、high、xhigh、max 和 auto 六种质量设置。",en:"Added gpt-image-2.5-flare: OpenAI’s image model for fast, everyday high-quality image generation. 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It accepts text and image input, produces image output, and supports quality settings low, medium, high, xhigh, max, and auto."}]}]}},{id:"models-2026-09-09-0-1",category:"models",kind:"launch",publishedAt:"2026-09-09T00:00:00+08:00",models:["gpt-image-2.5-sunburst"],model:"gpt-image-2.5-sunburst",source:{zh:"https://docs.aihubmix.com/cn/update/News#release-2026-09-09",en:"https://docs.aihubmix.com/en/update/News#release-2026-09-09"},title:{zh:"gpt-image-2.5-sunburst 上线",en:"gpt-image-2.5-sunburst now available"},body:{zh:"新增 gpt-image-2.5-sunburst：OpenAI 图像生成与编辑模型，适合对编辑精度要求较高的工作流，支持文本与图像输入、图像输出，以及 low、medium、high、xhigh、max 和 auto 六种质量设置。",en:"Added gpt-image-2.5-sunburst: OpenAI’s image generation and editing model for workflows requiring high editing precision. It accepts text and image input, produces image output, and supports quality settings low, medium, high, xhigh, max, and auto."},blocks:{zh:[{tag:"li",children:[{tag:"text",text:"新增 "},{tag:"a",href:"https://aihubmix.com/model/gpt-image-2.5-sunburst",children:[{tag:"code",children:[{tag:"text",text:"gpt-image-2.5-sunburst"}]}]},{tag:"text",text:"：OpenAI 图像生成与编辑模型，适合对编辑精度要求较高的工作流，支持文本与图像输入、图像输出，以及 low、medium、high、xhigh、max 和 auto 六种质量设置。"}]}],en:[{tag:"li",children:[{tag:"text",text:"Added "},{tag:"a",href:"https://aihubmix.com/model/gpt-image-2.5-sunburst",children:[{tag:"code",children:[{tag:"text",text:"gpt-image-2.5-sunburst"}]}]},{tag:"text",text:": OpenAI’s image generation and editing model for workflows requiring high editing precision. It accepts text and image input, produces image output, and supports quality settings low, medium, high, xhigh, max, and auto."}]}]}},{id:"release-2026-09-09",category:"changelog",kind:"release",publishedAt:"2026-09-09T00:00:00+08:00",title:{zh:"媒体计费与调用体验优化",en:"Media billing and request experience improvements"},sourceTitle:{zh:"GPT Image 2.5 上线与媒体体验优化",en:"GPT Image 2.5: Models and media improvements"},releaseLabel:"Release 2026.09.09",source:{zh:"https://docs.aihubmix.com/cn/update/News#release-2026-09-09",en:"https://docs.aihubmix.com/en/update/News#release-2026-09-09"},sections:[{heading:{zh:"图片结算兜底更可靠",en:"Image billing fallback is more reliable"},blocks:{zh:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"当上游图片 usage 缺少 "},{tag:"code",children:[{tag:"text",text:"InputTokensDetails"}]},{tag:"text",text:" 时，平台会继续使用可用输入用量结算，不再把有效输入误记为 0；消费记录与相关日志保持同一口径。"}]},{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"完整 usage 明细仍优先按上游明细结算。"}]},{tag:"text",text:`
`}]}],en:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"When upstream image usage omits "},{tag:"code",children:[{tag:"text",text:"InputTokensDetails"}]},{tag:"text",text:", available input usage is still used for billing instead of being recorded as zero; consumption records and related logs use the same fallback."}]},{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"Complete upstream usage details remain preferred when available."}]},{tag:"text",text:`
`}]}]}},{heading:{zh:"公开模型信息更一致",en:"Public model information is more consistent"},blocks:{zh:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"模型信息接口现在只返回公开字段，已停用接入不会继续混入公开数据；模型详情和可用参数展示更稳定。"}]},{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"性能指标按实测结果回写，减少模型页面与接口展示过期指标的情况。"}]},{tag:"text",text:`
`}]}],en:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"Public model-information responses now expose only supported public fields, and retired integrations no longer appear in public data."}]},{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"Performance metrics are written back from measured results, reducing stale values in model details and API responses."}]},{tag:"text",text:`
`}]}]}},{heading:{zh:"媒体错误提示更清晰",en:"Media errors are clearer and more consistent"},blocks:{zh:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"图片、视频和异步媒体任务会使用统一的公开错误码、HTTP 状态和可操作提示，区分模型不可用、输入不合法、格式不支持、上游拒绝和结果过期等场景。"}]},{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"列表、详情和 webhook 的错误语义更一致；响应不会暴露上游原文、媒体内容或内部配置细节。"}]},{tag:"text",text:`
`}]}],en:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"Image, video, and async media endpoints now use consistent public error codes, HTTP statuses, and actionable messages for invalid input, unsupported formats, provider rejection, unavailable models, blocked output, and expired results."}]},{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"List, detail, and webhook responses use the same error semantics without exposing raw provider responses or media payloads."}]},{tag:"text",text:`
`}]}]}},{heading:{zh:"Playground 模型端点筛选更准确",en:"Playground endpoint filtering is more accurate"},blocks:{zh:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"Playground 现在会按协议版本筛选模型端点，其他协议版本的入口不会混入当前列表。"}]},{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"选择模型和端点时更少出现协议不匹配导致的调用失败。"}]},{tag:"text",text:`
`}]}],en:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"Model endpoints are now filtered by protocol version, preventing entries for other versions from appearing in a selected Playground/API list."}]},{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"This reduces protocol-mismatch failures when selecting a model or endpoint."}]},{tag:"text",text:`
`}]}]}},{heading:{zh:"旧版 Claude system 兼容性提升",en:"Legacy Claude system-role requests are more compatible"},blocks:{zh:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"旧版 Claude 模型仍使用 "},{tag:"code",children:[{tag:"text",text:"system"}]},{tag:"text",text:" 角色的请求，现在可在 Chat 和 Responses 调用中更稳定地转换处理。"}]},{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"兼容路径保留现有消息、缓存和 thinking 相关语义，调用方无需为旧模型单独改写请求。"}]},{tag:"text",text:`
`}]}],en:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"Older Claude models that still use a "},{tag:"code",children:[{tag:"text",text:"system"}]},{tag:"text",text:" role are now handled more consistently across Chat and Responses requests."}]},{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"The compatibility path preserves existing message, cache, and thinking behavior, so callers do not need a separate rewrite for legacy models."}]},{tag:"text",text:`
`}]}]}}]},{id:"models-2026-09-08-0-0",category:"models",kind:"launch",publishedAt:"2026-09-08T00:00:00+08:00",models:["deepseek-v4.1-flash"],model:"deepseek-v4.1-flash",source:{zh:"https://docs.aihubmix.com/cn/update/News#release-2026-09-08",en:"https://docs.aihubmix.com/en/update/News#release-2026-09-08"},title:{zh:"deepseek-v4.1-flash 上线",en:"deepseek-v4.1-flash now available"},body:{zh:"新增 deepseek-v4.1-flash：DeepSeek 采用新架构的原生多模态内测模型，支持文本与图像输入、文本输出，以及推理、工具调用和结构化输出。当前开放限流试用，仅供测试，不建议用于生产环境。",en:"Added deepseek-v4.1-flash: DeepSeek’s internal-beta model built on a new architecture with native multimodal support. It accepts text and image input, produces text output, and supports reasoning, tool calling, and structured outputs. Trial access is rate-limited and intended for testing only, and is not recommended for production."},blocks:{zh:[{tag:"li",children:[{tag:"text",text:"新增 "},{tag:"a",href:"https://aihubmix.com/model/deepseek-v4.1-flash",children:[{tag:"code",children:[{tag:"text",text:"deepseek-v4.1-flash"}]}]},{tag:"text",text:"：DeepSeek 采用新架构的原生多模态内测模型，支持文本与图像输入、文本输出，以及推理、工具调用和结构化输出。当前开放限流试用，仅供测试，不建议用于生产环境。"}]}],en:[{tag:"li",children:[{tag:"text",text:"Added "},{tag:"a",href:"https://aihubmix.com/model/deepseek-v4.1-flash",children:[{tag:"code",children:[{tag:"text",text:"deepseek-v4.1-flash"}]}]},{tag:"text",text:": DeepSeek’s internal-beta model built on a new architecture with native multimodal support. It accepts text and image input, produces text output, and supports reasoning, tool calling, and structured outputs. Trial access is rate-limited and intended for testing only, and is not recommended for production."}]}]}},{id:"release-2026-09-08",category:"changelog",kind:"release",publishedAt:"2026-09-08T00:00:00+08:00",title:{zh:"临时文件上传与 API 兼容性升级",en:"Temporary file uploads and API compatibility"},sourceTitle:{zh:"DeepSeek 多模态试用与临时文件上传",en:"DeepSeek V4.1 Flash: Models and platform improvements"},releaseLabel:"Release 2026.09.08",source:{zh:"https://docs.aihubmix.com/cn/update/News#release-2026-09-08",en:"https://docs.aihubmix.com/en/update/News#release-2026-09-08"},sections:[{heading:{zh:"Gemini usage 缺失时计费更稳",en:"Gemini billing is safer when usage is missing"},blocks:{zh:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"Gemini 原生透传请求在客户端中断或上游没有返回 usage 时，会基于已下发内容估算输出用量，避免把预扣值误当作最终实收导致异常超收。"}]},{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"Web Search 和 cachedContent 场景保留现有计费语义；有真实 usage 时仍优先使用上游返回的用量。"}]},{tag:"text",text:`
`}]}],en:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"For native Gemini passthrough calls, when the client disconnects or the provider does not return usage, AIHubMix now estimates delivered output usage from the content already sent back instead of treating the pre-bill value as final billing."}]},{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"Web Search and cachedContent keep their existing billing semantics; when provider usage is available, it still takes priority."}]},{tag:"text",text:`
`}]}]}},{heading:{zh:"模型路由错误更易排查",en:"Model routing errors are easier to diagnose"},blocks:{zh:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"当模型不存在或当前没有可用接入时，错误信息会回显用户原始请求的模型 ID，并引导到模型页复制正确 ID。映射、重定向和 fallback 不会改写这个回显名称，客服和调用方更容易定位失败请求。"}]},{tag:"text",text:`
`}]}],en:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"When a model does not exist or no compatible integration is available, the error message now echoes the original model ID from the request and points users to the model page for the correct ID. Mapping, redirects, and fallback no longer hide the model name the user actually sent."}]},{tag:"text",text:`
`}]}]}},{heading:{zh:"GPT 与 Claude 工具调用兼容性更稳",en:"GPT and Claude tool-call compatibility is steadier"},blocks:{zh:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"推理模型的 Chat 请求在桥接到 Responses 后，会过滤 "},{tag:"code",children:[{tag:"text",text:"temperature"}]},{tag:"text",text:" / "},{tag:"code",children:[{tag:"text",text:"top_p"}]},{tag:"text",text:" 等不被目标模型接受的采样参数，减少 gpt-5.6、gpt-6-astra 等模型的 400 错误。"}]},{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"AWS Bedrock Claude 路径会移除 Bedrock 当前不支持的 advisor 工具声明；其他工具、非 AWS 平台和消息内容保持原样。"}]},{tag:"text",text:`
`}]}],en:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"Reasoning-model Chat requests bridged to Responses now filter sampling parameters such as "},{tag:"code",children:[{tag:"text",text:"temperature"}]},{tag:"text",text:" and "},{tag:"code",children:[{tag:"text",text:"top_p"}]},{tag:"text",text:" when the target model does not accept them, reducing 400 errors for models such as gpt-5.6 and gpt-6-astra."}]},{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"AWS Bedrock Claude requests remove the advisor tool declaration that Bedrock does not currently support, while preserving other tools, non-AWS behavior, and message content."}]},{tag:"text",text:`
`}]}]}},{heading:{zh:"新增临时文件上传 API",en:"Temporary file upload API added"},blocks:{zh:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"新增 "},{tag:"code",children:[{tag:"text",text:"POST /v1/oss/upload"}]},{tag:"text",text:"，支持通过 API Key 上传单个 50 MB 以内的图片、PDF、文本、音频或视频文件，并返回 15 分钟有效的读取链接。"}]},{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"上传接口不单独计费，适合把多模态文件临时托管后交给模型使用；文件类型校验不等同于病毒扫描或所有模型输入兼容保证。"}]},{tag:"text",text:`
`}]}],en:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"Added "},{tag:"code",children:[{tag:"text",text:"POST /v1/oss/upload"}]},{tag:"text",text:" for API-key authenticated uploads of one image, PDF, text, audio, or video file up to 50 MB, returning a read URL valid for 15 minutes."}]},{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"The upload endpoint is not billed separately and is intended for temporarily hosting multimodal files before passing them to models. File-type validation is not a virus scan or a guarantee that every model accepts the file."}]},{tag:"text",text:`
`}]}]}}]},{id:"release-2026-09-07",category:"changelog",kind:"release",publishedAt:"2026-09-07T00:00:00+08:00",title:{zh:"筛选标签、工具调用与缓存优化",en:"Discovery labels, tools and caching improvements"},sourceTitle:{zh:"模型标签与 GPT 工具、缓存兼容优化",en:"GPT: Tools and prompt caching"},releaseLabel:"Release 2026.09.07",source:{zh:"https://docs.aihubmix.com/cn/update/News#release-2026-09-07",en:"https://docs.aihubmix.com/en/update/News#release-2026-09-07"},sections:[{heading:{zh:"模型标签显示更一致",en:"Model badges are more consistent"},blocks:{zh:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"模型广场、模型详情和 "},{tag:"code",children:[{tag:"text",text:"?tag=new"}]},{tag:"text",text:" 列表现在统一按上架时间派生 New 标签；New 会在窗口到期后自动消失，旧的 Hot 标签不再展示。近期上线模型不会因为供应商自绑定数据而丢失 New 角标。"}]},{tag:"text",text:`
`}]}],en:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"The model catalog, model detail pages, and "},{tag:"code",children:[{tag:"text",text:"?tag=new"}]},{tag:"text",text:" lists now derive the New badge from model launch time. New expires automatically after the configured window, and the old Hot badge is no longer shown. Recently launched parent models also keep their New badge when supplier self-binding data exists."}]},{tag:"text",text:`
`}]}]}},{heading:{zh:"GPT 工具调用兼容性提升",en:"GPT tool-call compatibility is improved"},blocks:{zh:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"gpt≥5.5 模型通过 "},{tag:"code",children:[{tag:"text",text:"/v1/chat/completions"}]},{tag:"text",text:" 搭配 tools 调用时，会自动走 Responses 兼容路径，减少上游 400 错误；使用 Claude Messages 入口转接这类模型的工具调用也覆盖该兼容口径。"}]},{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"OpenAI/Azure 直连渠道会过滤不兼容的 reasoning 参数，避免参数方言造成请求失败。"}]},{tag:"text",text:`
`}]}],en:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"gpt≥5.5 models called through "},{tag:"code",children:[{tag:"text",text:"/v1/chat/completions"}]},{tag:"text",text:" with tools now use the Responses compatibility path automatically, reducing upstream 400 errors. Claude Messages bridge calls for these GPT tool workflows follow the same compatibility path."}]},{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"OpenAI and Azure direct channels filter incompatible reasoning parameters before sending requests, avoiding failures caused by provider-specific parameter dialects."}]},{tag:"text",text:`
`}]}]}},{heading:{zh:"GPT 缓存写计费档位更准确",en:"GPT cache-write billing tiers are more accurate"},blocks:{zh:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"GPT 模型含缓存写入的请求会按原始总输入长度选择计费档位，长上下文首轮请求更贴近官方口径；普通输入、缓存写数量和非 GPT 模型计费逻辑不变。"}]},{tag:"text",text:`
`}]}],en:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"GPT requests that include cache writes now choose billing tiers from the original total input length, which better matches official long-context pricing behavior. Normal input counts, cache-write quantities, and non-GPT billing behavior are unchanged."}]},{tag:"text",text:`
`}]}]}}]},{id:"models-2026-09-06-0-0",category:"models",kind:"launch",publishedAt:"2026-09-06T00:00:00+08:00",models:["gpt-6-astra"],model:"gpt-6-astra",source:{zh:"https://docs.aihubmix.com/cn/update/News#release-2026-09-06",en:"https://docs.aihubmix.com/en/update/News#release-2026-09-06"},title:{zh:"gpt-6-astra 上线",en:"gpt-6-astra now available"},body:{zh:"新增 gpt-6-astra：OpenAI 面向复杂推理、编程、计算机操作、研究和文档创作的模型。支持 105 万 token 上下文、最高 128K 输出、从 low 到 max 的推理强度、异步工具调用，以及回合执行中的指令调整（mid-turn steering）。上线公告。",en:"Added gpt-6-astra: OpenAI’s model for complex reasoning, coding, computer use, research, and document creation. It supports a 1.05M-token context window, up to 128K output, low-to-max reasoning effort, asynchronous tool calling, and mid-turn steering. Launch announcement."},blocks:{zh:[{tag:"li",children:[{tag:"text",text:"新增 "},{tag:"a",href:"https://aihubmix.com/model/gpt-6-astra",children:[{tag:"code",children:[{tag:"text",text:"gpt-6-astra"}]}]},{tag:"text",text:"：OpenAI 面向复杂推理、编程、计算机操作、研究和文档创作的模型。支持 105 万 token 上下文、最高 128K 输出、从 low 到 max 的推理强度、异步工具调用，以及回合执行中的指令调整（mid-turn steering）。"},{tag:"a",href:"https://x.com/AiHubMix/status/2096102211524984984",children:[{tag:"text",text:"上线公告"}]},{tag:"text",text:"。"}]}],en:[{tag:"li",children:[{tag:"text",text:"Added "},{tag:"a",href:"https://aihubmix.com/model/gpt-6-astra",children:[{tag:"code",children:[{tag:"text",text:"gpt-6-astra"}]}]},{tag:"text",text:": OpenAI’s model for complex reasoning, coding, computer use, research, and document creation. It supports a 1.05M-token context window, up to 128K output, low-to-max reasoning effort, asynchronous tool calling, and mid-turn steering. "},{tag:"a",href:"https://x.com/AiHubMix/status/2096102211524984984",children:[{tag:"text",text:"Launch announcement"}]},{tag:"text",text:"."}]}]}},{id:"models-2026-09-06-0-1",category:"models",kind:"launch",publishedAt:"2026-09-06T00:00:00+08:00",models:["mai-image-2.6"],model:"mai-image-2.6",source:{zh:"https://docs.aihubmix.com/cn/update/News#release-2026-09-06",en:"https://docs.aihubmix.com/en/update/News#release-2026-09-06"},title:{zh:"mai-image-2.6 上线",en:"mai-image-2.6 now available"},body:{zh:"新增 mai-image-2.6：Microsoft 的图像生成与编辑模型，支持文生图、多种宽高比生成和图像引导编辑。",en:"Added mai-image-2.6: Microsoft’s image generation and editing model, supporting text-to-image generation and image-guided editing in multiple aspect ratios."},blocks:{zh:[{tag:"li",children:[{tag:"text",text:"新增 "},{tag:"a",href:"https://aihubmix.com/model/mai-image-2.6",children:[{tag:"code",children:[{tag:"text",text:"mai-image-2.6"}]}]},{tag:"text",text:"：Microsoft 的图像生成与编辑模型，支持文生图、多种宽高比生成和图像引导编辑。"}]}],en:[{tag:"li",children:[{tag:"text",text:"Added "},{tag:"a",href:"https://aihubmix.com/model/mai-image-2.6",children:[{tag:"code",children:[{tag:"text",text:"mai-image-2.6"}]}]},{tag:"text",text:": Microsoft’s image generation and editing model, supporting text-to-image generation and image-guided editing in multiple aspect ratios."}]}]}},{id:"models-2026-09-06-0-2",category:"models",kind:"launch",publishedAt:"2026-09-06T00:00:00+08:00",models:["mai-image-2.6-flash"],model:"mai-image-2.6-flash",source:{zh:"https://docs.aihubmix.com/cn/update/News#release-2026-09-06",en:"https://docs.aihubmix.com/en/update/News#release-2026-09-06"},title:{zh:"mai-image-2.6-flash 上线",en:"mai-image-2.6-flash now available"},body:{zh:"新增 mai-image-2.6-flash：Microsoft MAI-Image-2.6 的低延迟版本，支持多种宽高比的图像生成和图像引导编辑。",en:"Added mai-image-2.6-flash: Microsoft’s low-latency version of MAI-Image-2.6, supporting image generation in multiple aspect ratios and image-guided editing."},blocks:{zh:[{tag:"li",children:[{tag:"text",text:"新增 "},{tag:"a",href:"https://aihubmix.com/model/mai-image-2.6-flash",children:[{tag:"code",children:[{tag:"text",text:"mai-image-2.6-flash"}]}]},{tag:"text",text:"：Microsoft MAI-Image-2.6 的低延迟版本，支持多种宽高比的图像生成和图像引导编辑。"}]}],en:[{tag:"li",children:[{tag:"text",text:"Added "},{tag:"a",href:"https://aihubmix.com/model/mai-image-2.6-flash",children:[{tag:"code",children:[{tag:"text",text:"mai-image-2.6-flash"}]}]},{tag:"text",text:": Microsoft’s low-latency version of MAI-Image-2.6, supporting image generation in multiple aspect ratios and image-guided editing."}]}]}},{id:"release-2026-09-04",category:"changelog",kind:"release",publishedAt:"2026-09-04T00:00:00+08:00",title:{zh:"媒体并发调度与错误处理优化",en:"Media concurrency and error handling improvements"},sourceTitle:{zh:"图片高并发调度与媒体错误优化",en:"Image generation reliability"},releaseLabel:"Release 2026.09.04",source:{zh:"https://docs.aihubmix.com/cn/update/News#release-2026-09-04",en:"https://docs.aihubmix.com/en/update/News#release-2026-09-04"},sections:[{heading:{zh:"图片生成请求更稳",en:"Image generation requests are more stable"},blocks:{zh:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"图片生成、编辑、变体和 "},{tag:"code",children:[{tag:"text",text:"gpt-image-2"}]},{tag:"text",text:" 预测请求现在会结合 Azure 返回的限频信息和分布式在途上限调度请求。高并发或上游 429 场景下，平台会更早限流、释放容量并尝试可用渠道，减少图片请求被瞬时流量拖垮的情况。"}]},{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"依赖 "},{tag:"code",children:[{tag:"text",text:"by-bill"}]},{tag:"text",text:" 图片模型的请求在缺少上游用量字段时会使用统一兜底用量，限流与计费口径更一致；当 Redis 限流组件异常时，图片请求会保守失败并返回错误，避免无限制打到上游。"}]},{tag:"text",text:`
`}]}],en:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"Image generation, edits, variations, and "},{tag:"code",children:[{tag:"text",text:"gpt-image-2"}]},{tag:"text",text:" prediction requests now use Azure rate-limit headers together with distributed in-flight limits. Under high concurrency or upstream 429 responses, AIHubMix can throttle earlier, release capacity, and try available channels more consistently."}]},{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"For "},{tag:"code",children:[{tag:"text",text:"by-bill"}]},{tag:"text",text:" image models, missing upstream usage now falls back to a consistent usage value, keeping rate limits and billing aligned. If the Redis limit component is unavailable, image requests fail conservatively instead of sending unbounded traffic upstream."}]},{tag:"text",text:`
`}]}]}},{heading:{zh:"媒体任务错误返回更一致",en:"Media task errors are more consistent"},blocks:{zh:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"code",children:[{tag:"text",text:"/ai/v1"}]},{tag:"text",text:"、"},{tag:"code",children:[{tag:"text",text:"/call/media"}]},{tag:"text",text:"、任务查询、列表和 webhook 中的媒体任务失败会使用同一套公开错误码和失败快照。调用方看到的 HTTP 状态、"},{tag:"code",children:[{tag:"text",text:"error.code"}]},{tag:"text",text:" 与失败原因更一致，便于重试、展示和客服排查。"}]},{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"上游原文、媒体 URL、data URI、Base64 和内部错误细节不会进入用户可见响应；坏 JSON、未知模型、输入超限或上游拒绝等场景会归类到更明确的公开错误。"}]},{tag:"text",text:`
`}]}],en:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"Media task failures across "},{tag:"code",children:[{tag:"text",text:"/ai/v1"}]},{tag:"text",text:", "},{tag:"code",children:[{tag:"text",text:"/call/media"}]},{tag:"text",text:", task detail, task lists, and webhooks now render from the same public error codes and failure snapshots. Clients should see more consistent HTTP statuses, "},{tag:"code",children:[{tag:"text",text:"error.code"}]},{tag:"text",text:" values, and failure reasons for retries, UI display, and support workflows."}]},{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"Upstream raw text, media URLs, data URIs, Base64 payloads, and internal error details stay out of user-visible responses. Bad JSON, unknown models, oversized input, and upstream rejection are mapped to clearer public errors."}]},{tag:"text",text:`
`}]}]}}]},{id:"models-2026-09-03-0-0",category:"models",kind:"launch",publishedAt:"2026-09-03T00:00:00+08:00",models:["muse-spark-1.3"],model:"muse-spark-1.3",source:{zh:"https://docs.aihubmix.com/cn/update/News#release-2026-09-03",en:"https://docs.aihubmix.com/en/update/News#release-2026-09-03"},title:{zh:"muse-spark-1.3 上线",en:"muse-spark-1.3 now available"},body:{zh:"新增 muse-spark-1.3：Meta 面向长周期 Agent、多智能体与复杂编码工作流的多模态推理模型。支持文本、图像、音频和视频输入、文本输出、100 万 token 上下文与最高 100 万 token 输出，以及推理和工具能力。相比 Muse Spark 1.2，它以更少的不必要轮次和工具调用、更精炼的输出，提高长周期 Agent 与编码任务的执行效率。",en:"Added muse-spark-1.3: Meta’s multimodal reasoning model for long-running agentic, multi-agent, and complex coding workflows. It supports text, image, audio, and video input, text output, a 1M-token context window and up to 1M output, reasoning, and tools. Compared with Muse Spark 1.2, it completes long-horizon agentic and coding work more efficiently with fewer unnecessary turns and tool calls and more concise output."},blocks:{zh:[{tag:"li",children:[{tag:"text",text:"新增 "},{tag:"a",href:"https://aihubmix.com/model/muse-spark-1.3",children:[{tag:"code",children:[{tag:"text",text:"muse-spark-1.3"}]}]},{tag:"text",text:"：Meta 面向长周期 Agent、多智能体与复杂编码工作流的多模态推理模型。支持文本、图像、音频和视频输入、文本输出、100 万 token 上下文与最高 100 万 token 输出，以及推理和工具能力。相比 Muse Spark 1.2，它以更少的不必要轮次和工具调用、更精炼的输出，提高长周期 Agent 与编码任务的执行效率。"}]}],en:[{tag:"li",children:[{tag:"text",text:"Added "},{tag:"a",href:"https://aihubmix.com/model/muse-spark-1.3",children:[{tag:"code",children:[{tag:"text",text:"muse-spark-1.3"}]}]},{tag:"text",text:": Meta’s multimodal reasoning model for long-running agentic, multi-agent, and complex coding workflows. It supports text, image, audio, and video input, text output, a 1M-token context window and up to 1M output, reasoning, and tools. Compared with Muse Spark 1.2, it completes long-horizon agentic and coding work more efficiently with fewer unnecessary turns and tool calls and more concise output."}]}]}},{id:"models-2026-09-03-0-1",category:"models",kind:"launch",publishedAt:"2026-09-03T00:00:00+08:00",models:["gemini-3.8-flash"],model:"gemini-3.8-flash",source:{zh:"https://docs.aihubmix.com/cn/update/News#release-2026-09-03",en:"https://docs.aihubmix.com/en/update/News#release-2026-09-03"},title:{zh:"gemini-3.8-flash 上线",en:"gemini-3.8-flash now available"},body:{zh:"新增 gemini-3.8-flash：Google Flash 系列中智能能力最强的模型，面向长时间运行的软件工程、自主 Agent 与复杂企业工作流。支持文本、图像、音频和视频输入、100 万 token 上下文、最高 65K 输出、推理、联网、工具调用、结构化输出与长上下文。",en:"Added gemini-3.8-flash: Google’s most intelligent Flash-series model for long-running software engineering, autonomous agents, and complex enterprise workflows. It supports text, image, audio, and video input, a 1M-token context window, up to 65K output, reasoning, web access, tool calling, structured outputs, and long context."},blocks:{zh:[{tag:"li",children:[{tag:"text",text:"新增 "},{tag:"a",href:"https://aihubmix.com/model/gemini-3.8-flash",children:[{tag:"code",children:[{tag:"text",text:"gemini-3.8-flash"}]}]},{tag:"text",text:"：Google Flash 系列中智能能力最强的模型，面向长时间运行的软件工程、自主 Agent 与复杂企业工作流。支持文本、图像、音频和视频输入、100 万 token 上下文、最高 65K 输出、推理、联网、工具调用、结构化输出与长上下文。"}]}],en:[{tag:"li",children:[{tag:"text",text:"Added "},{tag:"a",href:"https://aihubmix.com/model/gemini-3.8-flash",children:[{tag:"code",children:[{tag:"text",text:"gemini-3.8-flash"}]}]},{tag:"text",text:": Google’s most intelligent Flash-series model for long-running software engineering, autonomous agents, and complex enterprise workflows. It supports text, image, audio, and video input, a 1M-token context window, up to 65K output, reasoning, web access, tool calling, structured outputs, and long context."}]}]}},{id:"models-2026-09-03-0-2",category:"models",kind:"launch",publishedAt:"2026-09-03T00:00:00+08:00",models:["gemini-3.8-flash-free"],model:"gemini-3.8-flash-free",source:{zh:"https://docs.aihubmix.com/cn/update/News#release-2026-09-03",en:"https://docs.aihubmix.com/en/update/News#release-2026-09-03"},title:{zh:"gemini-3.8-flash-free 上线",en:"gemini-3.8-flash-free now available"},body:{zh:"新增 gemini-3.8-flash-free：Gemini 3.8 Flash 的免费试用版本，保留核心多模态与 Agent 能力。免费资源有限、稳定性不保证，使用时可能出现 429；生产环境如需稳定并发，请使用 gemini-3.8-flash。",en:"Added gemini-3.8-flash-free: a free trial version of Gemini 3.8 Flash with the same core multimodal and agent capabilities. Resources are limited, stability is not guaranteed, and 429 errors may occur; use gemini-3.8-flash for production workloads requiring stable concurrency."},blocks:{zh:[{tag:"li",children:[{tag:"text",text:"新增 "},{tag:"a",href:"https://aihubmix.com/model/gemini-3.8-flash-free",children:[{tag:"code",children:[{tag:"text",text:"gemini-3.8-flash-free"}]}]},{tag:"text",text:"：Gemini 3.8 Flash 的免费试用版本，保留核心多模态与 Agent 能力。免费资源有限、稳定性不保证，使用时可能出现 429；生产环境如需稳定并发，请使用 gemini-3.8-flash。"}]}],en:[{tag:"li",children:[{tag:"text",text:"Added "},{tag:"a",href:"https://aihubmix.com/model/gemini-3.8-flash-free",children:[{tag:"code",children:[{tag:"text",text:"gemini-3.8-flash-free"}]}]},{tag:"text",text:": a free trial version of Gemini 3.8 Flash with the same core multimodal and agent capabilities. Resources are limited, stability is not guaranteed, and 429 errors may occur; use gemini-3.8-flash for production workloads requiring stable concurrency."}]}]}},{id:"models-2026-09-02-0-0",category:"models",kind:"launch",publishedAt:"2026-09-02T00:00:00+08:00",models:["claude-fable-5-1"],model:"claude-fable-5-1",source:{zh:"https://docs.aihubmix.com/cn/update/News#release-2026-09-02",en:"https://docs.aihubmix.com/en/update/News#release-2026-09-02"},title:{zh:"claude-fable-5-1 上线",en:"claude-fable-5-1 now available"},body:{zh:"新增 claude-fable-5-1：Anthropic 面向高强度推理、长周期 Agent、多步研究和文档/表格/幻灯片处理的旗舰模型。支持文本与图像输入、100 万 token 上下文，以及思考、工具调用、代码解释器、Computer Use、记忆工具、结构化输出、引用和提示词缓存；缓存读取价格较 Claude Fable 5 降至四分之一。",en:"Added claude-fable-5-1: Anthropic’s flagship model for demanding reasoning, long-running agents, multi-step research, and document, spreadsheet, and slide work. It supports text and image input, a 1M-token context window, thinking, tool calling, code interpreter, computer use, memory tool, structured outputs, citations, and prompt caching, with cached-input reads priced at one quarter of Claude Fable 5."},blocks:{zh:[{tag:"li",children:[{tag:"text",text:"新增 "},{tag:"a",href:"https://aihubmix.com/model/claude-fable-5-1",children:[{tag:"code",children:[{tag:"text",text:"claude-fable-5-1"}]}]},{tag:"text",text:"：Anthropic 面向高强度推理、长周期 Agent、多步研究和文档/表格/幻灯片处理的旗舰模型。支持文本与图像输入、100 万 token 上下文，以及思考、工具调用、代码解释器、Computer Use、记忆工具、结构化输出、引用和提示词缓存；缓存读取价格较 Claude Fable 5 降至四分之一。"}]}],en:[{tag:"li",children:[{tag:"text",text:"Added "},{tag:"a",href:"https://aihubmix.com/model/claude-fable-5-1",children:[{tag:"code",children:[{tag:"text",text:"claude-fable-5-1"}]}]},{tag:"text",text:": Anthropic’s flagship model for demanding reasoning, long-running agents, multi-step research, and document, spreadsheet, and slide work. It supports text and image input, a 1M-token context window, thinking, tool calling, code interpreter, computer use, memory tool, structured outputs, citations, and prompt caching, with cached-input reads priced at one quarter of Claude Fable 5."}]}]}},{id:"models-2026-09-02-0-1",category:"models",kind:"launch",publishedAt:"2026-09-02T00:00:00+08:00",models:["mercury-2.5-preview"],model:"mercury-2.5-preview",source:{zh:"https://docs.aihubmix.com/cn/update/News#release-2026-09-02",en:"https://docs.aihubmix.com/en/update/News#release-2026-09-02"},title:{zh:"mercury-2.5-preview 上线",en:"mercury-2.5-preview now available"},body:{zh:"新增 mercury-2.5-preview：Inception 的扩散式大语言模型预览版，面向高速度文本生成场景。支持 26 万 token 上下文，官方资料强调可并行生成和优化多个 token，标准 GPU 上生成速度可达 1,107 tokens/s；该模型能力标记尚未完整发布，接入前建议做最小调用验证。",en:"Added mercury-2.5-preview: Inception’s preview diffusion LLM for high-speed text generation. It supports a 260K-token context window, and the published model page highlights parallel token generation and optimization with up to 1,107 tokens/s on standard GPUs. Capability flags are not fully published yet, so validate target capabilities with a minimal call before relying on them."},blocks:{zh:[{tag:"li",children:[{tag:"text",text:"新增 "},{tag:"a",href:"https://aihubmix.com/model/mercury-2.5-preview",children:[{tag:"code",children:[{tag:"text",text:"mercury-2.5-preview"}]}]},{tag:"text",text:"：Inception 的扩散式大语言模型预览版，面向高速度文本生成场景。支持 26 万 token 上下文，官方资料强调可并行生成和优化多个 token，标准 GPU 上生成速度可达 1,107 tokens/s；该模型能力标记尚未完整发布，接入前建议做最小调用验证。"}]}],en:[{tag:"li",children:[{tag:"text",text:"Added "},{tag:"a",href:"https://aihubmix.com/model/mercury-2.5-preview",children:[{tag:"code",children:[{tag:"text",text:"mercury-2.5-preview"}]}]},{tag:"text",text:": Inception’s preview diffusion LLM for high-speed text generation. It supports a 260K-token context window, and the published model page highlights parallel token generation and optimization with up to 1,107 tokens/s on standard GPUs. Capability flags are not fully published yet, so validate target capabilities with a minimal call before relying on them."}]}]}},{id:"models-2026-09-02-0-2",category:"models",kind:"launch",publishedAt:"2026-09-02T00:00:00+08:00",models:["qwen3.8-max-2026-09-02"],model:"qwen3.8-max-2026-09-02",source:{zh:"https://docs.aihubmix.com/cn/update/News#release-2026-09-02",en:"https://docs.aihubmix.com/en/update/News#release-2026-09-02"},title:{zh:"qwen3.8-max-2026-09-02 上线",en:"qwen3.8-max-2026-09-02 now available"},body:{zh:"新增 qwen3.8-max-2026-09-02：Qwen3.8 Max 的 2026-09-02 快照版本，面向复杂工程项目、长程自主开发和多工具协作 Agent 场景。AIHubMix 当前记录为文本输入、约 99.1 万 token 上下文；能力标记尚未完整发布，依赖视觉、工具或结构化输出前需以实测为准。",en:"Added qwen3.8-max-2026-09-02: a September 2 snapshot of Qwen3.8 Max for complex engineering projects, long-horizon autonomous development, and multi-tool agent collaboration. AIHubMix currently lists text input and roughly 991K tokens of context; capability flags are not fully published yet, so verify vision, tool, or structured-output needs before production use."},blocks:{zh:[{tag:"li",children:[{tag:"text",text:"新增 "},{tag:"a",href:"https://aihubmix.com/model/qwen3.8-max-2026-09-02",children:[{tag:"code",children:[{tag:"text",text:"qwen3.8-max-2026-09-02"}]}]},{tag:"text",text:"：Qwen3.8 Max 的 2026-09-02 快照版本，面向复杂工程项目、长程自主开发和多工具协作 Agent 场景。AIHubMix 当前记录为文本输入、约 99.1 万 token 上下文；能力标记尚未完整发布，依赖视觉、工具或结构化输出前需以实测为准。"}]}],en:[{tag:"li",children:[{tag:"text",text:"Added "},{tag:"a",href:"https://aihubmix.com/model/qwen3.8-max-2026-09-02",children:[{tag:"code",children:[{tag:"text",text:"qwen3.8-max-2026-09-02"}]}]},{tag:"text",text:": a September 2 snapshot of Qwen3.8 Max for complex engineering projects, long-horizon autonomous development, and multi-tool agent collaboration. AIHubMix currently lists text input and roughly 991K tokens of context; capability flags are not fully published yet, so verify vision, tool, or structured-output needs before production use."}]}]}},{id:"models-2026-09-02-0-3",category:"models",kind:"launch",publishedAt:"2026-09-02T00:00:00+08:00",models:["minimax-h3-max"],model:"minimax-h3-max",source:{zh:"https://docs.aihubmix.com/cn/update/News#release-2026-09-02",en:"https://docs.aihubmix.com/en/update/News#release-2026-09-02"},title:{zh:"minimax-h3-max 上线",en:"minimax-h3-max now available"},body:{zh:"新增 minimax-h3-max：MiniMax H3 系列面向广告、品牌、电商、产品设计、UI/UX 和游戏创意工作流的多模态创作模型。AIHubMix 当前记录支持文本与图像输入；H3 系列资料强调视频、音频和参考编辑能力，具体能力标记尚未完整发布，生产接入前建议先验证目标链路。",en:"Added minimax-h3-max: a MiniMax H3-family multimodal creation model for advertising, branding, e-commerce, product design, UI/UX, and game workflows. AIHubMix currently lists text and image input; H3-family material highlights video, audio, and reference-editing workflows, while capability flags are not fully published yet, so validate the target path before production use."},blocks:{zh:[{tag:"li",children:[{tag:"text",text:"新增 "},{tag:"a",href:"https://aihubmix.com/model/minimax-h3-max",children:[{tag:"code",children:[{tag:"text",text:"minimax-h3-max"}]}]},{tag:"text",text:"：MiniMax H3 系列面向广告、品牌、电商、产品设计、UI/UX 和游戏创意工作流的多模态创作模型。AIHubMix 当前记录支持文本与图像输入；H3 系列资料强调视频、音频和参考编辑能力，具体能力标记尚未完整发布，生产接入前建议先验证目标链路。"}]}],en:[{tag:"li",children:[{tag:"text",text:"Added "},{tag:"a",href:"https://aihubmix.com/model/minimax-h3-max",children:[{tag:"code",children:[{tag:"text",text:"minimax-h3-max"}]}]},{tag:"text",text:": a MiniMax H3-family multimodal creation model for advertising, branding, e-commerce, product design, UI/UX, and game workflows. AIHubMix currently lists text and image input; H3-family material highlights video, audio, and reference-editing workflows, while capability flags are not fully published yet, so validate the target path before production use."}]}]}},{id:"release-2026-09-01",category:"changelog",kind:"release",publishedAt:"2026-09-01T00:00:00+08:00",title:{zh:"请求错误提示与媒体输入优化",en:"Request errors and media input improvements"},sourceTitle:{zh:"模型退役提示与媒体输入优化",en:"Model availability and media input"},releaseLabel:"Release 2026.09.01",source:{zh:"https://docs.aihubmix.com/cn/update/News#release-2026-09-01",en:"https://docs.aihubmix.com/en/update/News#release-2026-09-01"},sections:[{heading:{zh:"模型下线提示更明确",en:"Retired-model errors are clearer"},blocks:{zh:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"调用已下线模型时，API 会返回明确的 "},{tag:"code",children:[{tag:"text",text:"model_retired"}]},{tag:"text",text:" 404 错误，而不是笼统的模型不存在提示；如果已配置继任模型，响应头会带上下线和替代模型信息，便于客户端迁移。"}]},{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"创建或编辑 Key 时会拒绝已下线模型，智能路由也会避开已下线模型，减少请求落到不可用模型后的失败。"}]},{tag:"text",text:`
`}]}],en:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"Calls to retired models now return an explicit "},{tag:"code",children:[{tag:"text",text:"model_retired"}]},{tag:"text",text:" 404 instead of a generic model-not-found response. When a successor model is configured, response headers include retirement and successor information to help clients migrate."}]},{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"Key creation or editing rejects retired models, and smart routing avoids retired models, reducing failures caused by requests landing on unavailable models."}]},{tag:"text",text:`
`}]}]}},{heading:{zh:"媒体输入错误更可操作",en:"Media input errors are more actionable"},blocks:{zh:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"图片、视频等媒体输入格式不受支持时，错误会稳定返回 "},{tag:"code",children:[{tag:"text",text:"media_form_unsupported"}]},{tag:"text",text:"，并说明检测到的媒体类型和当前模型可接受的输入形式。"}]},{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"同步、异步和回调场景的媒体错误提示保持一致，并避免把 URL、Base64 或内部原因暴露给用户。"}]},{tag:"text",text:`
`}]}],en:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"Unsupported image, video, and other media inputs now consistently return "},{tag:"code",children:[{tag:"text",text:"media_form_unsupported"}]},{tag:"text",text:", with the detected media type and the input forms accepted by the selected model."}]},{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"Media error messages are aligned across sync, async, and webhook flows, while URLs, Base64 payloads, and internal causes stay hidden from user-facing responses."}]},{tag:"text",text:`
`}]}]}},{heading:{zh:"Google 图片输入上限临时放宽",en:"Google image input validation is temporarily higher"},blocks:{zh:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"AIHubMix 侧对 Google/Gemini 图片输入的网关校验临时放宽到 20 MB，较大的图片请求不会再被平台提前拦截。"}]},{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"由于当前链路仍使用 Google inline media 方式，超过 Google 实际可接受范围的输入仍可能被上游拒绝；大文件调用仍需关注返回错误。"}]},{tag:"text",text:`
`}]}],en:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"AIHubMix gateway validation for Google/Gemini image inputs is temporarily raised to 20 MB, so larger image requests are no longer blocked by the platform before upstream submission."}]},{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"The current path still uses Google’s inline media mode, so Google may still reject inputs above its effective accepted range. Watch upstream errors when sending large files."}]},{tag:"text",text:`
`}]}]}}]},{id:"models-2026-08-28-0-0",category:"models",kind:"launch",publishedAt:"2026-08-28T00:00:00+08:00",models:["hy4-preview"],model:"hy4-preview",source:{zh:"https://docs.aihubmix.com/cn/update/News#release-2026-08-28",en:"https://docs.aihubmix.com/en/update/News#release-2026-08-28"},title:{zh:"hy4-preview 上线",en:"hy4-preview now available"},body:{zh:"新增 hy4-preview：腾讯混元 Hy4 Preview，采用 770B 总参数、49B 激活参数的 MoE 架构，面向 Agent、Coding 与生产力工作流优化。它强化复杂任务中的理解、规划、工具调用与持续执行，适合编码、复杂软件工程、文档处理、信息分析、办公自动化、网页生成及跨工具协作。支持 100 万 token 上下文、最高 64K 输出、推理、联网、工具/函数调用和结构化输出。",en:"Added hy4-preview: Tencent Hunyuan’s 770B-parameter MoE model with 49B active parameters, optimized for Agent, coding, and productivity workflows. It strengthens task understanding, planning, tool use, and sustained execution across complex multi-step tasks, software engineering, document processing, information analysis, office automation, web generation, and cross-tool collaboration. It supports a 1M-token context window, up to 64K output, reasoning, web search, tool/function calling, and structured outputs."},blocks:{zh:[{tag:"li",children:[{tag:"text",text:"新增 "},{tag:"a",href:"https://aihubmix.com/model/hy4-preview",children:[{tag:"code",children:[{tag:"text",text:"hy4-preview"}]}]},{tag:"text",text:"：腾讯混元 Hy4 Preview，采用 770B 总参数、49B 激活参数的 MoE 架构，面向 Agent、Coding 与生产力工作流优化。它强化复杂任务中的理解、规划、工具调用与持续执行，适合编码、复杂软件工程、文档处理、信息分析、办公自动化、网页生成及跨工具协作。支持 100 万 token 上下文、最高 64K 输出、推理、联网、工具/函数调用和结构化输出。"}]}],en:[{tag:"li",children:[{tag:"text",text:"Added "},{tag:"a",href:"https://aihubmix.com/model/hy4-preview",children:[{tag:"code",children:[{tag:"text",text:"hy4-preview"}]}]},{tag:"text",text:": Tencent Hunyuan’s 770B-parameter MoE model with 49B active parameters, optimized for Agent, coding, and productivity workflows. It strengthens task understanding, planning, tool use, and sustained execution across complex multi-step tasks, software engineering, document processing, information analysis, office automation, web generation, and cross-tool collaboration. It supports a 1M-token context window, up to 64K output, reasoning, web search, tool/function calling, and structured outputs."}]}]}},{id:"models-2026-08-27-0-0",category:"models",kind:"launch",publishedAt:"2026-08-27T00:00:00+08:00",models:["qwen3.8-flash"],model:"qwen3.8-flash",source:{zh:"https://docs.aihubmix.com/cn/update/News#release-2026-08-27",en:"https://docs.aihubmix.com/en/update/News#release-2026-08-27"},title:{zh:"qwen3.8-flash 上线",en:"qwen3.8-flash now available"},body:{zh:"新增 qwen3.8-flash：阿里云通义千问的旗舰原生视觉语言模型，适合编程、办公、长上下文推理和 Agent 工作流。支持 100 万 token 上下文、最高 128K 输出，以及思考、联网、工具调用和结构化输出。相比 Qwen3.7-Plus，进一步增强编程与办公能力，并提升训练和推理效率。",en:"Added qwen3.8-flash: Alibaba Cloud Qwen’s flagship native vision-language model for coding, office tasks, long-context reasoning, and agent workflows. It supports a 1M-token context window, up to 128K output, thinking, web access, tool calling, and structured outputs. Compared with Qwen3.7-Plus, it strengthens coding and office capabilities while improving training and inference efficiency."},blocks:{zh:[{tag:"li",children:[{tag:"text",text:"新增 "},{tag:"a",href:"https://aihubmix.com/model/qwen3.8-flash",children:[{tag:"code",children:[{tag:"text",text:"qwen3.8-flash"}]}]},{tag:"text",text:"：阿里云通义千问的旗舰原生视觉语言模型，适合编程、办公、长上下文推理和 Agent 工作流。支持 100 万 token 上下文、最高 128K 输出，以及思考、联网、工具调用和结构化输出。相比 Qwen3.7-Plus，进一步增强编程与办公能力，并提升训练和推理效率。"}]}],en:[{tag:"li",children:[{tag:"text",text:"Added "},{tag:"a",href:"https://aihubmix.com/model/qwen3.8-flash",children:[{tag:"code",children:[{tag:"text",text:"qwen3.8-flash"}]}]},{tag:"text",text:": Alibaba Cloud Qwen’s flagship native vision-language model for coding, office tasks, long-context reasoning, and agent workflows. It supports a 1M-token context window, up to 128K output, thinking, web access, tool calling, and structured outputs. Compared with Qwen3.7-Plus, it strengthens coding and office capabilities while improving training and inference efficiency."}]}]}},{id:"release-2026-08-27",category:"changelog",kind:"release",publishedAt:"2026-08-27T00:00:00+08:00",title:{zh:"MCP 图片工具调用优化",en:"MCP image tool improvements"},sourceTitle:{zh:"Qwen 3.8 Flash 与 MCP 图片工具更新",en:"Qwen 3.8 Flash: Models and MCP image tools"},releaseLabel:"Release 2026.08.27",source:{zh:"https://docs.aihubmix.com/cn/update/News#release-2026-08-27",en:"https://docs.aihubmix.com/en/update/News#release-2026-08-27"},sections:[{heading:{zh:"MCP 图片工具调用更明确",en:"MCP image-tool calls are clearer"},blocks:{zh:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"MCP 图片生成工具现在要求调用方显式传入模型，避免在未指定模型时被静默使用默认模型。"}]},{tag:"text",text:`
`},{tag:"li",children:[{tag:"code",children:[{tag:"text",text:"openai/gpt-image-1"}]},{tag:"text",text:" 是对外展示和转发使用的标准模型 ID；历史拼写错误仍作为兼容输入保留，旧客户端可继续提交但会被规范化处理。"}]},{tag:"text",text:`
`}]}],en:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"The MCP image generation tool now requires callers to pass a model explicitly, avoiding silent fallback to a default model when none is provided."}]},{tag:"text",text:`
`},{tag:"li",children:[{tag:"code",children:[{tag:"text",text:"openai/gpt-image-1"}]},{tag:"text",text:" is the standard model ID shown and forwarded externally. The historical typo remains accepted as an input-only compatibility alias and is normalized before the request is sent."}]},{tag:"text",text:`
`}]}]}}]},{id:"models-2026-08-26-0-0",category:"models",kind:"launch",publishedAt:"2026-08-26T00:00:00+08:00",models:["glm-5.3-flash"],model:"glm-5.3-flash",source:{zh:"https://docs.aihubmix.com/cn/update/News#release-2026-08-26",en:"https://docs.aihubmix.com/en/update/News#release-2026-08-26"},title:{zh:"glm-5.3-flash 上线",en:"glm-5.3-flash now available"},body:{zh:"新增 glm-5.3-flash：Z.AI 面向 Coding Agent、复杂推理和长周期软件工程任务的高效率多模态模型。支持文本、图像和视频输入、约 100 万 token 上下文、最高 128K 输出，以及思考、工具调用和结构化输出。模型基于现有 GLM 技术栈进一步后训练和优化，更侧重推理效率与响应速度。",en:"Added glm-5.3-flash: Z.AI’s high-efficiency multimodal model for coding agents, complex reasoning, and long-horizon software engineering tasks. It supports text, image, and video inputs, roughly 1 million tokens of context, up to 128K output, thinking, tool calling, and structured outputs. Built on the GLM technology stack with further post-training and optimization, it emphasizes inference efficiency and responsiveness."},blocks:{zh:[{tag:"li",children:[{tag:"text",text:"新增 "},{tag:"a",href:"https://aihubmix.com/model/glm-5.3-flash",children:[{tag:"code",children:[{tag:"text",text:"glm-5.3-flash"}]}]},{tag:"text",text:"：Z.AI 面向 Coding Agent、复杂推理和长周期软件工程任务的高效率多模态模型。支持文本、图像和视频输入、约 100 万 token 上下文、最高 128K 输出，以及思考、工具调用和结构化输出。模型基于现有 GLM 技术栈进一步后训练和优化，更侧重推理效率与响应速度。"}]}],en:[{tag:"li",children:[{tag:"text",text:"Added "},{tag:"a",href:"https://aihubmix.com/model/glm-5.3-flash",children:[{tag:"code",children:[{tag:"text",text:"glm-5.3-flash"}]}]},{tag:"text",text:": Z.AI’s high-efficiency multimodal model for coding agents, complex reasoning, and long-horizon software engineering tasks. It supports text, image, and video inputs, roughly 1 million tokens of context, up to 128K output, thinking, tool calling, and structured outputs. Built on the GLM technology stack with further post-training and optimization, it emphasizes inference efficiency and responsiveness."}]}]}},{id:"release-2026-08-26",category:"changelog",kind:"release",publishedAt:"2026-08-26T00:00:00+08:00",title:{zh:"Broadcast、远程 MCP 与 Access Key",en:"Broadcast, remote MCP and Access Keys"},sourceTitle:{zh:"Broadcast、远程 MCP 与 Access Key",en:"Broadcast, remote MCP and Access Key"},releaseLabel:"Release 2026.08.26",source:{zh:"https://docs.aihubmix.com/cn/update/News#release-2026-08-26",en:"https://docs.aihubmix.com/en/update/News#release-2026-08-26"},sections:[{heading:{zh:"新增功能",en:"New feature"},blocks:{zh:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"新增 "},{tag:"a",href:"https://docs.aihubmix.com/cn/api/Broadcast",children:[{tag:"text",text:"Broadcast"}]},{tag:"text",text:"：可将 AIHubMix API 请求的 Trace 自动发送到 LangWatch，无需在应用代码中额外埋点。支持隐私模式、采样率和 API Key 过滤，可在 LangWatch 中查看模型、token 用量、实际计费成本、耗时、会话和 Trace 信息。"}]},{tag:"text",text:`
`}]}],en:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"Added "},{tag:"a",href:"https://docs.aihubmix.com/en/api/Broadcast",children:[{tag:"text",text:"Broadcast"}]},{tag:"text",text:", which automatically sends traces from AIHubMix API requests to LangWatch without requiring additional application instrumentation. It supports privacy mode, sampling rates, and API key filtering, with model, token usage, actual billed cost, latency, session, and trace data available in LangWatch."}]},{tag:"text",text:`
`}]}]}},{heading:{zh:"MCP 与 Access Key 入口补齐",en:"MCP and Access Key entry points are more complete"},blocks:{zh:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"旧版 "},{tag:"code",children:[{tag:"text",text:"/mcp/"}]},{tag:"text",text:" 入口可继续按既有鉴权方式使用，并统一到远程 HTTP MCP 能力；旧客户端不需要立即迁移，仍可访问模型、价格、文档检索、余额查询以及聊天、图像和视频工具。"}]},{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"控制台将提供只读 Access Key 复制入口，外部 MCP 客户端也可使用 "},{tag:"code",children:[{tag:"text",text:"X-Aihubmix-Access-Key"}]},{tag:"text",text:" 请求头；服务端不轮换密钥，不缓存该响应。"}]},{tag:"text",text:`
`}]}],en:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"The legacy "},{tag:"code",children:[{tag:"text",text:"/mcp/"}]},{tag:"text",text:" entry point can keep using the existing authentication flow while routing to the unified remote HTTP MCP capabilities. Older clients do not need to migrate immediately and can still access model and pricing lookup, document search, credit checks, chat, image, and video tools."}]},{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"The console will provide a read-only Access Key copy path, and external MCP clients can use the "},{tag:"code",children:[{tag:"text",text:"X-Aihubmix-Access-Key"}]},{tag:"text",text:" header. The server does not rotate the key or cache this response."}]},{tag:"text",text:`
`}]}]}},{heading:{zh:"图片透明背景请求更稳",en:"Transparent-background image requests are steadier"},blocks:{zh:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"code",children:[{tag:"text",text:"gpt-image-2"}]},{tag:"text",text:" 透明背景请求会优先走支持该能力的 OpenAI 原生渠道，减少在不支持透明背景的渠道间连续失败。其他图片请求路径不变。"}]},{tag:"text",text:`
`}]}],en:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"code",children:[{tag:"text",text:"gpt-image-2"}]},{tag:"text",text:" requests with transparent background now prefer OpenAI-native channels that support the capability, reducing repeated failures on channels that do not. Other image request paths are unchanged."}]},{tag:"text",text:`
`}]}]}},{heading:{zh:"Agent 文件更新更及时",en:"Agent-facing files update more quickly"},blocks:{zh:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"面向 AI Agent 和外部工具的模型清单与指南更新会更及时。"}]},{tag:"text",text:`
`}]}],en:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"Model lists and guides for AI Agents and external tools now refresh more promptly."}]},{tag:"text",text:`
`}]}]}}]},{id:"release-2026-08-25",category:"changelog",kind:"release",publishedAt:"2026-08-25T00:00:00+08:00",title:{zh:"旧版媒体接口迁移提示优化",en:"Legacy media API migration guidance"},sourceTitle:{zh:"旧版媒体接口迁移提示更新",en:"API migration guidance"},releaseLabel:"Release 2026.08.25",source:{zh:"https://docs.aihubmix.com/cn/update/News#release-2026-08-25",en:"https://docs.aihubmix.com/en/update/News#release-2026-08-25"},sections:[{heading:{zh:"旧版媒体接口迁移提示更明确",en:"Legacy media API migration errors are clearer"},blocks:{zh:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"对已关闭旧协议支持的媒体模型，"},{tag:"code",children:[{tag:"text",text:"/v1/images/generations"}]},{tag:"text",text:"、"},{tag:"code",children:[{tag:"text",text:"/v1/images/edits"}]},{tag:"text",text:"、"},{tag:"code",children:[{tag:"text",text:"/v1/models/:organization/:model/predictions"}]},{tag:"text",text:" 和 "},{tag:"code",children:[{tag:"text",text:"/v1/videos"}]},{tag:"text",text:" 会返回 "},{tag:"code",children:[{tag:"text",text:"protocol_not_supported"}]},{tag:"text",text:"，并提示迁移到 "},{tag:"code",children:[{tag:"text",text:"/ai/v1"}]},{tag:"text",text:" 异步媒体接口和对应文档。"}]},{tag:"text",text:`
`},{tag:"li",children:[{tag:"code",children:[{tag:"text",text:"model=auto"}]},{tag:"text",text:" 仍保持既有自动选模逻辑；如果最终选中的模型不支持旧接口，调用方会收到明确错误，而不是被静默切换到其他模型。Playground 也会隐藏这些模型的旧入口，减少误用。"}]},{tag:"text",text:`
`}]}],en:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"For media models that no longer support legacy protocol access, "},{tag:"code",children:[{tag:"text",text:"/v1/images/generations"}]},{tag:"text",text:", "},{tag:"code",children:[{tag:"text",text:"/v1/images/edits"}]},{tag:"text",text:", "},{tag:"code",children:[{tag:"text",text:"/v1/models/:organization/:model/predictions"}]},{tag:"text",text:", and "},{tag:"code",children:[{tag:"text",text:"/v1/videos"}]},{tag:"text",text:" now return "},{tag:"code",children:[{tag:"text",text:"protocol_not_supported"}]},{tag:"text",text:" with guidance to the "},{tag:"code",children:[{tag:"text",text:"/ai/v1"}]},{tag:"text",text:" async media APIs and related documentation."}]},{tag:"text",text:`
`},{tag:"li",children:[{tag:"code",children:[{tag:"text",text:"model=auto"}]},{tag:"text",text:" keeps the existing automatic model-selection behavior. If the final selected model does not support the legacy endpoint, callers receive a clear error instead of being silently switched to another model. Playground also hides legacy entries for these models to reduce accidental use."}]},{tag:"text",text:`
`}]}]}}]},{id:"models-2026-08-24-0-0",category:"models",kind:"launch",publishedAt:"2026-08-24T00:00:00+08:00",models:["wan3.0-video"],model:"wan3.0-video",source:{zh:"https://docs.aihubmix.com/cn/update/News#release-2026-08-24",en:"https://docs.aihubmix.com/en/update/News#release-2026-08-24"},title:{zh:"wan3.0-video 上线",en:"wan3.0-video now available"},body:{zh:"新增 wan3.0-video：阿里云通义万相预览版 All-in-One 视频模型，整合文生视频、首尾帧图生视频与参考视频工作流，支持最长 30 秒、30 fps 的 480P/720P/1080P 输出，适用于一体化生成与编辑。",en:"Added wan3.0-video: Alibaba Cloud Wan’s preview All-in-One video model unifies text-to-video, first/last-frame image-to-video, and reference-video workflows, supporting up to 30 seconds at 30 fps in 480P/720P/1080P for integrated generation and editing."},blocks:{zh:[{tag:"li",children:[{tag:"text",text:"新增 "},{tag:"a",href:"https://aihubmix.com/model/wan3.0-video",children:[{tag:"code",children:[{tag:"text",text:"wan3.0-video"}]}]},{tag:"text",text:"：阿里云通义万相预览版 All-in-One 视频模型，整合文生视频、首尾帧图生视频与参考视频工作流，支持最长 30 秒、30 fps 的 480P/720P/1080P 输出，适用于一体化生成与编辑。"}]}],en:[{tag:"li",children:[{tag:"text",text:"Added "},{tag:"a",href:"https://aihubmix.com/model/wan3.0-video",children:[{tag:"code",children:[{tag:"text",text:"wan3.0-video"}]}]},{tag:"text",text:": Alibaba Cloud Wan’s preview All-in-One video model unifies text-to-video, first/last-frame image-to-video, and reference-video workflows, supporting up to 30 seconds at 30 fps in 480P/720P/1080P for integrated generation and editing."}]}]}},{id:"models-2026-08-24-0-1",category:"models",kind:"launch",publishedAt:"2026-08-24T00:00:00+08:00",models:["wan3.0-video-prime"],model:"wan3.0-video-prime",source:{zh:"https://docs.aihubmix.com/cn/update/News#release-2026-08-24",en:"https://docs.aihubmix.com/en/update/News#release-2026-08-24"},title:{zh:"wan3.0-video-prime 上线",en:"wan3.0-video-prime now available"},body:{zh:"新增 wan3.0-video-prime：能力对齐的高速版，面向一体化视频生成与编辑工作流，提供更快的端到端生成周转。",en:"Added wan3.0-video-prime: a capability-aligned high-speed edition for integrated video generation and editing workflows, with faster end-to-end turnaround."},blocks:{zh:[{tag:"li",children:[{tag:"text",text:"新增 "},{tag:"a",href:"https://aihubmix.com/model/wan3.0-video-prime",children:[{tag:"code",children:[{tag:"text",text:"wan3.0-video-prime"}]}]},{tag:"text",text:"：能力对齐的高速版，面向一体化视频生成与编辑工作流，提供更快的端到端生成周转。"}]}],en:[{tag:"li",children:[{tag:"text",text:"Added "},{tag:"a",href:"https://aihubmix.com/model/wan3.0-video-prime",children:[{tag:"code",children:[{tag:"text",text:"wan3.0-video-prime"}]}]},{tag:"text",text:": a capability-aligned high-speed edition for integrated video generation and editing workflows, with faster end-to-end turnaround."}]}]}},{id:"models-2026-08-24-1-0",category:"models",kind:"price",publishedAt:"2026-08-24T00:00:00+08:00",models:["gpt-5.6-sol"],model:"gpt-5.6-sol",source:{zh:"https://docs.aihubmix.com/cn/update/News#release-2026-08-24",en:"https://docs.aihubmix.com/en/update/News#release-2026-08-24"},title:{zh:"GPT-5.6 Sol 价格下调",en:"GPT-5.6 Sol price reduction"},body:{zh:"gpt-5.6-sol 已同步官方价格下调 20%，当前价格为输入 $4/百万 token、输出 $20/百万 token。现有调用方式不变，复杂推理、编码和长上下文任务的调用成本降低。",en:"gpt-5.6-sol has synchronized the official 20% price reduction and is now priced at $4 per million input tokens and $20 per million output tokens. Existing request paths are unchanged, while complex reasoning, coding, and long-context workloads cost less to run."},blocks:{zh:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"a",href:"https://aihubmix.com/model/gpt-5.6-sol",children:[{tag:"code",children:[{tag:"text",text:"gpt-5.6-sol"}]}]},{tag:"text",text:" 已同步官方价格下调 20%，当前价格为输入 "},{tag:"text",text:"$4/百万 token、输出 $"},{tag:"text",text:"20/百万 token。现有调用方式不变，复杂推理、编码和长上下文任务的调用成本降低。"}]},{tag:"text",text:`
`}]}],en:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"a",href:"https://aihubmix.com/model/gpt-5.6-sol",children:[{tag:"code",children:[{tag:"text",text:"gpt-5.6-sol"}]}]},{tag:"text",text:" has synchronized the official 20% price reduction and is now priced at "},{tag:"text",text:"$4 per million input tokens and $"},{tag:"text",text:"20 per million output tokens. Existing request paths are unchanged, while complex reasoning, coding, and long-context workloads cost less to run."}]},{tag:"text",text:`
`}]}]}},{id:"release-2026-08-24",category:"changelog",kind:"release",publishedAt:"2026-08-24T00:00:00+08:00",title:{zh:"异步媒体任务开通引导优化",en:"Clearer async media activation guidance"},sourceTitle:{zh:"Wan 3.0 上线与 GPT-5.6 Sol 降价",en:"Wan 3.0 / GPT-5.6 Sol: Models and pricing updates"},releaseLabel:"Release 2026.08.24",source:{zh:"https://docs.aihubmix.com/cn/update/News#release-2026-08-24",en:"https://docs.aihubmix.com/en/update/News#release-2026-08-24"},sections:[{heading:{zh:"异步媒体任务开通提示更清楚",en:"Async media task enablement is clearer"},blocks:{zh:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"用户未开通异步任务功能时，媒体创建接口会在 "},{tag:"code",children:[{tag:"text",text:"async_not_enabled"}]},{tag:"text",text:" 错误中返回对应站点的控制台开通入口。aihubmix、inferera、shkq 用户会被引导到各自控制台，减少 403 后不知道如何开通的问题。"}]},{tag:"text",text:`
`}]}],en:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"When an account has not enabled async tasks, media creation APIs now include the matching console enablement page in the "},{tag:"code",children:[{tag:"text",text:"async_not_enabled"}]},{tag:"text",text:" error. aihubmix, inferera, and shkq users are guided to their own console, reducing dead-end 403 responses."}]},{tag:"text",text:`
`}]}]}}]},{id:"models-2026-08-21-0-0",category:"models",kind:"launch",publishedAt:"2026-08-21T00:00:00+08:00",models:["deepseek-v4-flash-0731-fast"],model:"deepseek-v4-flash-0731-fast",source:{zh:"https://docs.aihubmix.com/cn/update/News#release-2026-08-21",en:"https://docs.aihubmix.com/en/update/News#release-2026-08-21"},title:{zh:"deepseek-v4-flash-0731-fast 上线",en:"deepseek-v4-flash-0731-fast now available"},body:{zh:"新增 deepseek-v4-flash-0731-fast：DeepSeek Agent 模型的高速版本，面向编码、工具调用和高并发 Agent 工作负载。它保留 V4 Flash 0731 的能力并提供更快推理；相比 V4 Pro，更侧重低延迟与执行效率。支持 100 万 token 上下文窗口、最高 384K 输出、思考、工具调用和结构化输出。",en:"Added deepseek-v4-flash-0731-fast: a high-speed variant of DeepSeek’s agentic model for coding, tool use, and high-volume agent workloads. It preserves V4 Flash 0731 capabilities while delivering faster inference; compared with V4 Pro, it prioritizes lower latency and execution efficiency. It supports a 1M-token context window, up to 384K output, thinking, tool calling, and structured outputs."},blocks:{zh:[{tag:"li",children:[{tag:"text",text:"新增 "},{tag:"a",href:"https://aihubmix.com/model/deepseek-v4-flash-0731-fast",children:[{tag:"code",children:[{tag:"text",text:"deepseek-v4-flash-0731-fast"}]}]},{tag:"text",text:"：DeepSeek Agent 模型的高速版本，面向编码、工具调用和高并发 Agent 工作负载。它保留 V4 Flash 0731 的能力并提供更快推理；相比 V4 Pro，更侧重低延迟与执行效率。支持 100 万 token 上下文窗口、最高 384K 输出、思考、工具调用和结构化输出。"}]}],en:[{tag:"li",children:[{tag:"text",text:"Added "},{tag:"a",href:"https://aihubmix.com/model/deepseek-v4-flash-0731-fast",children:[{tag:"code",children:[{tag:"text",text:"deepseek-v4-flash-0731-fast"}]}]},{tag:"text",text:": a high-speed variant of DeepSeek’s agentic model for coding, tool use, and high-volume agent workloads. It preserves V4 Flash 0731 capabilities while delivering faster inference; compared with V4 Pro, it prioritizes lower latency and execution efficiency. It supports a 1M-token context window, up to 384K output, thinking, tool calling, and structured outputs."}]}]}},{id:"models-2026-08-21-0-1",category:"models",kind:"launch",publishedAt:"2026-08-21T00:00:00+08:00",models:["deepseek-v4-flash-vision-exp"],model:"deepseek-v4-flash-vision-exp",source:{zh:"https://docs.aihubmix.com/cn/update/News#release-2026-08-21",en:"https://docs.aihubmix.com/en/update/News#release-2026-08-21"},title:{zh:"deepseek-v4-flash-vision-exp 上线",en:"deepseek-v4-flash-vision-exp now available"},body:{zh:"新增 deepseek-v4-flash-vision-exp：DeepSeek 实验性多模态视觉理解模型，支持文本和图像输入、文本输出。适合图像描述、截图 OCR、图表分析和视觉 Agent；纯文本 Agent、推理与世界知识能力与 DeepSeek V4 Flash 正式版保持同级。",en:"Added deepseek-v4-flash-vision-exp: DeepSeek’s experimental multimodal visual-understanding model with text and image input and text output. It is suited to image description, screenshot OCR, chart analysis, and visually grounded agents; its pure-text agent, reasoning, and world-knowledge capabilities stay in line with the official DeepSeek V4 Flash release."},blocks:{zh:[{tag:"li",children:[{tag:"text",text:"新增 "},{tag:"a",href:"https://aihubmix.com/model/deepseek-v4-flash-vision-exp",children:[{tag:"code",children:[{tag:"text",text:"deepseek-v4-flash-vision-exp"}]}]},{tag:"text",text:"：DeepSeek 实验性多模态视觉理解模型，支持文本和图像输入、文本输出。适合图像描述、截图 OCR、图表分析和视觉 Agent；纯文本 Agent、推理与世界知识能力与 DeepSeek V4 Flash 正式版保持同级。"}]}],en:[{tag:"li",children:[{tag:"text",text:"Added "},{tag:"a",href:"https://aihubmix.com/model/deepseek-v4-flash-vision-exp",children:[{tag:"code",children:[{tag:"text",text:"deepseek-v4-flash-vision-exp"}]}]},{tag:"text",text:": DeepSeek’s experimental multimodal visual-understanding model with text and image input and text output. It is suited to image description, screenshot OCR, chart analysis, and visually grounded agents; its pure-text agent, reasoning, and world-knowledge capabilities stay in line with the official DeepSeek V4 Flash release."}]}]}},{id:"models-2026-08-21-0-2",category:"models",kind:"launch",publishedAt:"2026-08-21T00:00:00+08:00",models:["ox-alpha"],model:"ox-alpha",source:{zh:"https://docs.aihubmix.com/cn/update/News#release-2026-08-21",en:"https://docs.aihubmix.com/en/update/News#release-2026-08-21"},title:{zh:"ox-alpha 上线",en:"ox-alpha now available"},body:{zh:"新增 ox-alpha：由匿名第三方供应商提供的隐身推理模型，真实开发者和所有者尚未披露。AIHubMix 免费提供，支持文本和图像输入、文本输出及 1,048,576 token 上下文窗口，适合编程、长周期软件工程、持续 Agent 工作、复杂推理和图文工作流。",en:"Added ox-alpha, a stealth reasoning model from an anonymous third-party provider whose true developer and owner have not been disclosed. Free on AIHubMix, it supports text and image input, text output, and a 1,048,576-token context window for coding, long-horizon software engineering, sustained agentic work, complex reasoning, and text-plus-visual workflows."},blocks:{zh:[{tag:"li",children:[{tag:"text",text:"新增 "},{tag:"a",href:"https://aihubmix.com/model/ox-alpha",children:[{tag:"code",children:[{tag:"text",text:"ox-alpha"}]}]},{tag:"text",text:"：由匿名第三方供应商提供的隐身推理模型，真实开发者和所有者尚未披露。AIHubMix 免费提供，支持文本和图像输入、文本输出及 1,048,576 token 上下文窗口，适合编程、长周期软件工程、持续 Agent 工作、复杂推理和图文工作流。"}]}],en:[{tag:"li",children:[{tag:"text",text:"Added "},{tag:"a",href:"https://aihubmix.com/model/ox-alpha",children:[{tag:"code",children:[{tag:"text",text:"ox-alpha"}]}]},{tag:"text",text:", a stealth reasoning model from an anonymous third-party provider whose true developer and owner have not been disclosed. Free on AIHubMix, it supports text and image input, text output, and a 1,048,576-token context window for coding, long-horizon software engineering, sustained agentic work, complex reasoning, and text-plus-visual workflows."}]}]}},{id:"release-2026-08-21",category:"changelog",kind:"release",publishedAt:"2026-08-21T00:00:00+08:00",title:{zh:"图片生成失败恢复优化",en:"Image generation recovery improvements"},sourceTitle:{zh:"DeepSeek 新版本与图片生成恢复优化",en:"DeepSeek V4: Models and media improvements"},releaseLabel:"Release 2026.08.21",source:{zh:"https://docs.aihubmix.com/cn/update/News#release-2026-08-21",en:"https://docs.aihubmix.com/en/update/News#release-2026-08-21"},sections:[{heading:{zh:"图片生成失败恢复更稳",en:"Image generation failure recovery is steadier"},blocks:{zh:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"图片生成请求遇到临时服务错误时，自动恢复能力更稳定；当错误明确属于当前模型能力限制时，仍会停止重试并返回清晰原因。使用 "},{tag:"code",children:[{tag:"text",text:"gpt-image-2"}]},{tag:"text",text:" 等图片模型时，偶发服务差异导致的直接失败会减少。"}]},{tag:"text",text:`
`}]}],en:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"When an image generation request hits a temporary service error, AIHubMix now recovers more consistently. If the error clearly reflects a capability limit of the selected model, it still stops retrying and returns the reason. This should reduce direct failures caused by transient service differences for models such as "},{tag:"code",children:[{tag:"text",text:"gpt-image-2"}]},{tag:"text",text:"."}]},{tag:"text",text:`
`}]}]}}]},{id:"release-2026-08-20",category:"changelog",kind:"release",publishedAt:"2026-08-20T00:00:00+08:00",title:{zh:"Agent 接入与模型广场升级",en:"Agent access and model discovery improvements"},sourceTitle:{zh:"Agent 接入与模型广场升级",en:"Agent integration and model discovery"},releaseLabel:"Release 2026.08.20",source:{zh:"https://docs.aihubmix.com/cn/update/News#release-2026-08-20",en:"https://docs.aihubmix.com/en/update/News#release-2026-08-20"},sections:[{heading:{zh:"平台公告",en:"Platform announcement"},blocks:{zh:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"上线面向 AI Agent 的接入能力："},{tag:"a",href:"https://aihubmix.com/agents.md",children:[{tag:"code",children:[{tag:"text",text:"agents.md"}]}]},{tag:"text",text:" 接入说明、站点与每模型 "},{tag:"a",href:"https://aihubmix.com/llms.txt",children:[{tag:"code",children:[{tag:"text",text:"llms.txt"}]}]},{tag:"text",text:" 机读文档、"},{tag:"a",href:"https://aihubmix.com/skills/playground",children:[{tag:"text",text:"Playground Skill"}]},{tag:"text",text:"（固定地址分发）、Playground 深链（"},{tag:"code",children:[{tag:"text",text:"?models="}]},{tag:"text",text:" 多模型开局，一条链接开出最多 6 个模型的标签页；"},{tag:"code",children:[{tag:"text",text:"?config="}]},{tag:"text",text:" 配置直达）。需要并排查看两个模型的规格与价格，可用主站对比页 "},{tag:"code",children:[{tag:"text",text:"https://aihubmix.com/compare/{model_a}/{model_b}"}]},{tag:"text",text:"。入口清单见 "},{tag:"a",href:"https://docs.aihubmix.com/cn/agents",children:[{tag:"text",text:"Agent 接入"}]},{tag:"text",text:"。"}]},{tag:"text",text:`
`}]}],en:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"Launched agent-facing access capabilities: the "},{tag:"a",href:"https://aihubmix.com/agents.md",children:[{tag:"code",children:[{tag:"text",text:"agents.md"}]}]},{tag:"text",text:" onboarding guide, site-wide and per-model "},{tag:"a",href:"https://aihubmix.com/llms.txt",children:[{tag:"code",children:[{tag:"text",text:"llms.txt"}]}]},{tag:"text",text:" machine-readable documentation, the "},{tag:"a",href:"https://aihubmix.com/skills/playground",children:[{tag:"text",text:"Playground Skill"}]},{tag:"text",text:" (distributed at a fixed URL), and Playground deep links ("},{tag:"code",children:[{tag:"text",text:"?models="}]},{tag:"text",text:" opens up to 6 model tabs from one link, "},{tag:"code",children:[{tag:"text",text:"?config="}]},{tag:"text",text:" loads a configuration directly). To view two models’ specs and pricing side by side, use the compare page at "},{tag:"code",children:[{tag:"text",text:"https://aihubmix.com/compare/{model_a}/{model_b}"}]},{tag:"text",text:". See "},{tag:"a",href:"https://docs.aihubmix.com/en/agents",children:[{tag:"text",text:"Agent Access"}]},{tag:"text",text:" for the full list of entry points."}]},{tag:"text",text:`
`}]}]}},{heading:{zh:"图片生成 API 参数与错误更一致",en:"Image generation API parameters and errors are more consistent"},blocks:{zh:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"图片生成请求对 "},{tag:"code",children:[{tag:"text",text:"resolution"}]},{tag:"text",text:"、"},{tag:"code",children:[{tag:"text",text:"imageSize"}]},{tag:"text",text:" 等尺寸参数的处理更稳定；当请求参数不符合当前模型能力时，接口会返回更明确的错误说明，便于调用方快速修正。"}]},{tag:"text",text:`
`}]}],en:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"Image generation requests now handle size parameters such as "},{tag:"code",children:[{tag:"text",text:"resolution"}]},{tag:"text",text:" and "},{tag:"code",children:[{tag:"text",text:"imageSize"}]},{tag:"text",text:" more consistently. When a request is outside the current model capability, the API returns clearer errors so callers can fix parameters faster."}]},{tag:"text",text:`
`}]}]}},{heading:{zh:"模型广场支持主模型与多版本展示",en:"Model Hub now supports main models with versions"},blocks:{zh:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"GLM 5.2、Coding 版和 Free 版这类同一模型的不同形态可按“主模型 + 版本”归并展示。用户浏览模型广场、对比价格和选择版本时，更容易理解它们之间的关系。"}]},{tag:"text",text:`
`}]}],en:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"Related variants such as GLM 5.2, the Coding edition, and the Free edition can be presented as one main model with multiple versions. This makes browsing, price comparison, and version selection easier to understand."}]},{tag:"text",text:`
`}]}]}},{heading:{zh:"异步图片任务处理能力提升",en:"Async image tasks can move faster"},blocks:{zh:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"异步图片任务可按实时空闲能力并发推进。高峰期排队和处理延迟有望降低，任务状态与结果交付更稳定。"}]},{tag:"text",text:`
`}]}],en:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"Async image tasks can now progress with bounded concurrency based on available capacity. During busy periods, queues should clear more smoothly and task status/result delivery should be steadier."}]},{tag:"text",text:`
`}]}]}}]},{id:"models-2026-08-19-0-0",category:"models",kind:"launch",publishedAt:"2026-08-19T00:00:00+08:00",models:["gpt-5.6-sol-disc"],model:"gpt-5.6-sol-disc",source:{zh:"https://docs.aihubmix.com/cn/update/News#release-2026-08-19",en:"https://docs.aihubmix.com/en/update/News#release-2026-08-19"},title:{zh:"gpt-5.6-sol-disc 上线",en:"gpt-5.6-sol-disc now available"},body:{zh:"新增 gpt-5.6-sol-disc：AIHubMix 为 OpenAI GPT 5.6 Sol 提供的限时折扣线路，当前最高优惠 50%，底层能力与标准 gpt-5.6-sol 相同。该前沿推理模型适合复杂编程、专业知识工作、深度研究与长程 Agent，支持文本和图像输入、约 105 万 token 上下文、最高 12.8 万 token 输出，以及思考、工具和结构化输出。",en:"Added gpt-5.6-sol-disc: AIHubMix’s limited-time discount route for OpenAI’s GPT 5.6 Sol, offering up to 50% off with the same underlying capabilities as standard gpt-5.6-sol. This frontier reasoning model is suited to complex coding, professional knowledge work, deep research, and long-running agents, with text and image input, about 1.05M tokens of context, up to 128K output, thinking, tools, and structured outputs."},blocks:{zh:[{tag:"li",children:[{tag:"text",text:"新增 "},{tag:"a",href:"https://aihubmix.com/model/gpt-5.6-sol-disc",children:[{tag:"code",children:[{tag:"text",text:"gpt-5.6-sol-disc"}]}]},{tag:"text",text:"：AIHubMix 为 OpenAI GPT 5.6 Sol 提供的限时折扣线路，当前最高优惠 50%，底层能力与标准 "},{tag:"code",children:[{tag:"text",text:"gpt-5.6-sol"}]},{tag:"text",text:" 相同。该前沿推理模型适合复杂编程、专业知识工作、深度研究与长程 Agent，支持文本和图像输入、约 105 万 token 上下文、最高 12.8 万 token 输出，以及思考、工具和结构化输出。"}]}],en:[{tag:"li",children:[{tag:"text",text:"Added "},{tag:"a",href:"https://aihubmix.com/model/gpt-5.6-sol-disc",children:[{tag:"code",children:[{tag:"text",text:"gpt-5.6-sol-disc"}]}]},{tag:"text",text:": AIHubMix’s limited-time discount route for OpenAI’s GPT 5.6 Sol, offering up to 50% off with the same underlying capabilities as standard "},{tag:"code",children:[{tag:"text",text:"gpt-5.6-sol"}]},{tag:"text",text:". This frontier reasoning model is suited to complex coding, professional knowledge work, deep research, and long-running agents, with text and image input, about 1.05M tokens of context, up to 128K output, thinking, tools, and structured outputs."}]}]}},{id:"release-2026-08-19",category:"changelog",kind:"release",publishedAt:"2026-08-19T00:00:00+08:00",title:{zh:"媒体生成与任务交付优化",en:"Media generation and task delivery improvements"},sourceTitle:{zh:"GPT-5.6 Sol 折扣线路与媒体任务优化",en:"GPT-5.6 Sol: Models and media improvements"},releaseLabel:"Release 2026.08.19",source:{zh:"https://docs.aihubmix.com/cn/update/News#release-2026-08-19",en:"https://docs.aihubmix.com/en/update/News#release-2026-08-19"},sections:[{heading:{zh:"媒体生成任务更稳",en:"Media generation tasks are more reliable"},blocks:{zh:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"图片、视频等媒体任务的完成时间、结果过期时间和产物归档口径更一致；滚动发布或高并发场景下，任务状态、下载结果和用户侧记录更不容易出现延迟或重复处理。"}]},{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"媒体生产服务容量已提升，较大媒体响应的处理上限也更宽，长耗时或大产物任务的可用性更稳。"}]},{tag:"text",text:`
`}]}],en:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"Completion time, result expiry, and artifact archival are now more consistent for image and video tasks. During rolling releases or high concurrency, task status, downloadable results, and user-facing records are less likely to be delayed or processed twice."}]},{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"Media production capacity has been increased, and larger media responses have more headroom, improving availability for long-running or large-artifact tasks."}]},{tag:"text",text:`
`}]}]}}]},{id:"release-2026-08-18",category:"changelog",kind:"release",publishedAt:"2026-08-18T00:00:00+08:00",title:{zh:"详情页信息、媒体兼容与错误提示优化",en:"Model details, media compatibility and clearer errors"},sourceTitle:{zh:"模型信息与 Gemini 媒体兼容优化",en:"Model information and media compatibility"},releaseLabel:"Release 2026.08.18",source:{zh:"https://docs.aihubmix.com/cn/update/News#release-2026-08-18",en:"https://docs.aihubmix.com/en/update/News#release-2026-08-18"},sections:[{heading:{zh:"模型详情和页面展示更完整",en:"Model details are more complete"},blocks:{zh:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"模型详情现在会展示 "},{tag:"code",children:[{tag:"text",text:"release_date"}]},{tag:"text",text:"，模型列表和对比页也能同步读取，官方发布时间与站内上架时间更容易区分。"}]},{tag:"text",text:`
`}]}],en:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"Model details now expose "},{tag:"code",children:[{tag:"text",text:"release_date"}]},{tag:"text",text:", so the official release date is easier to distinguish from AIHubMix launch time across model pages and comparisons."}]},{tag:"text",text:`
`}]}]}},{heading:{zh:"Gemini 图片与上传兼容性更稳",en:"Gemini image and upload compatibility is steadier"},blocks:{zh:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"Gemini 图片生成/编辑和 Vertex 多图流程现在对 "},{tag:"code",children:[{tag:"text",text:"imageSize"}]},{tag:"text",text:" 与相关图片参数保留得更完整，尺寸和协议信息更不容易丢失。"}]},{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"Gemini 文件上传会保留原始 "},{tag:"code",children:[{tag:"text",text:"Content-Type"}]},{tag:"text",text:"，视频和其他媒体更不容易被误判为错误类型。"}]},{tag:"text",text:`
`}]}],en:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"Gemini image generation/editing and Vertex multi-image flows now preserve "},{tag:"code",children:[{tag:"text",text:"imageSize"}]},{tag:"text",text:" and related image parameters more consistently, reducing size or protocol mismatches."}]},{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"Gemini file uploads keep the original "},{tag:"code",children:[{tag:"text",text:"Content-Type"}]},{tag:"text",text:", so videos and other media are less likely to be misclassified."}]},{tag:"text",text:`
`}]}]}},{heading:{zh:"公开错误提示更可读",en:"Public errors are easier to read"},blocks:{zh:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"面向用户的 schema、枚举和能力限制报错现在会优先显示更清楚的文案，排查参数问题更直接。"}]},{tag:"text",text:`
`}]}],en:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"User-facing schema, enum, and capability errors now surface clearer messages, making parameter issues easier to diagnose."}]},{tag:"text",text:`
`}]}]}}]},{id:"models-2026-08-17-0-0",category:"models",kind:"launch",publishedAt:"2026-08-17T00:00:00+08:00",models:["glm-5.3"],model:"glm-5.3",source:{zh:"https://docs.aihubmix.com/cn/update/News#release-2026-08-17",en:"https://docs.aihubmix.com/en/update/News#release-2026-08-17"},title:{zh:"glm-5.3 上线",en:"glm-5.3 now available"},body:{zh:"新增 glm-5.3：Z.AI 旗舰级纯文本推理模型的正式版 API，面向复杂软件工程、长周期 Agent 任务与漏洞分析，支持 100 万 token 上下文和最高 12.8 万 token 输出。AIHubMix 提供无限并发，当前限时优惠 10%。",en:"Added glm-5.3, Z.AI’s production API for its text-only flagship reasoning model, built for complex software engineering, long-horizon Agent tasks, and vulnerability analysis. It supports a 1M-token context window, up to 128K output, and unlimited concurrency on AIHubMix, with a limited-time 10% discount."},blocks:{zh:[{tag:"li",children:[{tag:"text",text:"新增 "},{tag:"a",href:"https://aihubmix.com/model/glm-5.3",children:[{tag:"code",children:[{tag:"text",text:"glm-5.3"}]}]},{tag:"text",text:"：Z.AI 旗舰级纯文本推理模型的正式版 API，面向复杂软件工程、长周期 Agent 任务与漏洞分析，支持 100 万 token 上下文和最高 12.8 万 token 输出。AIHubMix 提供无限并发，当前限时优惠 10%。"}]}],en:[{tag:"li",children:[{tag:"text",text:"Added "},{tag:"a",href:"https://aihubmix.com/model/glm-5.3",children:[{tag:"code",children:[{tag:"text",text:"glm-5.3"}]}]},{tag:"text",text:", Z.AI’s production API for its text-only flagship reasoning model, built for complex software engineering, long-horizon Agent tasks, and vulnerability analysis. It supports a 1M-token context window, up to 128K output, and unlimited concurrency on AIHubMix, with a limited-time 10% discount."}]}]}},{id:"models-2026-08-15-0-0",category:"models",kind:"launch",publishedAt:"2026-08-15T00:00:00+08:00",models:["mai-thinking-1"],model:"mai-thinking-1",source:{zh:"https://docs.aihubmix.com/cn/update/News#release-2026-08-15",en:"https://docs.aihubmix.com/en/update/News#release-2026-08-15"},title:{zh:"mai-thinking-1 上线",en:"mai-thinking-1 now available"},body:{zh:"新增 mai-thinking-1：Microsoft MAI 系列推理模型，面向企业级推理、数学、通用智能和高吞吐工作负载。支持 256K 上下文窗口、Chat Completions 与结构化输出，适合需要长上下文推理、稳定响应格式和全天候调用成本控制的业务场景。",en:"Added mai-thinking-1: Microsoft MAI-series reasoning model for enterprise-scale reasoning, math, general intelligence, and high-throughput workloads. It supports a 256K-token context window, Chat Completions, and structured outputs, making it suitable for long-context reasoning, stable response formats, and cost-conscious always-on usage."},blocks:{zh:[{tag:"li",children:[{tag:"text",text:"新增 "},{tag:"a",href:"https://aihubmix.com/model/mai-thinking-1",children:[{tag:"code",children:[{tag:"text",text:"mai-thinking-1"}]}]},{tag:"text",text:"：Microsoft MAI 系列推理模型，面向企业级推理、数学、通用智能和高吞吐工作负载。支持 256K 上下文窗口、Chat Completions 与结构化输出，适合需要长上下文推理、稳定响应格式和全天候调用成本控制的业务场景。"}]}],en:[{tag:"li",children:[{tag:"text",text:"Added "},{tag:"a",href:"https://aihubmix.com/model/mai-thinking-1",children:[{tag:"code",children:[{tag:"text",text:"mai-thinking-1"}]}]},{tag:"text",text:": Microsoft MAI-series reasoning model for enterprise-scale reasoning, math, general intelligence, and high-throughput workloads. It supports a 256K-token context window, Chat Completions, and structured outputs, making it suitable for long-context reasoning, stable response formats, and cost-conscious always-on usage."}]}]}},{id:"models-2026-08-14-0-0",category:"models",kind:"launch",publishedAt:"2026-08-14T00:00:00+08:00",models:["gemini-3.7-flash"],model:"gemini-3.7-flash",source:{zh:"https://docs.aihubmix.com/cn/update/News#release-2026-08-14",en:"https://docs.aihubmix.com/en/update/News#release-2026-08-14"},title:{zh:"gemini-3.7-flash 上线",en:"gemini-3.7-flash now available"},body:{zh:"新增 gemini-3.7-flash：Google 原生多模态推理模型，面向编程、Agent、Web 开发和知识工作。支持文本、图像、音频和视频输入、100 万 tokens 上下文窗口、可调思考级别、工具调用、联网搜索和结构化输出；相比 Gemini 3.6 Flash，在编程、工具使用、多步规划和指令遵循方面进一步提升。",en:"Added gemini-3.7-flash: Google’s natively multimodal reasoning model for coding, agents, web development, and knowledge work. It supports text, image, audio, and video input, a 1M-token context window, adjustable thinking levels, tool calling, web search, and structured outputs, with stronger coding, tool use, multi-step planning, and instruction following than Gemini 3.6 Flash."},blocks:{zh:[{tag:"li",children:[{tag:"text",text:"新增 "},{tag:"a",href:"https://aihubmix.com/model/gemini-3.7-flash",children:[{tag:"code",children:[{tag:"text",text:"gemini-3.7-flash"}]}]},{tag:"text",text:"：Google 原生多模态推理模型，面向编程、Agent、Web 开发和知识工作。支持文本、图像、音频和视频输入、100 万 tokens 上下文窗口、可调思考级别、工具调用、联网搜索和结构化输出；相比 Gemini 3.6 Flash，在编程、工具使用、多步规划和指令遵循方面进一步提升。"}]}],en:[{tag:"li",children:[{tag:"text",text:"Added "},{tag:"a",href:"https://aihubmix.com/model/gemini-3.7-flash",children:[{tag:"code",children:[{tag:"text",text:"gemini-3.7-flash"}]}]},{tag:"text",text:": Google’s natively multimodal reasoning model for coding, agents, web development, and knowledge work. It supports text, image, audio, and video input, a 1M-token context window, adjustable thinking levels, tool calling, web search, and structured outputs, with stronger coding, tool use, multi-step planning, and instruction following than Gemini 3.6 Flash."}]}]}},{id:"models-2026-08-14-0-1",category:"models",kind:"launch",publishedAt:"2026-08-14T00:00:00+08:00",models:["coding-glm-5.3"],model:"coding-glm-5.3",source:{zh:"https://docs.aihubmix.com/cn/update/News#release-2026-08-14",en:"https://docs.aihubmix.com/en/update/News#release-2026-08-14"},title:{zh:"coding-glm-5.3 上线",en:"coding-glm-5.3 now available"},body:{zh:"新增 coding-glm-5.3：Z.AI 面向编程与 Agent 工作流的推理模型，适合复杂软件工程、长程 Agent 和漏洞分析。它与 GLM-5.2 使用相同底座，通过扩大后训练规模提升编程、任务执行和 token 效率。当前为限时预览版，仅供测试与评估，服务稳定性不作保证，不建议用于生产环境。",en:"Added coding-glm-5.3: Z.AI’s reasoning model for coding and agentic workflows, designed for complex software engineering, long-running agents, and vulnerability analysis. Built on the same base model as GLM-5.2, scaled post-training improves coding, task execution, and token efficiency. This limited-time preview is for testing and evaluation only; service stability is not guaranteed and production use is not recommended."},blocks:{zh:[{tag:"li",children:[{tag:"text",text:"新增 "},{tag:"a",href:"https://aihubmix.com/model/coding-glm-5.3",children:[{tag:"code",children:[{tag:"text",text:"coding-glm-5.3"}]}]},{tag:"text",text:"：Z.AI 面向编程与 Agent 工作流的推理模型，适合复杂软件工程、长程 Agent 和漏洞分析。它与 GLM-5.2 使用相同底座，通过扩大后训练规模提升编程、任务执行和 token 效率。当前为限时预览版，仅供测试与评估，服务稳定性不作保证，不建议用于生产环境。"}]}],en:[{tag:"li",children:[{tag:"text",text:"Added "},{tag:"a",href:"https://aihubmix.com/model/coding-glm-5.3",children:[{tag:"code",children:[{tag:"text",text:"coding-glm-5.3"}]}]},{tag:"text",text:": Z.AI’s reasoning model for coding and agentic workflows, designed for complex software engineering, long-running agents, and vulnerability analysis. Built on the same base model as GLM-5.2, scaled post-training improves coding, task execution, and token efficiency. This limited-time preview is for testing and evaluation only; service stability is not guaranteed and production use is not recommended."}]}]}},{id:"release-2026-08-14",category:"changelog",kind:"release",publishedAt:"2026-08-14T00:00:00+08:00",title:{zh:"API 兼容性与异步任务可靠性提升",en:"API compatibility and async task reliability"},sourceTitle:{zh:"Gemini 3.7 Flash 与 Coding GLM-5.3",en:"Gemini 3.7 / Coding GLM-5.3: API compatibility and task reliability"},releaseLabel:"Release 2026.08.14",source:{zh:"https://docs.aihubmix.com/cn/update/News#release-2026-08-14",en:"https://docs.aihubmix.com/en/update/News#release-2026-08-14"},sections:[{heading:{zh:"API 兼容性与任务可靠性",en:"API compatibility and task reliability"},blocks:{zh:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"Gemini 原生 embedding 请求现在能正确保留 Gemini Files API 返回的 "},{tag:"code",children:[{tag:"text",text:"fileUri"}]},{tag:"text",text:" 所属模型服务来源。使用 "},{tag:"code",children:[{tag:"text",text:"embedContent"}]},{tag:"text",text:" / "},{tag:"code",children:[{tag:"text",text:"batchEmbedContents"}]},{tag:"text",text:" 搭配已上传文件时，减少因文件来源不一致导致的 403；批量请求引用多个不同来源文件会明确返回 400。"}]},{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"OpenRouter 返回模型推理厂商结构化错误时，接口错误会优先展示内层 "},{tag:"code",children:[{tag:"text",text:"message"}]},{tag:"text",text:" / "},{tag:"code",children:[{tag:"text",text:"param"}]},{tag:"text",text:" / "},{tag:"code",children:[{tag:"text",text:"type"}]},{tag:"text",text:"，减少难以解析的长文案；单个模型推理厂商的错误也更不容易影响整个 OpenRouter 服务。"}]},{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"媒体异步任务的轮询、终态归档和产物处理更稳。图片、视频生成等长耗时任务在高并发、重启或产物较大时，更不容易重复处理、丢失终态或延迟交付；公开 API 字段、状态词和计费公式保持不变。"}]},{tag:"text",text:`
`}]}],en:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"Native Gemini embedding requests now preserve the model-service source for the "},{tag:"code",children:[{tag:"text",text:"fileUri"}]},{tag:"text",text:" returned by the Gemini Files API. When "},{tag:"code",children:[{tag:"text",text:"embedContent"}]},{tag:"text",text:" or "},{tag:"code",children:[{tag:"text",text:"batchEmbedContents"}]},{tag:"text",text:" references uploaded files, 403 errors caused by source mismatches are reduced; batch requests that mix files from different sources now return a clear 400."}]},{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"When OpenRouter returns a structured model provider error, the API error now prioritizes the inner "},{tag:"code",children:[{tag:"text",text:"message"}]},{tag:"text",text:", "},{tag:"code",children:[{tag:"text",text:"param"}]},{tag:"text",text:", and "},{tag:"code",children:[{tag:"text",text:"type"}]},{tag:"text",text:", reducing long, hard-to-parse messages. Single-provider errors are also less likely to affect the whole OpenRouter service."}]},{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"Media async task polling, terminal archival, and artifact handling are more reliable. Long-running image and video tasks are less likely to be processed twice, lose terminal state, or be delivered late during high concurrency, restarts, or large artifacts; public API fields, status words, and billing formulas are unchanged."}]},{tag:"text",text:`
`}]}]}}]},{id:"models-2026-08-13-0-0",category:"models",kind:"launch",publishedAt:"2026-08-13T00:00:00+08:00",models:["grok-4.6"],model:"grok-4.6",source:{zh:"https://docs.aihubmix.com/cn/update/News#release-2026-08-13",en:"https://docs.aihubmix.com/en/update/News#release-2026-08-13"},title:{zh:"grok-4.6 上线",en:"grok-4.6 now available"},body:{zh:"新增 grok-4.6：xAI 旗舰多模态推理模型，面向编程、长程 Agent、知识工作和交互式应用开发。支持图像理解、500K 上下文窗口、工具调用和结构化输出；相比 Grok 4.5，在多步执行、自我验证、编程和视觉项目生成方面进一步提升。",en:"Added grok-4.6: xAI’s flagship multimodal reasoning model for coding, long-running agents, knowledge work, and interactive application development. It supports image understanding, a 500K context window, tool calling, and structured outputs, with stronger multi-step execution, self-verification, coding, and visual project generation than Grok 4.5."},blocks:{zh:[{tag:"li",children:[{tag:"text",text:"新增 "},{tag:"a",href:"https://aihubmix.com/model/grok-4.6",children:[{tag:"code",children:[{tag:"text",text:"grok-4.6"}]}]},{tag:"text",text:"：xAI 旗舰多模态推理模型，面向编程、长程 Agent、知识工作和交互式应用开发。支持图像理解、500K 上下文窗口、工具调用和结构化输出；相比 Grok 4.5，在多步执行、自我验证、编程和视觉项目生成方面进一步提升。"}]}],en:[{tag:"li",children:[{tag:"text",text:"Added "},{tag:"a",href:"https://aihubmix.com/model/grok-4.6",children:[{tag:"code",children:[{tag:"text",text:"grok-4.6"}]}]},{tag:"text",text:": xAI’s flagship multimodal reasoning model for coding, long-running agents, knowledge work, and interactive application development. It supports image understanding, a 500K context window, tool calling, and structured outputs, with stronger multi-step execution, self-verification, coding, and visual project generation than Grok 4.5."}]}]}},{id:"models-2026-08-13-0-1",category:"models",kind:"launch",publishedAt:"2026-08-13T00:00:00+08:00",models:["deepseek-v4-pro-0813"],model:"deepseek-v4-pro-0813",source:{zh:"https://docs.aihubmix.com/cn/update/News#release-2026-08-13",en:"https://docs.aihubmix.com/en/update/News#release-2026-08-13"},title:{zh:"deepseek-v4-pro-0813 上线",en:"deepseek-v4-pro-0813 now available"},body:{zh:"新增 deepseek-v4-pro-0813：DeepSeek 高性能通用推理与 Agent 模型，适合复杂推理、编程、长文档分析和 Agent 工作流。支持思考与非思考模式、100 万 tokens 上下文窗口、最长 384K 输出、工具调用和 Responses API。",en:"Added deepseek-v4-pro-0813: DeepSeek’s high-performance general-purpose reasoning and agent model for complex reasoning, coding, long-document analysis, and agentic workflows. It supports thinking and non-thinking modes, a 1M-token context window, up to 384K output, tool calling, and the Responses API."},blocks:{zh:[{tag:"li",children:[{tag:"text",text:"新增 "},{tag:"a",href:"https://aihubmix.com/model/deepseek-v4-pro-0813",children:[{tag:"code",children:[{tag:"text",text:"deepseek-v4-pro-0813"}]}]},{tag:"text",text:"：DeepSeek 高性能通用推理与 Agent 模型，适合复杂推理、编程、长文档分析和 Agent 工作流。支持思考与非思考模式、100 万 tokens 上下文窗口、最长 384K 输出、工具调用和 Responses API。"}]}],en:[{tag:"li",children:[{tag:"text",text:"Added "},{tag:"a",href:"https://aihubmix.com/model/deepseek-v4-pro-0813",children:[{tag:"code",children:[{tag:"text",text:"deepseek-v4-pro-0813"}]}]},{tag:"text",text:": DeepSeek’s high-performance general-purpose reasoning and agent model for complex reasoning, coding, long-document analysis, and agentic workflows. It supports thinking and non-thinking modes, a 1M-token context window, up to 384K output, tool calling, and the Responses API."}]}]}},{id:"models-2026-08-12-0-0",category:"models",kind:"launch",publishedAt:"2026-08-12T00:00:00+08:00",models:["agnes-2.5-flash","agnes-2.5-pro","agnes-2.5-pro-alpha","agnes-image-2.1-flash"],model:"agnes-2.5-flash",source:{zh:"https://docs.aihubmix.com/cn/update/News#release-2026-08-12",en:"https://docs.aihubmix.com/en/update/News#release-2026-08-12"},title:{zh:"agnes-2.5-flash、agnes-2.5-pro、agnes-2.5-pro-alpha、agnes-image-2.1-flash 上线",en:"agnes-2.5-flash, agnes-2.5-pro, agnes-2.5-pro-alpha, agnes-image-2.1-flash now available"},body:{zh:"新增 agnes-2.5-flash、agnes-2.5-pro、agnes-2.5-pro-alpha 与 agnes-image-2.1-flash。文本与图像类 Agnes 模型现已可通过 AIHubMix 统一入口调用。",en:"Added agnes-2.5-flash, agnes-2.5-pro, agnes-2.5-pro-alpha, and agnes-image-2.1-flash. Text and image Agnes models are now available through the unified AIHubMix entrypoint."},blocks:{zh:[{tag:"li",children:[{tag:"text",text:"新增 "},{tag:"a",href:"https://aihubmix.com/model/agnes-2.5-flash",children:[{tag:"code",children:[{tag:"text",text:"agnes-2.5-flash"}]}]},{tag:"text",text:"、"},{tag:"a",href:"https://aihubmix.com/model/agnes-2.5-pro",children:[{tag:"code",children:[{tag:"text",text:"agnes-2.5-pro"}]}]},{tag:"text",text:"、"},{tag:"a",href:"https://aihubmix.com/model/agnes-2.5-pro-alpha",children:[{tag:"code",children:[{tag:"text",text:"agnes-2.5-pro-alpha"}]}]},{tag:"text",text:" 与 "},{tag:"a",href:"https://aihubmix.com/model/agnes-image-2.1-flash",children:[{tag:"code",children:[{tag:"text",text:"agnes-image-2.1-flash"}]}]},{tag:"text",text:"。文本与图像类 Agnes 模型现已可通过 AIHubMix 统一入口调用。"}]}],en:[{tag:"li",children:[{tag:"text",text:"Added "},{tag:"a",href:"https://aihubmix.com/model/agnes-2.5-flash",children:[{tag:"code",children:[{tag:"text",text:"agnes-2.5-flash"}]}]},{tag:"text",text:", "},{tag:"a",href:"https://aihubmix.com/model/agnes-2.5-pro",children:[{tag:"code",children:[{tag:"text",text:"agnes-2.5-pro"}]}]},{tag:"text",text:", "},{tag:"a",href:"https://aihubmix.com/model/agnes-2.5-pro-alpha",children:[{tag:"code",children:[{tag:"text",text:"agnes-2.5-pro-alpha"}]}]},{tag:"text",text:", and "},{tag:"a",href:"https://aihubmix.com/model/agnes-image-2.1-flash",children:[{tag:"code",children:[{tag:"text",text:"agnes-image-2.1-flash"}]}]},{tag:"text",text:". Text and image Agnes models are now available through the unified AIHubMix entrypoint."}]}]}},{id:"release-2026-08-12",category:"changelog",kind:"release",publishedAt:"2026-08-12T00:00:00+08:00",title:{zh:"视频任务、工具调用与供应商信息优化",en:"Video tasks, tool calls and provider information"},sourceTitle:{zh:"Agnes 模型扩容与视频、工具兼容优化",en:"Agnes: Models and media improvements"},releaseLabel:"Release 2026.08.12",source:{zh:"https://docs.aihubmix.com/cn/update/News#release-2026-08-12",en:"https://docs.aihubmix.com/en/update/News#release-2026-08-12"},sections:[{heading:{zh:"Veo 视频时长返回更准确",en:"More accurate Veo video duration"},blocks:{zh:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"Gemini Veo 任务在创建、轮询和结果读取时会更稳定地保留真实视频时长。使用自定义秒数生成视频的用户，看到的任务时长、下载文件时长和后续计费/展示口径会更一致。"}]},{tag:"text",text:`
`}]}],en:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"Gemini Veo tasks now preserve the real video duration more reliably across creation, polling, and result retrieval. Users generating videos with custom seconds should see task duration, downloaded file duration, and later billing/display records stay better aligned."}]},{tag:"text",text:`
`}]}]}},{heading:{zh:"Azure Responses 工具调用兼容性提升",en:"Improved Azure Responses tool-call compatibility"},blocks:{zh:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"通过 Azure Responses 使用 namespace 工具时，空白或缺失的 description 不再直接导致 Azure 在模型执行前返回 400。原本可在 OpenAI 官方接口通过的工具调用，更容易在 Azure 上保持一致行为。"}]},{tag:"text",text:`
`}]}],en:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"When using namespace tools through Azure Responses, blank or missing descriptions no longer cause Azure to reject the request with a 400 before model execution. Tool calls that already worked through the official OpenAI interface should behave more consistently on Azure."}]},{tag:"text",text:`
`}]}]}},{heading:{zh:"模型与供应商页面数据更完整",en:"More complete model and supplier page data"},blocks:{zh:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"code",children:[{tag:"text",text:"/call/devs"}]},{tag:"text",text:" 现在支持返回厂商官网字段，模型聚合页和供应商页面可在数据补齐后展示 Official site 入口。"},{tag:"code",children:[{tag:"text",text:"/providers/<slug>"}]},{tag:"text",text:" 厂商页也补齐了边缘缓存策略，页面响应会更稳定。"}]},{tag:"text",text:`
`}]}],en:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"code",children:[{tag:"text",text:"/call/devs"}]},{tag:"text",text:" now supports a supplier website field, so model aggregation and supplier pages can show an Official site entry once data is filled in. "},{tag:"code",children:[{tag:"text",text:"/providers/<slug>"}]},{tag:"text",text:" pages also now preserve edge-cache behavior for steadier page responses."}]},{tag:"text",text:`
`}]}]}}]},{id:"release-2026-08-11",category:"changelog",kind:"release",publishedAt:"2026-08-11T00:00:00+08:00",title:{zh:"异步交互与多协议兼容优化",en:"Async interactions and protocol compatibility"},sourceTitle:{zh:"Gemini 异步交互与多协议兼容优化",en:"Asynchronous tasks and protocol compatibility"},releaseLabel:"Release 2026.08.11",source:{zh:"https://docs.aihubmix.com/cn/update/News#release-2026-08-11",en:"https://docs.aihubmix.com/en/update/News#release-2026-08-11"},sections:[{heading:{zh:"Gemini interactions 支持异步任务",en:"Gemini interactions now supports async tasks"},blocks:{zh:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"Gemini interactions 请求现在可显式使用 "},{tag:"code",children:[{tag:"text",text:"background: true"}]},{tag:"text",text:" 走异步任务，并通过返回的任务 ID 查询状态、取消或清理任务。长耗时的多模态/omni 交互更适合接入，不必保持同步连接等待完成。"}]},{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"异步任务按真实用量结算，并补齐 token 分列、取消全额释放和长响应保护。用户控制台里的消费记录、余额预扣与最终扣费更一致。"}]},{tag:"text",text:`
`}]}],en:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"Gemini interactions requests can now explicitly use "},{tag:"code",children:[{tag:"text",text:"background: true"}]},{tag:"text",text:" for async tasks, then use the returned task ID to query status, cancel, or clean up the task. Long-running multimodal/omni interactions are easier to integrate without keeping a synchronous connection open until completion."}]},{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"Async tasks are settled against real usage, with token columns, full release on cancellation, and large-response safeguards filled in. Console usage records, balance pre-deduction, and final charges should stay more consistent."}]},{tag:"text",text:`
`}]}]}},{heading:{zh:"Claude Messages 桥接 thinking 兼容性提升",en:"Improved Claude Messages thinking compatibility"},blocks:{zh:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"通过 Messages 协议调用 OpenAI 兼容模型提供方时，模型返回的 thinking/reasoning 内容会被保留，并可在后续多轮中回传。使用 Claude SDK、tools 多轮和 DeepSeek thinking 模式的应用更不容易遇到空响应、400 或思维链上下文丢失。"}]},{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"无签名 thinking 块在切换到原生 Claude 服务时会被兼容处理，减少多模型 fallback 或桥接链路中的格式错误。"}]},{tag:"text",text:`
`}]}],en:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"When the Messages protocol calls OpenAI-compatible model providers, returned thinking/reasoning content is preserved and can be sent back in later turns. Apps using Claude SDKs, multi-turn tools, or DeepSeek thinking mode should see fewer empty responses, 400 errors, or lost reasoning context."}]},{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"Unsigned thinking blocks are handled more compatibly when traffic switches to native Claude service, reducing format errors in multi-model fallback or bridge flows."}]},{tag:"text",text:`
`}]}]}},{heading:{zh:"Doubao 视频素材提示词兼容性提升",en:"Improved Doubao video prompt and reference compatibility"},blocks:{zh:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"调用豆包视频模型时，"},{tag:"code",children:[{tag:"text",text:"prompt"}]},{tag:"text",text:" 与 "},{tag:"code",children:[{tag:"text",text:"extra_body.content"}]},{tag:"text",text:" 中的参考素材可以一起生效；仅素材内容的请求会保留提示词与素材顺序。"}]},{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"创建响应中的尺寸与提示词信息更贴近实际下发内容，并继续忽略暂未支持的原生字段，减少 SDK 额外参数导致的视频生成失败。"}]},{tag:"text",text:`
`}]}],en:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"When calling Doubao video models, "},{tag:"code",children:[{tag:"text",text:"prompt"}]},{tag:"text",text:" and reference assets in "},{tag:"code",children:[{tag:"text",text:"extra_body.content"}]},{tag:"text",text:" can now work together; requests that contain only assets keep both the prompt and the asset order."}]},{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"Create responses now echo size and prompt information closer to what is actually sent, while still ignoring unsupported native fields. This reduces video-generation failures caused by extra SDK parameters."}]},{tag:"text",text:`
`}]}]}}]},{id:"release-2026-08-10",category:"changelog",kind:"release",publishedAt:"2026-08-10T00:00:00+08:00",title:{zh:"媒体用量统计与请求转换修复",en:"Media usage accounting and request conversion fixes"},sourceTitle:{zh:"媒体累计用量与 Azure 模型名修复",en:"Usage billing and log accuracy"},releaseLabel:"Release 2026.08.10",source:{zh:"https://docs.aihubmix.com/cn/update/News#release-2026-08-10",en:"https://docs.aihubmix.com/en/update/News#release-2026-08-10"},sections:[{heading:{zh:"媒体生成累计用量统计更完整",en:"More complete cumulative usage for media generation"},blocks:{zh:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"媒体生成任务完成结算后，会同步补记账号累计已用额度。用户在控制台查看累计已用、余额扣减和消费记录时，媒体生成部分会更完整地反映真实用量。"}]},{tag:"text",text:`
`}]}],en:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"After a media-generation task is settled, account-level cumulative used quota is updated as well. In the console, cumulative usage, balance deduction, and usage records reflect real media-generation consumption more completely."}]},{tag:"text",text:`
`}]}]}},{heading:{zh:"Azure 点号模型名兼容性提升",en:"Improved Azure model-name compatibility"},blocks:{zh:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"调用 deployment/model 名中包含点号的 Azure 模型时，模型名在 Chat、Responses 和 Messages 转换链路中保持原样，不再被自动改写；既有 Azure 调用行为保持不变。"}]},{tag:"text",text:`
`}]}],en:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"When calling Azure models whose deployment/model name contains dots, the model name is preserved across the Chat, Responses, and Messages conversion paths and is no longer rewritten automatically. Existing Azure call behavior remains unchanged."}]},{tag:"text",text:`
`}]}]}}]},{id:"models-2026-08-08-0-0",category:"models",kind:"launch",publishedAt:"2026-08-08T00:00:00+08:00",models:["doubao-seedance-2-5-260628"],model:"doubao-seedance-2-5-260628",source:{zh:"https://docs.aihubmix.com/cn/update/News#release-2026-08-08",en:"https://docs.aihubmix.com/en/update/News#release-2026-08-08"},title:{zh:"doubao-seedance-2-5-260628 上线",en:"doubao-seedance-2-5-260628 now available"},body:{zh:"新增 doubao-seedance-2-5-260628：ByteDance Seed 新一代统一多模态音视频生成模型，可一次生成最长 30 秒的音视频同步内容，并支持多轮延展。相比 Seedance 2.0，它在叙事、转场、参考素材支持、真实感和精准编辑方面进一步提升，适合影视制作、广告、教育、仿真和长内容创作。",en:"Added doubao-seedance-2-5-260628: ByteDance Seed’s next-generation unified multimodal audio-video generation model. It creates up to 30 seconds of synchronized audio and video in one pass, supports multi-round extensions, and improves storytelling, transitions, reference support, realism, and precise editing for filmmaking, advertising, education, simulation, and long-form content."},blocks:{zh:[{tag:"li",children:[{tag:"text",text:"新增 "},{tag:"a",href:"https://aihubmix.com/model/doubao-seedance-2-5-260628",children:[{tag:"code",children:[{tag:"text",text:"doubao-seedance-2-5-260628"}]}]},{tag:"text",text:"：ByteDance Seed 新一代统一多模态音视频生成模型，可一次生成最长 30 秒的音视频同步内容，并支持多轮延展。相比 Seedance 2.0，它在叙事、转场、参考素材支持、真实感和精准编辑方面进一步提升，适合影视制作、广告、教育、仿真和长内容创作。"}]}],en:[{tag:"li",children:[{tag:"text",text:"Added "},{tag:"a",href:"https://aihubmix.com/model/doubao-seedance-2-5-260628",children:[{tag:"code",children:[{tag:"text",text:"doubao-seedance-2-5-260628"}]}]},{tag:"text",text:": ByteDance Seed’s next-generation unified multimodal audio-video generation model. It creates up to 30 seconds of synchronized audio and video in one pass, supports multi-round extensions, and improves storytelling, transitions, reference support, realism, and precise editing for filmmaking, advertising, education, simulation, and long-form content."}]}]}},{id:"models-2026-08-06-0-0",category:"models",kind:"launch",publishedAt:"2026-08-06T00:00:00+08:00",models:["wan3.0-video"],model:"wan3.0-video",source:{zh:"https://docs.aihubmix.com/cn/update/News#release-2026-08-06",en:"https://docs.aihubmix.com/en/update/News#release-2026-08-06"},title:{zh:"wan3.0-video 上线",en:"wan3.0-video now available"},body:{zh:"新增 wan3.0-video：阿里通义实验室推出的一体化视频生成与编辑模型，目前处于公测阶段。它支持最长 30 秒的原生视频生成、生产级角色一致性、逼真的画面与声音，以及统一的参考生成、编辑、复刻和动作驱动工作流，适合广告、电商、影视制作、角色动画、视频编辑和文档转视频。",en:"Added wan3.0-video: Alibaba Tongyi Lab’s all-in-one video generation and editing model, currently in public beta. It supports native videos up to 30 seconds, production-grade character consistency, lifelike visuals and sound, and unified reference, editing, replication, and motion-driving workflows for advertising, e-commerce, filmmaking, animation, video editing, and document-to-video creation."},blocks:{zh:[{tag:"li",children:[{tag:"text",text:"新增 "},{tag:"a",href:"https://aihubmix.com/model/wan3.0-video",children:[{tag:"code",children:[{tag:"text",text:"wan3.0-video"}]}]},{tag:"text",text:"：阿里通义实验室推出的一体化视频生成与编辑模型，目前处于公测阶段。它支持最长 30 秒的原生视频生成、生产级角色一致性、逼真的画面与声音，以及统一的参考生成、编辑、复刻和动作驱动工作流，适合广告、电商、影视制作、角色动画、视频编辑和文档转视频。"}]}],en:[{tag:"li",children:[{tag:"text",text:"Added "},{tag:"a",href:"https://aihubmix.com/model/wan3.0-video",children:[{tag:"code",children:[{tag:"text",text:"wan3.0-video"}]}]},{tag:"text",text:": Alibaba Tongyi Lab’s all-in-one video generation and editing model, currently in public beta. It supports native videos up to 30 seconds, production-grade character consistency, lifelike visuals and sound, and unified reference, editing, replication, and motion-driving workflows for advertising, e-commerce, filmmaking, animation, video editing, and document-to-video creation."}]}]}},{id:"models-2026-08-06-0-1",category:"models",kind:"launch",publishedAt:"2026-08-06T00:00:00+08:00",models:["qwen-image-3.0"],model:"qwen-image-3.0",source:{zh:"https://docs.aihubmix.com/cn/update/News#release-2026-08-06",en:"https://docs.aihubmix.com/en/update/News#release-2026-08-06"},title:{zh:"qwen-image-3.0 上线",en:"qwen-image-3.0 now available"},body:{zh:"新增 qwen-image-3.0：阿里云通义千问团队的图像生成与编辑模型，支持文生图、参考图创作和图像编辑。兼顾画质与速度，适合社交媒体、电商、创意设计、日常内容生产以及高频大规模创作。",en:"Added qwen-image-3.0: image generation and editing model from Alibaba Cloud’s Qwen team, supporting text-to-image, reference-based creation, and image editing. It balances image quality and speed for social media, e-commerce, creative design, everyday content production, and frequent large-scale creation."},blocks:{zh:[{tag:"li",children:[{tag:"text",text:"新增 "},{tag:"a",href:"https://aihubmix.com/model/qwen-image-3.0",children:[{tag:"code",children:[{tag:"text",text:"qwen-image-3.0"}]}]},{tag:"text",text:"：阿里云通义千问团队的图像生成与编辑模型，支持文生图、参考图创作和图像编辑。兼顾画质与速度，适合社交媒体、电商、创意设计、日常内容生产以及高频大规模创作。"}]}],en:[{tag:"li",children:[{tag:"text",text:"Added "},{tag:"a",href:"https://aihubmix.com/model/qwen-image-3.0",children:[{tag:"code",children:[{tag:"text",text:"qwen-image-3.0"}]}]},{tag:"text",text:": image generation and editing model from Alibaba Cloud’s Qwen team, supporting text-to-image, reference-based creation, and image editing. It balances image quality and speed for social media, e-commerce, creative design, everyday content production, and frequent large-scale creation."}]}]}},{id:"models-2026-08-06-0-2",category:"models",kind:"launch",publishedAt:"2026-08-06T00:00:00+08:00",models:["qwen-image-3.0-pro"],model:"qwen-image-3.0-pro",source:{zh:"https://docs.aihubmix.com/cn/update/News#release-2026-08-06",en:"https://docs.aihubmix.com/en/update/News#release-2026-08-06"},title:{zh:"qwen-image-3.0-pro 上线",en:"qwen-image-3.0-pro now available"},body:{zh:"新增 qwen-image-3.0-pro：阿里云通义千问旗舰级图像生成与编辑模型，面向广告、品牌视觉、UI、演示、产品图和专业设计场景。擅长复杂版式、精准的中英文字渲染、真实材质、参考图编辑，以及细节、构图和商业级视觉品质。",en:"Added qwen-image-3.0-pro: Alibaba Cloud Qwen’s flagship image generation and editing model for advertising, brand visuals, UI, presentations, product imagery, and professional design. Strong at complex layouts, accurate Chinese and English text rendering, realistic materials, reference-based editing, detail, composition, and commercial-grade visual quality."},blocks:{zh:[{tag:"li",children:[{tag:"text",text:"新增 "},{tag:"a",href:"https://aihubmix.com/model/qwen-image-3.0-pro",children:[{tag:"code",children:[{tag:"text",text:"qwen-image-3.0-pro"}]}]},{tag:"text",text:"：阿里云通义千问旗舰级图像生成与编辑模型，面向广告、品牌视觉、UI、演示、产品图和专业设计场景。擅长复杂版式、精准的中英文字渲染、真实材质、参考图编辑，以及细节、构图和商业级视觉品质。"}]}],en:[{tag:"li",children:[{tag:"text",text:"Added "},{tag:"a",href:"https://aihubmix.com/model/qwen-image-3.0-pro",children:[{tag:"code",children:[{tag:"text",text:"qwen-image-3.0-pro"}]}]},{tag:"text",text:": Alibaba Cloud Qwen’s flagship image generation and editing model for advertising, brand visuals, UI, presentations, product imagery, and professional design. Strong at complex layouts, accurate Chinese and English text rendering, realistic materials, reference-based editing, detail, composition, and commercial-grade visual quality."}]}]}},{id:"release-2026-08-04",category:"changelog",kind:"release",publishedAt:"2026-08-04T00:00:00+08:00",title:{zh:"多模态向量接口与视频调用优化",en:"Multimodal embeddings and video API improvements"},sourceTitle:{zh:"多模态 Embedding 与 Seedance 调用优化",en:"Gemini / Seedance: Multimodal embeddings and video compatibility"},releaseLabel:"Release 2026.08.04",source:{zh:"https://docs.aihubmix.com/cn/update/News#release-2026-08-04",en:"https://docs.aihubmix.com/en/update/News#release-2026-08-04"},sections:[{heading:{zh:"Gemini embedding 支持多模态输入与分模态计费",en:"Gemini embedding supports multimodal input and per-modality billing"},blocks:{zh:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"code",children:[{tag:"text",text:"gemini-embedding-2-preview"}]},{tag:"text",text:" 现在可以通过 OpenAI 兼容 embeddings 入口和 Gemini 原生入口处理文本、图片、音频、视频和文档输入。使用多模态向量检索、内容理解或知识库构建的用户，可在同一模型上接入更多输入类型。"}]},{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"Gemini embedding 的费用会优先按模型推理厂商返回的分模态 token 用量计算；图片、音频、视频和文档不再被统一当作文本估算。多模态 embedding 账单更贴近真实用量。"}]},{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"单条 embedding 请求可直接使用新一代 Gemini embedding；包含多条输入的批量请求会明确返回能力不支持的提示。"}]},{tag:"text",text:`
`}]}],en:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"code",children:[{tag:"text",text:"gemini-embedding-2-preview"}]},{tag:"text",text:" can now handle text, image, audio, video, and document inputs through both the OpenAI-compatible embeddings endpoint and the native Gemini path. Users building multimodal retrieval, content understanding, or knowledge-base workflows can use more input types on the same model."}]},{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"Gemini embedding charges now prioritize the per-modality token usage returned by the model provider. Image, audio, video, and document inputs are no longer treated as a single text estimate, so multimodal embedding bills should better match actual usage."}]},{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"Single-input embedding requests can call the newer Gemini embedding path directly. Multi-input batch requests now return a clear capability-mismatch response."}]},{tag:"text",text:`
`}]}]}},{heading:{zh:"Seedance 2.0 视频生成调用更稳定",en:"More reliable Seedance 2.0 video-generation calls"},blocks:{zh:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"Seedance 2.0 的图片、音频、视频等引用会按媒体类型更准确地映射，"},{tag:"code",children:[{tag:"text",text:"adaptive"}]},{tag:"text",text:"、4K、尺寸等能力约束更清晰。视频生成工作流中因参数映射不准导致的失败会减少。"}]},{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"自适应时长请求会先按 15 秒预扣，并在任务完成后按真实时长结算；用户余额预扣和最终账单更一致。"}]},{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"媒体生成失败时，接口会返回脱敏后的错误信息，方便用户和客服定位参数、签名链接或模型推理厂商限制问题，同时避免暴露敏感凭证。"}]},{tag:"text",text:`
`}]}],en:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"Seedance 2.0 references for images, audio, and video are mapped more accurately by media type, with clearer constraints for "},{tag:"code",children:[{tag:"text",text:"adaptive"}]},{tag:"text",text:", 4K, and size options. Video-generation workflows should see fewer failures caused by parameter mapping mismatches."}]},{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"Adaptive-duration requests are pre-deducted at 15 seconds and settled against the actual duration after the task finishes, keeping balance pre-deduction and final billing more consistent."}]},{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"When media generation fails, APIs now return sanitized error details, making it easier for users and support teams to diagnose parameter, signed-URL, or model-provider limit issues without exposing sensitive credentials."}]},{tag:"text",text:`
`}]}]}}]},{id:"models-2026-08-03-0-0",category:"models",kind:"launch",publishedAt:"2026-08-03T00:00:00+08:00",models:["qwen3.8-max"],model:"qwen3.8-max",source:{zh:"https://docs.aihubmix.com/cn/update/News#release-2026-08-03",en:"https://docs.aihubmix.com/en/update/News#release-2026-08-03"},title:{zh:"qwen3.8-max 上线",en:"qwen3.8-max now available"},body:{zh:"新增 qwen3.8-max：阿里云旗舰原生视觉语言模型，基于 2.4 万亿参数的 Mixture-of-Experts (MoE) 架构，最大上下文窗口达 100 万 tokens。适合复杂多模态理解、高阶推理、软件开发、agent 工作流以及长上下文处理。与 Qwen3.7-Max 价格相近，在推理、编码和 agent 能力上均有明显提升，整体表现对齐当前主流领先模型。",en:"Added qwen3.8-max: Alibaba Cloud’s flagship native vision-language model, built on a 2.4-trillion-parameter Mixture-of-Experts (MoE) architecture and supporting context windows of up to 1 million tokens. It is well suited for complex multimodal understanding, advanced reasoning, software development, agentic workflows, and long-context processing. At a similar price to Qwen3.7-Max, Qwen3.8-Max delivers significant improvements in reasoning, coding, and agent capabilities, with overall performance comparable to today’s leading models."},blocks:{zh:[{tag:"li",children:[{tag:"text",text:"新增 "},{tag:"a",href:"https://aihubmix.com/model/qwen3.8-max",children:[{tag:"code",children:[{tag:"text",text:"qwen3.8-max"}]}]},{tag:"text",text:"：阿里云旗舰原生视觉语言模型，基于 2.4 万亿参数的 Mixture-of-Experts (MoE) 架构，最大上下文窗口达 100 万 tokens。适合复杂多模态理解、高阶推理、软件开发、agent 工作流以及长上下文处理。与 Qwen3.7-Max 价格相近，在推理、编码和 agent 能力上均有明显提升，整体表现对齐当前主流领先模型。"}]}],en:[{tag:"li",children:[{tag:"text",text:"Added "},{tag:"a",href:"https://aihubmix.com/model/qwen3.8-max",children:[{tag:"code",children:[{tag:"text",text:"qwen3.8-max"}]}]},{tag:"text",text:": Alibaba Cloud’s flagship native vision-language model, built on a 2.4-trillion-parameter Mixture-of-Experts (MoE) architecture and supporting context windows of up to 1 million tokens. It is well suited for complex multimodal understanding, advanced reasoning, software development, agentic workflows, and long-context processing. At a similar price to Qwen3.7-Max, Qwen3.8-Max delivers significant improvements in reasoning, coding, and agent capabilities, with overall performance comparable to today’s leading models."}]}]}},{id:"release-2026-08-03",category:"changelog",kind:"release",publishedAt:"2026-08-03T00:00:00+08:00",title:{zh:"详情页供应商可用性展示优化",en:"Provider availability in model details"},sourceTitle:{zh:"Qwen 3.8 Max 与供应商展示优化",en:"Qwen 3.8 Max: Models and provider availability"},releaseLabel:"Release 2026.08.03",source:{zh:"https://docs.aihubmix.com/cn/update/News#release-2026-08-03",en:"https://docs.aihubmix.com/en/update/News#release-2026-08-03"},sections:[{heading:{zh:"模型详情中的供应商可用性更清晰",en:"Clearer supplier availability in model details"},blocks:{zh:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"模型详情会优先展示当前更适合调用的供应商，并把仅作备用或暂不可用的供应商放到后面。用户选择模型供应商、客服排查模型可用性时，可以更直观看到哪些供应商当前更适合使用，减少因展示顺序过期带来的误判。"}]},{tag:"text",text:`
`}]}],en:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"Model details now show suppliers that are better suited for current requests first, while backup-only or temporarily unavailable suppliers appear later. When choosing a model supplier or troubleshooting availability, users and support teams can more quickly see which suppliers are currently the better choice and avoid decisions based on stale ordering."}]},{tag:"text",text:`
`}]}]}}]},{id:"release-2026-08-02",category:"changelog",kind:"release",publishedAt:"2026-08-02T00:00:00+08:00",title:{zh:"小额请求计费与消费日志修复",en:"Small-request billing and usage log fixes"},sourceTitle:{zh:"小额请求计费与消费日志修复",en:"Usage billing and log accuracy"},releaseLabel:"Release 2026.08.02",source:{zh:"https://docs.aihubmix.com/cn/update/News#release-2026-08-02",en:"https://docs.aihubmix.com/en/update/News#release-2026-08-02"},sections:[{heading:{zh:"小额账单与消费日志更准确",en:"More accurate small-request billing and usage logs"},blocks:{zh:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"by-bill 计费模型的小额请求不再因为取整归零而缺少扣费和消费日志。受影响的请求会按最小有效额度入账，用户账单、余额预扣和消费记录更一致；客服和运营排查小额 embedding 等高频请求时，可以看到更完整的记录。"}]},{tag:"text",text:`
`}]}],en:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"Small requests on by-bill models no longer disappear from billing and usage logs because the calculated amount rounded down to zero. Affected requests are now charged at the minimum effective quota, keeping invoices, balance pre-deduction, and usage records aligned; support and operations teams should see more complete records for high-frequency embedding-style traffic."}]},{tag:"text",text:`
`}]}]}}]},{id:"models-2026-07-31-0-0",category:"models",kind:"launch",publishedAt:"2026-07-31T00:00:00+08:00",models:["deepseek-v4-flash"],model:"deepseek-v4-flash",source:{zh:"https://docs.aihubmix.com/cn/update/News#release-2026-07-31",en:"https://docs.aihubmix.com/en/update/News#release-2026-07-31"},title:{zh:"deepseek-v4-flash 上线",en:"deepseek-v4-flash now available"},body:{zh:"新增 deepseek-v4-flash：面向效率优化的 Mixture-of-Experts 模型，总参数 284B，激活参数 13B，上下文窗口 100 万 tokens。为快速推理和高吞吐场景设计，同时保留强劲的推理与编码能力，适合编码助手、聊天系统和 agent 工作流。",en:"Added deepseek-v4-flash: an efficiency-optimized Mixture-of-Experts model with 284B total parameters, 13B active parameters, and a 1M-token context window. It is designed for fast inference and high-throughput workloads while retaining strong reasoning and coding performance, making it suitable for coding assistants, chat systems, and agentic workflows."},blocks:{zh:[{tag:"li",children:[{tag:"text",text:"新增 "},{tag:"a",href:"https://aihubmix.com/model/deepseek-v4-flash",children:[{tag:"code",children:[{tag:"text",text:"deepseek-v4-flash"}]}]},{tag:"text",text:"：面向效率优化的 Mixture-of-Experts 模型，总参数 284B，激活参数 13B，上下文窗口 100 万 tokens。为快速推理和高吞吐场景设计，同时保留强劲的推理与编码能力，适合编码助手、聊天系统和 agent 工作流。"}]}],en:[{tag:"li",children:[{tag:"text",text:"Added "},{tag:"a",href:"https://aihubmix.com/model/deepseek-v4-flash",children:[{tag:"code",children:[{tag:"text",text:"deepseek-v4-flash"}]}]},{tag:"text",text:": an efficiency-optimized Mixture-of-Experts model with 284B total parameters, 13B active parameters, and a 1M-token context window. It is designed for fast inference and high-throughput workloads while retaining strong reasoning and coding performance, making it suitable for coding assistants, chat systems, and agentic workflows."}]}]}},{id:"models-2026-07-31-1-0",category:"models",kind:"launch",publishedAt:"2026-07-31T00:00:00+08:00",models:["minimax-h3"],model:"minimax-h3",source:{zh:"https://docs.aihubmix.com/cn/update/News#release-2026-07-31",en:"https://docs.aihubmix.com/en/update/News#release-2026-07-31"},title:{zh:"minimax-h3 上线",en:"minimax-h3 now available"},body:{zh:"新增 minimax-h3：MiniMax 的通用多模态视频模型，支持文本、图像、音频和视频输入，可用于文生视频、图生视频、首尾帧生成、参考素材生成和视频编辑。需要搭建视频创作、广告素材、产品演示或多模态内容工作流的用户，可以通过 AIHubMix 统一接口接入。",en:"Added minimax-h3: MiniMax’s general-purpose multimodal video model. It accepts text, image, audio, and video inputs, and supports text-to-video, image-to-video, first/last-frame generation, reference-based generation, and video editing. Users building video creation, ad creative, product demo, or multimodal content workflows can call it through the AIHubMix unified API."},blocks:{zh:[{tag:"li",children:[{tag:"text",text:"新增 "},{tag:"a",href:"https://aihubmix.com/model/minimax-h3",children:[{tag:"code",children:[{tag:"text",text:"minimax-h3"}]}]},{tag:"text",text:"：MiniMax 的通用多模态视频模型，支持文本、图像、音频和视频输入，可用于文生视频、图生视频、首尾帧生成、参考素材生成和视频编辑。需要搭建视频创作、广告素材、产品演示或多模态内容工作流的用户，可以通过 AIHubMix 统一接口接入。"}]}],en:[{tag:"li",children:[{tag:"text",text:"Added "},{tag:"a",href:"https://aihubmix.com/model/minimax-h3",children:[{tag:"code",children:[{tag:"text",text:"minimax-h3"}]}]},{tag:"text",text:": MiniMax’s general-purpose multimodal video model. It accepts text, image, audio, and video inputs, and supports text-to-video, image-to-video, first/last-frame generation, reference-based generation, and video editing. Users building video creation, ad creative, product demo, or multimodal content workflows can call it through the AIHubMix unified API."}]}]}},{id:"models-2026-07-31-2-0",category:"models",kind:"price",publishedAt:"2026-07-31T00:00:00+08:00",models:["glm-5.2"],model:"glm-5.2",source:{zh:"https://docs.aihubmix.com/cn/update/News#release-2026-07-31",en:"https://docs.aihubmix.com/en/update/News#release-2026-07-31"},title:{zh:"GLM-5.2 分时段限时特惠",en:"GLM-5.2 limited-time time-of-day offer"},body:{zh:"glm-5.2 调用价格按每日时段打折，时间以 UTC 为准：每日 14:00 至 24:00 享 5 折，每日 00:00 至 14:00 享 7 折。活动限时。",en:"glm-5.2 is discounted by daily time window, in UTC: 50% off from 14:00 to 24:00 and 30% off from 00:00 to 14:00 each day. Limited-time offer."},blocks:{zh:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"code",children:[{tag:"text",text:"glm-5.2"}]},{tag:"text",text:" 调用价格按每日时段打折，时间以 UTC 为准：每日 14:00 至 24:00 享 5 折，每日 00:00 至 14:00 享 7 折。活动限时。"}]},{tag:"text",text:`
`}]}],en:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"code",children:[{tag:"text",text:"glm-5.2"}]},{tag:"text",text:" is discounted by daily time window, in UTC: 50% off from 14:00 to 24:00 and 30% off from 00:00 to 14:00 each day. Limited-time offer."}]},{tag:"text",text:`
`}]}]}},{id:"models-2026-07-31-3-0",category:"models",kind:"price",publishedAt:"2026-07-31T00:00:00+08:00",models:["gpt-5.6-luna","gpt-5.6-terra"],model:"gpt-5.6-luna",source:{zh:"https://docs.aihubmix.com/cn/update/News#release-2026-07-31",en:"https://docs.aihubmix.com/en/update/News#release-2026-07-31"},title:{zh:"GPT-5.6 Luna/Terra 价格下调",en:"GPT-5.6 Luna/Terra price reductions"},body:{zh:"gpt-5.6-luna 已同步官方降价 80%，当前价格为输入 $0.20、输出 $1.20；gpt-5.6-terra 已同步官方降价 20%，当前价格为输入 $2.00、输出 $12.00。对这两个模型有稳定调用量的用户，可重新评估成本敏感和通用任务的模型选择。",en:"gpt-5.6-luna has synced the official 80% price cut and is now priced at $0.20 input and $1.20 output; gpt-5.6-terra has synced the official 20% price cut and is now priced at $2.00 input and $12.00 output. Users with steady traffic on these models can re-evaluate model choices for cost-sensitive and general workloads."},blocks:{zh:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"code",children:[{tag:"text",text:"gpt-5.6-luna"}]},{tag:"text",text:" 已同步官方降价 80%，当前价格为输入 "},{tag:"text",text:"$0.20、输出 $"},{tag:"text",text:"1.20；"},{tag:"code",children:[{tag:"text",text:"gpt-5.6-terra"}]},{tag:"text",text:" 已同步官方降价 20%，当前价格为输入 "},{tag:"text",text:"$2.00、输出 $"},{tag:"text",text:"12.00。对这两个模型有稳定调用量的用户，可重新评估成本敏感和通用任务的模型选择。"}]},{tag:"text",text:`
`}]}],en:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"code",children:[{tag:"text",text:"gpt-5.6-luna"}]},{tag:"text",text:" has synced the official 80% price cut and is now priced at "},{tag:"text",text:"$0.20 input and $"},{tag:"text",text:"1.20 output; "},{tag:"code",children:[{tag:"text",text:"gpt-5.6-terra"}]},{tag:"text",text:" has synced the official 20% price cut and is now priced at "},{tag:"text",text:"$2.00 input and $"},{tag:"text",text:"12.00 output. Users with steady traffic on these models can re-evaluate model choices for cost-sensitive and general workloads."}]},{tag:"text",text:`
`}]}]}},{id:"release-2026-07-30",category:"changelog",kind:"release",publishedAt:"2026-07-30T00:00:00+08:00",title:{zh:"官方 MCP Server 开放接入",en:"Official MCP Server access"},sourceTitle:{zh:"官方 MCP 服务开放接入",en:"AIHubMix: Official MCP service"},releaseLabel:"Release 2026.07.30",source:{zh:"https://docs.aihubmix.com/cn/update/News#release-2026-07-30",en:"https://docs.aihubmix.com/en/update/News#release-2026-07-30"},sections:[{heading:{zh:"AIHubMix 官方 MCP Server 上线",en:"AIHubMix official MCP Server is available"},blocks:{zh:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"新增面向 MCP 客户端的官方托管入口 "},{tag:"a",href:"https://mcp.aihubmix.com/mcp",children:[{tag:"text",text:"mcp.aihubmix.com/mcp"}]},{tag:"text",text:" 或 "},{tag:"a",href:"https://mcp.inferera.com/mcp",children:[{tag:"text",text:"mcp.inferera.com/mcp"}]},{tag:"text",text:"。支持在 Claude Code、Codex、Cursor 等工具中通过同一入口查询模型与价格、检索文档、查看余额，并调用聊天、图像生成和视频生成等工具能力。"}]},{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"MCP 请求凭证由客户端逐次传入，服务端不保存 API Key；适合希望在 IDE 或 Agent 工具里直接使用 AIHubMix 模型和媒体能力的开发者。"}]},{tag:"text",text:`
`}]}],en:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"Added the official hosted MCP endpoint "},{tag:"a",href:"https://mcp.aihubmix.com/mcp",children:[{tag:"text",text:"mcp.aihubmix.com/mcp"}]},{tag:"text",text:" or "},{tag:"a",href:"https://mcp.inferera.com/mcp",children:[{tag:"text",text:"mcp.inferera.com/mcp"}]},{tag:"text",text:" for MCP-capable clients. Developers can use one entrypoint from tools such as Claude Code, Codex, and Cursor to query models and pricing, search docs, check credits, and call chat, image-generation, and video-generation tools."}]},{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"MCP credentials are passed by the client on each request and are not stored by the server, making the endpoint suitable for developers who want direct AIHubMix model and media access inside IDEs or agent tools."}]},{tag:"text",text:`
`}]}]}}]},{id:"models-2026-07-29-0-0",category:"models",kind:"launch",publishedAt:"2026-07-29T00:00:00+08:00",models:["jina-reranker-v3.5"],model:"jina-reranker-v3.5",source:{zh:"https://docs.aihubmix.com/cn/update/News#release-2026-07-29",en:"https://docs.aihubmix.com/en/update/News#release-2026-07-29"},title:{zh:"jina-reranker-v3.5 上线",en:"jina-reranker-v3.5 now available"},body:{zh:"新增 jina-reranker-v3.5：Jina AI 的 0.6B 多语言 listwise 文档重排模型，可作为 jina-reranker-v3 的同接口升级版本。它面向 RAG、搜索和结构化资料排序场景，支持长上下文候选排序，并提升领域鲁棒性、多语言检索、结构化数据排序和推理效率。",en:"Added jina-reranker-v3.5: Jina AI’s 0.6B multilingual listwise document reranker and a same-schema upgrade from jina-reranker-v3. It targets RAG, search, and structured-data ranking workflows, supports long-context candidate ranking, and improves domain robustness, multilingual retrieval, structured ranking, and inference efficiency."},blocks:{zh:[{tag:"li",children:[{tag:"text",text:"新增 "},{tag:"a",href:"https://aihubmix.com/model/jina-reranker-v3.5",children:[{tag:"code",children:[{tag:"text",text:"jina-reranker-v3.5"}]}]},{tag:"text",text:"：Jina AI 的 0.6B 多语言 listwise 文档重排模型，可作为 "},{tag:"code",children:[{tag:"text",text:"jina-reranker-v3"}]},{tag:"text",text:" 的同接口升级版本。它面向 RAG、搜索和结构化资料排序场景，支持长上下文候选排序，并提升领域鲁棒性、多语言检索、结构化数据排序和推理效率。"}]}],en:[{tag:"li",children:[{tag:"text",text:"Added "},{tag:"a",href:"https://aihubmix.com/model/jina-reranker-v3.5",children:[{tag:"code",children:[{tag:"text",text:"jina-reranker-v3.5"}]}]},{tag:"text",text:": Jina AI’s 0.6B multilingual listwise document reranker and a same-schema upgrade from "},{tag:"code",children:[{tag:"text",text:"jina-reranker-v3"}]},{tag:"text",text:". It targets RAG, search, and structured-data ranking workflows, supports long-context candidate ranking, and improves domain robustness, multilingual retrieval, structured ranking, and inference efficiency."}]}]}},{id:"release-2026-07-28",category:"changelog",kind:"release",publishedAt:"2026-07-28T00:00:00+08:00",title:{zh:"缓存计费、媒体任务与数据刷新优化",en:"Cache billing, media tasks and data freshness"},sourceTitle:{zh:"缓存计费、媒体任务与数据刷新优化",en:"Caching, media tasks and data refresh"},releaseLabel:"Release 2026.07.28",source:{zh:"https://docs.aihubmix.com/cn/update/News#release-2026-07-28",en:"https://docs.aihubmix.com/en/update/News#release-2026-07-28"},sections:[{heading:{zh:"缓存计费更准确",en:"More accurate cache billing"},blocks:{zh:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"对显式缓存与隐式缓存请求，平台会更一致地识别缓存读写用量，并按实际返回的缓存类型优先计费。使用 Qwen、Claude 等支持缓存能力的用户，可获得更贴近真实用量的账单明细。"}]},{tag:"text",text:`
`}]}],en:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"For explicit and implicit cache requests, AIHubMix now identifies cache read/write usage more consistently and prioritizes the cache type returned by the model provider. Users of cache-capable models such as Qwen and Claude should see billing details that better match actual usage."}]},{tag:"text",text:`
`}]}]}},{heading:{zh:"媒体生成与任务结果稳定性提升",en:"More reliable media generation and task results"},blocks:{zh:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"媒体生成首批模型列表收窄到已验证模型，并修复部分图像模型经媒体入口调用时可能 404 的问题。控制台中的 LLM 任务下载也补齐了文本存档场景，历史大结果更容易下载和交付。"}]},{tag:"text",text:`
`}]}],en:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"The first media-generation model list has been narrowed to verified models, and an issue that could return 404 for some image-model calls through the media endpoint has been fixed. Console downloads for LLM tasks now also cover text archives, making large historical results easier to retrieve and deliver."}]},{tag:"text",text:`
`}]}]}},{heading:{zh:"站点与模型数据刷新更可靠",en:"Fresher website and model data"},blocks:{zh:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"官网、模型数据、博客数据和站点地图的缓存策略已调整为发布后更快生效；模型广场与文档入口在内容更新后更不容易被旧缓存影响。"}]},{tag:"text",text:`
`}]}],en:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"Cache behavior for the website, model data, blog data, and sitemaps has been adjusted so releases take effect more reliably. Model pages and documentation entrypoints are less likely to be affected by stale cached data after updates."}]},{tag:"text",text:`
`}]}]}}]},{id:"release-2026-07-27",category:"changelog",kind:"release",publishedAt:"2026-07-27T00:00:00+08:00",title:{zh:"统一异步媒体生成接口上线",en:"Unified async media generation API"},sourceTitle:{zh:"统一异步媒体生成接口上线",en:"Unified asynchronous media API"},releaseLabel:"Release 2026.07.27",source:{zh:"https://docs.aihubmix.com/cn/update/News#release-2026-07-27",en:"https://docs.aihubmix.com/en/update/News#release-2026-07-27"},sections:[{heading:{zh:"媒体生成接口上线",en:"Media generation APIs are available"},blocks:{zh:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"新增 "},{tag:"code",children:[{tag:"text",text:"/ai/v1/images/generations"}]},{tag:"text",text:" 与 "},{tag:"code",children:[{tag:"text",text:"/ai/v1/images/edits"}]},{tag:"text",text:" 媒体生成入口，并提供统一的异步任务、结果查询、产物下载和 webhook 回调能力。图像与视频类工作流可以通过同一套任务生命周期接入，便于客户端跟踪长耗时生成结果。"}]},{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"生成产物会通过 AIHubMix 统一任务结果返回，并支持受限次数的下载与批量获取。运营和客服可重点关注首批用户的任务状态、下载链接有效性和计费明细反馈。"}]},{tag:"text",text:`
`}]}],en:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"Added "},{tag:"code",children:[{tag:"text",text:"/ai/v1/images/generations"}]},{tag:"text",text:" and "},{tag:"code",children:[{tag:"text",text:"/ai/v1/images/edits"}]},{tag:"text",text:" media-generation entrypoints with unified async tasks, result queries, artifact downloads, and webhook callbacks. Image and video workflows can now integrate through the same task lifecycle, making long-running generation results easier to track."}]},{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"Generated artifacts are returned through AIHubMix task results and support limited downloads and bulk retrieval. Customer-facing teams should watch early feedback on task status, download-link validity, and billing details."}]},{tag:"text",text:`
`}]}]}}]},{id:"models-2026-07-25-0-0",category:"models",kind:"launch",publishedAt:"2026-07-25T00:00:00+08:00",models:["claude-opus-5"],model:"claude-opus-5",source:{zh:"https://docs.aihubmix.com/cn/update/News#release-2026-07-25",en:"https://docs.aihubmix.com/en/update/News#release-2026-07-25"},title:{zh:"claude-opus-5 上线",en:"claude-opus-5 now available"},body:{zh:"新增 claude-opus-5：Anthropic 旗舰模型，面向高强度推理、编码和长程 agent 任务。擅长端到端软件开发、代码审查与缺陷发现、图表与文档视觉分析、复杂办公交付以及并行子代理协作，具备 1M 上下文窗口，最大输出 128K。",en:"Added claude-opus-5: Anthropic’s flagship model for demanding reasoning, coding, and long-horizon agentic work. It excels at end-to-end software tasks, code review and bug finding, chart and document analysis, complex office deliverables, and parallel subagent coordination, with a 1M context window and up to 128K output."},blocks:{zh:[{tag:"li",children:[{tag:"text",text:"新增 "},{tag:"a",href:"https://aihubmix.com/model/claude-opus-5",children:[{tag:"code",children:[{tag:"text",text:"claude-opus-5"}]}]},{tag:"text",text:"：Anthropic 旗舰模型，面向高强度推理、编码和长程 agent 任务。擅长端到端软件开发、代码审查与缺陷发现、图表与文档视觉分析、复杂办公交付以及并行子代理协作，具备 1M 上下文窗口，最大输出 128K。"}]}],en:[{tag:"li",children:[{tag:"text",text:"Added "},{tag:"a",href:"https://aihubmix.com/model/claude-opus-5",children:[{tag:"code",children:[{tag:"text",text:"claude-opus-5"}]}]},{tag:"text",text:": Anthropic’s flagship model for demanding reasoning, coding, and long-horizon agentic work. It excels at end-to-end software tasks, code review and bug finding, chart and document analysis, complex office deliverables, and parallel subagent coordination, with a 1M context window and up to 128K output."}]}]}},{id:"models-2026-07-24-0-0",category:"models",kind:"launch",publishedAt:"2026-07-24T00:00:00+08:00",models:["mai-image-2.5-pro"],model:"mai-image-2.5-pro",source:{zh:"https://docs.aihubmix.com/cn/update/News#release-2026-07-24",en:"https://docs.aihubmix.com/en/update/News#release-2026-07-24"},title:{zh:"mai-image-2.5-pro 上线",en:"mai-image-2.5-pro now available"},body:{zh:"新增 mai-image-2.5-pro：微软旗舰级图像生成与编辑模型，具备高保真写实效果、精准的图内文字渲染以及强大的图像编辑能力，适合商业设计、产品摄影和专业创意工作流。",en:"Added mai-image-2.5-pro: Microsoft’s flagship image generation and editing model, featuring high-fidelity realism, precise in-image text rendering, and powerful editing for commercial design, product photography, and professional creative workflows."},blocks:{zh:[{tag:"li",children:[{tag:"text",text:"新增 "},{tag:"a",href:"https://aihubmix.com/model/mai-image-2.5-pro",children:[{tag:"code",children:[{tag:"text",text:"mai-image-2.5-pro"}]}]},{tag:"text",text:"：微软旗舰级图像生成与编辑模型，具备高保真写实效果、精准的图内文字渲染以及强大的图像编辑能力，适合商业设计、产品摄影和专业创意工作流。"}]}],en:[{tag:"li",children:[{tag:"text",text:"Added "},{tag:"a",href:"https://aihubmix.com/model/mai-image-2.5-pro",children:[{tag:"code",children:[{tag:"text",text:"mai-image-2.5-pro"}]}]},{tag:"text",text:": Microsoft’s flagship image generation and editing model, featuring high-fidelity realism, precise in-image text rendering, and powerful editing for commercial design, product photography, and professional creative workflows."}]}]}},{id:"models-2026-07-24-0-1",category:"models",kind:"launch",publishedAt:"2026-07-24T00:00:00+08:00",models:["qwen-audio-3.0-tts-flash"],model:"qwen-audio-3.0-tts-flash",source:{zh:"https://docs.aihubmix.com/cn/update/News#release-2026-07-24",en:"https://docs.aihubmix.com/en/update/News#release-2026-07-24"},title:{zh:"qwen-audio-3.0-tts-flash 上线",en:"qwen-audio-3.0-tts-flash now available"},body:{zh:"新增 qwen-audio-3.0-tts-flash：通义千问实时语音合成模型，支持更多低资源语言与中文方言，具备丰富的表现力控制，首包延迟低于 200 ms，适合语音助手、实时对话和智能客服。",en:"Added qwen-audio-3.0-tts-flash: Qwen’s real-time speech synthesis model with expanded language and Chinese dialect support, expressive voice control, and first-packet latency under 200 ms for voice assistants, real-time dialogue, and intelligent customer service."},blocks:{zh:[{tag:"li",children:[{tag:"text",text:"新增 "},{tag:"a",href:"https://aihubmix.com/model/qwen-audio-3.0-tts-flash",children:[{tag:"code",children:[{tag:"text",text:"qwen-audio-3.0-tts-flash"}]}]},{tag:"text",text:"：通义千问实时语音合成模型，支持更多低资源语言与中文方言，具备丰富的表现力控制，首包延迟低于 200 ms，适合语音助手、实时对话和智能客服。"}]}],en:[{tag:"li",children:[{tag:"text",text:"Added "},{tag:"a",href:"https://aihubmix.com/model/qwen-audio-3.0-tts-flash",children:[{tag:"code",children:[{tag:"text",text:"qwen-audio-3.0-tts-flash"}]}]},{tag:"text",text:": Qwen’s real-time speech synthesis model with expanded language and Chinese dialect support, expressive voice control, and first-packet latency under 200 ms for voice assistants, real-time dialogue, and intelligent customer service."}]}]}},{id:"models-2026-07-24-0-2",category:"models",kind:"launch",publishedAt:"2026-07-24T00:00:00+08:00",models:["qwen-audio-3.0-tts-plus"],model:"qwen-audio-3.0-tts-plus",source:{zh:"https://docs.aihubmix.com/cn/update/News#release-2026-07-24",en:"https://docs.aihubmix.com/en/update/News#release-2026-07-24"},title:{zh:"qwen-audio-3.0-tts-plus 上线",en:"qwen-audio-3.0-tts-plus now available"},body:{zh:"新增 qwen-audio-3.0-tts-plus：通义千问高品质语音合成模型，可精细控制情感、语气、角色、语速、音量与合成风格，并在噪声和混响环境下更稳定，适合内容创作、有声书、影视配音、品牌声音设计及高品质语音服务。",en:"Added qwen-audio-3.0-tts-plus: Qwen’s premium speech synthesis model with richer control of emotion, tone, character, speaking rate, volume, and style, designed for content creation, audiobooks, dubbing, brand voices, and other high-quality speech workflows."},blocks:{zh:[{tag:"li",children:[{tag:"text",text:"新增 "},{tag:"a",href:"https://aihubmix.com/model/qwen-audio-3.0-tts-plus",children:[{tag:"code",children:[{tag:"text",text:"qwen-audio-3.0-tts-plus"}]}]},{tag:"text",text:"：通义千问高品质语音合成模型，可精细控制情感、语气、角色、语速、音量与合成风格，并在噪声和混响环境下更稳定，适合内容创作、有声书、影视配音、品牌声音设计及高品质语音服务。"}]}],en:[{tag:"li",children:[{tag:"text",text:"Added "},{tag:"a",href:"https://aihubmix.com/model/qwen-audio-3.0-tts-plus",children:[{tag:"code",children:[{tag:"text",text:"qwen-audio-3.0-tts-plus"}]}]},{tag:"text",text:": Qwen’s premium speech synthesis model with richer control of emotion, tone, character, speaking rate, volume, and style, designed for content creation, audiobooks, dubbing, brand voices, and other high-quality speech workflows."}]}]}},{id:"models-2026-07-24-1-0",category:"models",kind:"capability",publishedAt:"2026-07-24T00:00:00+08:00",models:["qwen-audio-3.0-tts-plus","qwen-audio-3.0-tts-flash"],model:"qwen-audio-3.0-tts-plus",source:{zh:"https://docs.aihubmix.com/cn/update/News#release-2026-07-24",en:"https://docs.aihubmix.com/en/update/News#release-2026-07-24"},title:{zh:"Qwen TTS 音频生成能力",en:"Qwen TTS audio-generation capabilities"},body:{zh:"两个 Qwen TTS 模型均可通过 /v1/audio/speech 调用；非流式请求返回音频结果链接，流式请求返回 SSE 音频生成事件。下表结合 AIHubMix 兼容字段与阿里云官方 Qwen-Audio-TTS 口径整理；voice 需按模型分别选择，plus 与 flash 的系统音色不可混用。",en:"Both Qwen TTS models can be called through /v1/audio/speech; non-streaming requests return an audio result URL, while streaming requests return SSE audio-generation events. The table below combines AIHubMix-compatible fields with Alibaba Cloud’s official Qwen-Audio-TTS docs; voice must be selected per model, and plus/flash system voices are not interchangeable."},blocks:{zh:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"两个 Qwen TTS 模型均可通过 "},{tag:"code",children:[{tag:"text",text:"/v1/audio/speech"}]},{tag:"text",text:" 调用；非流式请求返回音频结果链接，流式请求返回 SSE 音频生成事件。下表结合 AIHubMix 兼容字段与阿里云官方 Qwen-Audio-TTS 口径整理；"},{tag:"code",children:[{tag:"text",text:"voice"}]},{tag:"text",text:" 需按模型分别选择，plus 与 flash 的系统音色不可混用。"}]},{tag:"text",text:`
`}]},{tag:"table",children:[{tag:"thead",children:[{tag:"tr",children:[{tag:"th",children:[{tag:"text",text:"能力项"}]},{tag:"th",children:[{tag:"code",children:[{tag:"text",text:"qwen-audio-3.0-tts-plus"}]}]},{tag:"th",children:[{tag:"code",children:[{tag:"text",text:"qwen-audio-3.0-tts-flash"}]}]}]}]},{tag:"tbody",children:[{tag:"tr",children:[{tag:"td",children:[{tag:"text",text:"输入文本 "},{tag:"code",children:[{tag:"text",text:"input"}]}]},{tag:"td",children:[{tag:"text",text:"必填，AIHubMix 兼容入口单次不超过 4096 字符；文本内可嵌入 "},{tag:"code",children:[{tag:"text",text:"[excited]"}]},{tag:"text",text:"、"},{tag:"code",children:[{tag:"text",text:"[laughing]"}]},{tag:"text",text:" 等情感标签。"}]},{tag:"td",children:[{tag:"text",text:"必填，AIHubMix 兼容入口单次不超过 4096 字符；文本内可嵌入 "},{tag:"code",children:[{tag:"text",text:"[excited]"}]},{tag:"text",text:"、"},{tag:"code",children:[{tag:"text",text:"[laughing]"}]},{tag:"text",text:" 等情感标签。"}]}]},{tag:"tr",children:[{tag:"td",children:[{tag:"text",text:"音色 "},{tag:"code",children:[{tag:"text",text:"voice"}]}]},{tag:"td",children:[{tag:"text",text:"系统音色："},{tag:"code",children:[{tag:"text",text:"longanlingxin"}]},{tag:"text",text:"、"},{tag:"code",children:[{tag:"text",text:"longanlufeng"}]},{tag:"text",text:"；另有 500+ 基础/克隆音色。"}]},{tag:"td",children:[{tag:"text",text:"系统音色："},{tag:"code",children:[{tag:"text",text:"longanhuan_v3.6"}]},{tag:"text",text:"、"},{tag:"code",children:[{tag:"text",text:"longjielidou_v3.6"}]},{tag:"text",text:"、"},{tag:"code",children:[{tag:"text",text:"loongeva_v3.6"}]},{tag:"text",text:"、"},{tag:"code",children:[{tag:"text",text:"loongjohn"}]},{tag:"text",text:"；另有 500+ 基础/克隆音色。"}]}]},{tag:"tr",children:[{tag:"td",children:[{tag:"text",text:"输出格式 "},{tag:"code",children:[{tag:"text",text:"response_format"}]}]},{tag:"td",children:[{tag:"text",text:"默认 "},{tag:"code",children:[{tag:"text",text:"mp3"}]},{tag:"text",text:"，也支持 "},{tag:"code",children:[{tag:"text",text:"wav"}]},{tag:"text",text:"、"},{tag:"code",children:[{tag:"text",text:"pcm"}]},{tag:"text",text:"、"},{tag:"code",children:[{tag:"text",text:"opus"}]},{tag:"text",text:"；暂不支持 "},{tag:"code",children:[{tag:"text",text:"aac"}]},{tag:"text",text:"/"},{tag:"code",children:[{tag:"text",text:"flac"}]},{tag:"text",text:"。"}]},{tag:"td",children:[{tag:"text",text:"默认 "},{tag:"code",children:[{tag:"text",text:"mp3"}]},{tag:"text",text:"，也支持 "},{tag:"code",children:[{tag:"text",text:"wav"}]},{tag:"text",text:"、"},{tag:"code",children:[{tag:"text",text:"pcm"}]},{tag:"text",text:"、"},{tag:"code",children:[{tag:"text",text:"opus"}]},{tag:"text",text:"；暂不支持 "},{tag:"code",children:[{tag:"text",text:"aac"}]},{tag:"text",text:"/"},{tag:"code",children:[{tag:"text",text:"flac"}]},{tag:"text",text:"。"}]}]},{tag:"tr",children:[{tag:"td",children:[{tag:"text",text:"语速 "},{tag:"code",children:[{tag:"text",text:"speed"}]}]},{tag:"td",children:[{tag:"text",text:"支持 "},{tag:"code",children:[{tag:"text",text:"0.5"}]},{tag:"text",text:" 到 "},{tag:"code",children:[{tag:"text",text:"2.0"}]},{tag:"text",text:"，默认 "},{tag:"code",children:[{tag:"text",text:"1.0"}]},{tag:"text",text:"。"}]},{tag:"td",children:[{tag:"text",text:"支持 "},{tag:"code",children:[{tag:"text",text:"0.5"}]},{tag:"text",text:" 到 "},{tag:"code",children:[{tag:"text",text:"2.0"}]},{tag:"text",text:"，默认 "},{tag:"code",children:[{tag:"text",text:"1.0"}]},{tag:"text",text:"。"}]}]},{tag:"tr",children:[{tag:"td",children:[{tag:"text",text:"口播指令 "},{tag:"code",children:[{tag:"text",text:"instructions"}]}]},{tag:"td",children:[{tag:"text",text:"支持用自然语言控制角色、情绪和口音，例如“用温柔的语气”。"}]},{tag:"td",children:[{tag:"text",text:"支持用自然语言控制角色、情绪和口音，例如“用温柔的语气”。"}]}]},{tag:"tr",children:[{tag:"td",children:[{tag:"text",text:"流式 "},{tag:"code",children:[{tag:"text",text:"stream_format"}]}]},{tag:"td",children:[{tag:"text",text:"非流式可不传，返回 URL JSON；流式传 "},{tag:"code",children:[{tag:"text",text:"sse"}]},{tag:"text",text:"，返回 SSE 事件。"}]},{tag:"td",children:[{tag:"text",text:"非流式可不传，返回 URL JSON；流式传 "},{tag:"code",children:[{tag:"text",text:"sse"}]},{tag:"text",text:"，返回 SSE 事件。"}]}]}]}]}],en:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"Both Qwen TTS models can be called through "},{tag:"code",children:[{tag:"text",text:"/v1/audio/speech"}]},{tag:"text",text:"; non-streaming requests return an audio result URL, while streaming requests return SSE audio-generation events. The table below combines AIHubMix-compatible fields with Alibaba Cloud’s official Qwen-Audio-TTS docs; "},{tag:"code",children:[{tag:"text",text:"voice"}]},{tag:"text",text:" must be selected per model, and plus/flash system voices are not interchangeable."}]},{tag:"text",text:`
`}]},{tag:"table",children:[{tag:"thead",children:[{tag:"tr",children:[{tag:"th",children:[{tag:"text",text:"Capability"}]},{tag:"th",children:[{tag:"code",children:[{tag:"text",text:"qwen-audio-3.0-tts-plus"}]}]},{tag:"th",children:[{tag:"code",children:[{tag:"text",text:"qwen-audio-3.0-tts-flash"}]}]}]}]},{tag:"tbody",children:[{tag:"tr",children:[{tag:"td",children:[{tag:"text",text:"Input text "},{tag:"code",children:[{tag:"text",text:"input"}]}]},{tag:"td",children:[{tag:"text",text:"Required, up to 4096 characters through the AIHubMix-compatible endpoint. Text can include emotion tags such as "},{tag:"code",children:[{tag:"text",text:"[excited]"}]},{tag:"text",text:" and "},{tag:"code",children:[{tag:"text",text:"[laughing]"}]},{tag:"text",text:"."}]},{tag:"td",children:[{tag:"text",text:"Required, up to 4096 characters through the AIHubMix-compatible endpoint. Text can include emotion tags such as "},{tag:"code",children:[{tag:"text",text:"[excited]"}]},{tag:"text",text:" and "},{tag:"code",children:[{tag:"text",text:"[laughing]"}]},{tag:"text",text:"."}]}]},{tag:"tr",children:[{tag:"td",children:[{tag:"text",text:"Voice "},{tag:"code",children:[{tag:"text",text:"voice"}]}]},{tag:"td",children:[{tag:"text",text:"System voices: "},{tag:"code",children:[{tag:"text",text:"longanlingxin"}]},{tag:"text",text:", "},{tag:"code",children:[{tag:"text",text:"longanlufeng"}]},{tag:"text",text:"; also 500+ base/cloned voices."}]},{tag:"td",children:[{tag:"text",text:"System voices: "},{tag:"code",children:[{tag:"text",text:"longanhuan_v3.6"}]},{tag:"text",text:", "},{tag:"code",children:[{tag:"text",text:"longjielidou_v3.6"}]},{tag:"text",text:", "},{tag:"code",children:[{tag:"text",text:"loongeva_v3.6"}]},{tag:"text",text:", "},{tag:"code",children:[{tag:"text",text:"loongjohn"}]},{tag:"text",text:"; also 500+ base/cloned voices."}]}]},{tag:"tr",children:[{tag:"td",children:[{tag:"text",text:"Audio format "},{tag:"code",children:[{tag:"text",text:"response_format"}]}]},{tag:"td",children:[{tag:"text",text:"Default "},{tag:"code",children:[{tag:"text",text:"mp3"}]},{tag:"text",text:"; also supports "},{tag:"code",children:[{tag:"text",text:"wav"}]},{tag:"text",text:", "},{tag:"code",children:[{tag:"text",text:"pcm"}]},{tag:"text",text:", and "},{tag:"code",children:[{tag:"text",text:"opus"}]},{tag:"text",text:". "},{tag:"code",children:[{tag:"text",text:"aac"}]},{tag:"text",text:"/"},{tag:"code",children:[{tag:"text",text:"flac"}]},{tag:"text",text:" are not supported."}]},{tag:"td",children:[{tag:"text",text:"Default "},{tag:"code",children:[{tag:"text",text:"mp3"}]},{tag:"text",text:"; also supports "},{tag:"code",children:[{tag:"text",text:"wav"}]},{tag:"text",text:", "},{tag:"code",children:[{tag:"text",text:"pcm"}]},{tag:"text",text:", and "},{tag:"code",children:[{tag:"text",text:"opus"}]},{tag:"text",text:". "},{tag:"code",children:[{tag:"text",text:"aac"}]},{tag:"text",text:"/"},{tag:"code",children:[{tag:"text",text:"flac"}]},{tag:"text",text:" are not supported."}]}]},{tag:"tr",children:[{tag:"td",children:[{tag:"text",text:"Speed "},{tag:"code",children:[{tag:"text",text:"speed"}]}]},{tag:"td",children:[{tag:"text",text:"Supports "},{tag:"code",children:[{tag:"text",text:"0.5"}]},{tag:"text",text:" to "},{tag:"code",children:[{tag:"text",text:"2.0"}]},{tag:"text",text:", default "},{tag:"code",children:[{tag:"text",text:"1.0"}]},{tag:"text",text:"."}]},{tag:"td",children:[{tag:"text",text:"Supports "},{tag:"code",children:[{tag:"text",text:"0.5"}]},{tag:"text",text:" to "},{tag:"code",children:[{tag:"text",text:"2.0"}]},{tag:"text",text:", default "},{tag:"code",children:[{tag:"text",text:"1.0"}]},{tag:"text",text:"."}]}]},{tag:"tr",children:[{tag:"td",children:[{tag:"text",text:"Style instructions "},{tag:"code",children:[{tag:"text",text:"instructions"}]}]},{tag:"td",children:[{tag:"text",text:"Natural-language control for role, emotion, and accent, such as “use a gentle tone”."}]},{tag:"td",children:[{tag:"text",text:"Natural-language control for role, emotion, and accent, such as “use a gentle tone”."}]}]},{tag:"tr",children:[{tag:"td",children:[{tag:"text",text:"Streaming "},{tag:"code",children:[{tag:"text",text:"stream_format"}]}]},{tag:"td",children:[{tag:"text",text:"Omit for non-streaming URL JSON; set "},{tag:"code",children:[{tag:"text",text:"sse"}]},{tag:"text",text:" for streaming events."}]},{tag:"td",children:[{tag:"text",text:"Omit for non-streaming URL JSON; set "},{tag:"code",children:[{tag:"text",text:"sse"}]},{tag:"text",text:" for streaming events."}]}]}]}]}]}},{id:"release-2026-07-24",category:"changelog",kind:"release",publishedAt:"2026-07-24T00:00:00+08:00",title:{zh:"Messages 响应与流式用量统计优化",en:"Messages responses and streaming usage accounting"},sourceTitle:{zh:"MAI 图像与 Qwen TTS 音频能力更新",en:"Qwen TTS / MAI Image: Audio generation and model updates"},releaseLabel:"Release 2026.07.24",source:{zh:"https://docs.aihubmix.com/cn/update/News#release-2026-07-24",en:"https://docs.aihubmix.com/en/update/News#release-2026-07-24"},sections:[{heading:{zh:"Messages 响应兼容性提升",en:"Improved Messages response compatibility"},blocks:{zh:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"通过 "},{tag:"code",children:[{tag:"text",text:"/v1/messages"}]},{tag:"text",text:" 调用桥接模型或 Claude/Bedrock 等模型服务时，响应字段与 Anthropic Messages 生态更一致，减少严格 SDK 或校验器因缺少响应字段而报错的情况。"}]},{tag:"text",text:`
`}]}],en:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"code",children:[{tag:"text",text:"/v1/messages"}]},{tag:"text",text:" responses from bridged models and Claude-compatible services such as Bedrock now align more closely with the Anthropic Messages ecosystem, reducing errors in strict SDKs or schema validators caused by missing response fields."}]},{tag:"text",text:`
`}]}]}},{heading:{zh:"AWS Bedrock 流式断连后的用量更完整",en:"More complete usage after AWS Bedrock stream disconnects"},blocks:{zh:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"客户端中途断开 AWS Bedrock 流式请求时，平台会继续收口模型推理厂商结束信号和最终 usage，减少断连场景下请求日志、结算明细和实际模型推理厂商用量不一致的情况。"}]},{tag:"text",text:`
`}]}],en:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"When a client disconnects from an AWS Bedrock streaming request, AIHubMix continues collecting the model inference provider terminator and final usage, reducing mismatches between request logs, billing details, and model inference provider usage in disconnect scenarios."}]},{tag:"text",text:`
`}]}]}}]},{id:"models-2026-07-23-0-0",category:"models",kind:"launch",publishedAt:"2026-07-23T00:00:00+08:00",models:["qwen3-coder-480b-a35b-instruct"],model:"qwen3-coder-480b-a35b-instruct",source:{zh:"https://docs.aihubmix.com/cn/update/News#release-2026-07-23",en:"https://docs.aihubmix.com/en/update/News#release-2026-07-23"},title:{zh:"qwen3-coder-480b-a35b-instruct 上线",en:"qwen3-coder-480b-a35b-instruct now available"},body:{zh:"新增 qwen3-coder-480b-a35b-instruct：Qwen3-Coder 旗舰代码模型，面向代码生成、软件工程 Agent 和工具调用工作流，适合长上下文代码理解与自动化开发任务。",en:"Added qwen3-coder-480b-a35b-instruct: Qwen3-Coder’s flagship coding model for code generation, software-engineering agents, and tool-calling workflows, suited for long-context code understanding and automated development tasks."},blocks:{zh:[{tag:"li",children:[{tag:"text",text:"新增 "},{tag:"a",href:"https://aihubmix.com/model/qwen3-coder-480b-a35b-instruct",children:[{tag:"code",children:[{tag:"text",text:"qwen3-coder-480b-a35b-instruct"}]}]},{tag:"text",text:"：Qwen3-Coder 旗舰代码模型，面向代码生成、软件工程 Agent 和工具调用工作流，适合长上下文代码理解与自动化开发任务。"}]}],en:[{tag:"li",children:[{tag:"text",text:"Added "},{tag:"a",href:"https://aihubmix.com/model/qwen3-coder-480b-a35b-instruct",children:[{tag:"code",children:[{tag:"text",text:"qwen3-coder-480b-a35b-instruct"}]}]},{tag:"text",text:": Qwen3-Coder’s flagship coding model for code generation, software-engineering agents, and tool-calling workflows, suited for long-context code understanding and automated development tasks."}]}]}},{id:"models-2026-07-23-0-1",category:"models",kind:"launch",publishedAt:"2026-07-23T00:00:00+08:00",models:["gemini-2.5-flash-lite"],model:"gemini-2.5-flash-lite",source:{zh:"https://docs.aihubmix.com/cn/update/News#release-2026-07-23",en:"https://docs.aihubmix.com/en/update/News#release-2026-07-23"},title:{zh:"gemini-2.5-flash-lite 上线",en:"gemini-2.5-flash-lite now available"},body:{zh:"新增 gemini-2.5-flash-lite：Google 2.5 系列轻量模型，面向低延迟、低成本的高频文本、多模态和批处理场景。",en:"Added gemini-2.5-flash-lite: a lightweight Gemini 2.5 model for low-latency, lower-cost high-frequency text, multimodal, and batch workloads."},blocks:{zh:[{tag:"li",children:[{tag:"text",text:"新增 "},{tag:"a",href:"https://aihubmix.com/model/gemini-2.5-flash-lite",children:[{tag:"code",children:[{tag:"text",text:"gemini-2.5-flash-lite"}]}]},{tag:"text",text:"：Google 2.5 系列轻量模型，面向低延迟、低成本的高频文本、多模态和批处理场景。"}]}],en:[{tag:"li",children:[{tag:"text",text:"Added "},{tag:"a",href:"https://aihubmix.com/model/gemini-2.5-flash-lite",children:[{tag:"code",children:[{tag:"text",text:"gemini-2.5-flash-lite"}]}]},{tag:"text",text:": a lightweight Gemini 2.5 model for low-latency, lower-cost high-frequency text, multimodal, and batch workloads."}]}]}},{id:"models-2026-07-23-0-2",category:"models",kind:"launch",publishedAt:"2026-07-23T00:00:00+08:00",models:["Qwen/Qwen3-235B-A22B-Instruct-2507"],model:"Qwen/Qwen3-235B-A22B-Instruct-2507",source:{zh:"https://docs.aihubmix.com/cn/update/News#release-2026-07-23",en:"https://docs.aihubmix.com/en/update/News#release-2026-07-23"},title:{zh:"Qwen/Qwen3-235B-A22B-Instruct-2507 上线",en:"Qwen/Qwen3-235B-A22B-Instruct-2507 now available"},body:{zh:"新增 Qwen/Qwen3-235B-A22B-Instruct-2507：Qwen3 235B-A22B Instruct 2507 版本，适合通用对话、指令跟随、多语种与长上下文任务。",en:"Added Qwen/Qwen3-235B-A22B-Instruct-2507: the Qwen3 235B-A22B Instruct 2507 release for general chat, instruction following, multilingual work, and long-context tasks."},blocks:{zh:[{tag:"li",children:[{tag:"text",text:"新增 "},{tag:"a",href:"https://aihubmix.com/model/Qwen/Qwen3-235B-A22B-Instruct-2507",children:[{tag:"code",children:[{tag:"text",text:"Qwen/Qwen3-235B-A22B-Instruct-2507"}]}]},{tag:"text",text:"：Qwen3 235B-A22B Instruct 2507 版本，适合通用对话、指令跟随、多语种与长上下文任务。"}]}],en:[{tag:"li",children:[{tag:"text",text:"Added "},{tag:"a",href:"https://aihubmix.com/model/Qwen/Qwen3-235B-A22B-Instruct-2507",children:[{tag:"code",children:[{tag:"text",text:"Qwen/Qwen3-235B-A22B-Instruct-2507"}]}]},{tag:"text",text:": the Qwen3 235B-A22B Instruct 2507 release for general chat, instruction following, multilingual work, and long-context tasks."}]}]}},{id:"models-2026-07-22-0-0",category:"models",kind:"launch",publishedAt:"2026-07-22T00:00:00+08:00",models:["gemini-3.6-flash"],model:"gemini-3.6-flash",source:{zh:"https://docs.aihubmix.com/cn/update/News#release-2026-07-22",en:"https://docs.aihubmix.com/en/update/News#release-2026-07-22"},title:{zh:"gemini-3.6-flash 上线",en:"gemini-3.6-flash now available"},body:{zh:"新增 gemini-3.6-flash：Google 最新 Flash 系列模型，适合复杂工作流、计算机使用、图表推理和长上下文任务。",en:"Added gemini-3.6-flash: Google’s latest Flash model for complex workflows, computer use, chart reasoning, and long-context tasks."},blocks:{zh:[{tag:"li",children:[{tag:"text",text:"新增 "},{tag:"a",href:"https://aihubmix.com/model/gemini-3.6-flash",children:[{tag:"code",children:[{tag:"text",text:"gemini-3.6-flash"}]}]},{tag:"text",text:"：Google 最新 Flash 系列模型，适合复杂工作流、计算机使用、图表推理和长上下文任务。"}]}],en:[{tag:"li",children:[{tag:"text",text:"Added "},{tag:"a",href:"https://aihubmix.com/model/gemini-3.6-flash",children:[{tag:"code",children:[{tag:"text",text:"gemini-3.6-flash"}]}]},{tag:"text",text:": Google’s latest Flash model for complex workflows, computer use, chart reasoning, and long-context tasks."}]}]}},{id:"models-2026-07-22-0-1",category:"models",kind:"launch",publishedAt:"2026-07-22T00:00:00+08:00",models:["gemini-3.5-flash-lite"],model:"gemini-3.5-flash-lite",source:{zh:"https://docs.aihubmix.com/cn/update/News#release-2026-07-22",en:"https://docs.aihubmix.com/en/update/News#release-2026-07-22"},title:{zh:"gemini-3.5-flash-lite 上线",en:"gemini-3.5-flash-lite now available"},body:{zh:"新增 gemini-3.5-flash-lite：更轻量、低成本的 3.5 系列模型，适合高吞吐、日常自动化和批量处理场景。",en:"Added gemini-3.5-flash-lite: a lighter, lower-cost Gemini 3.5 model for high-throughput, everyday automation, and batch processing."},blocks:{zh:[{tag:"li",children:[{tag:"text",text:"新增 "},{tag:"a",href:"https://aihubmix.com/model/gemini-3.5-flash-lite",children:[{tag:"code",children:[{tag:"text",text:"gemini-3.5-flash-lite"}]}]},{tag:"text",text:"：更轻量、低成本的 3.5 系列模型，适合高吞吐、日常自动化和批量处理场景。"}]}],en:[{tag:"li",children:[{tag:"text",text:"Added "},{tag:"a",href:"https://aihubmix.com/model/gemini-3.5-flash-lite",children:[{tag:"code",children:[{tag:"text",text:"gemini-3.5-flash-lite"}]}]},{tag:"text",text:": a lighter, lower-cost Gemini 3.5 model for high-throughput, everyday automation, and batch processing."}]}]}},{id:"models-2026-07-22-0-2",category:"models",kind:"launch",publishedAt:"2026-07-22T00:00:00+08:00",models:["glm-5.2-fast-preview"],model:"glm-5.2-fast-preview",source:{zh:"https://docs.aihubmix.com/cn/update/News#release-2026-07-22",en:"https://docs.aihubmix.com/en/update/News#release-2026-07-22"},title:{zh:"glm-5.2-fast-preview 上线",en:"glm-5.2-fast-preview now available"},body:{zh:"新增 glm-5.2-fast-preview：智谱 GLM-5.2 系列快速预览模型，适合低延迟对话、快速推理和高吞吐应用场景。",en:"Added glm-5.2-fast-preview: Zhipu GLM-5.2 fast preview model for low-latency chat, fast reasoning, and high-throughput applications."},blocks:{zh:[{tag:"li",children:[{tag:"text",text:"新增 "},{tag:"a",href:"https://aihubmix.com/model/glm-5.2-fast-preview",children:[{tag:"code",children:[{tag:"text",text:"glm-5.2-fast-preview"}]}]},{tag:"text",text:"：智谱 GLM-5.2 系列快速预览模型，适合低延迟对话、快速推理和高吞吐应用场景。"}]}],en:[{tag:"li",children:[{tag:"text",text:"Added "},{tag:"a",href:"https://aihubmix.com/model/glm-5.2-fast-preview",children:[{tag:"code",children:[{tag:"text",text:"glm-5.2-fast-preview"}]}]},{tag:"text",text:": Zhipu GLM-5.2 fast preview model for low-latency chat, fast reasoning, and high-throughput applications."}]}]}},{id:"release-2026-07-22",category:"changelog",kind:"release",publishedAt:"2026-07-22T00:00:00+08:00",title:{zh:"流式请求稳定性提升",en:"Streaming request stability improvements"},sourceTitle:{zh:"Gemini、GLM 扩容与 Qwen 流式修复",en:"Gemini / GLM / Qwen: Models and platform improvements"},releaseLabel:"Release 2026.07.22",source:{zh:"https://docs.aihubmix.com/cn/update/News#release-2026-07-22",en:"https://docs.aihubmix.com/en/update/News#release-2026-07-22"},sections:[{heading:{zh:"Qwen 3.8 流式请求稳定性提升",en:"More reliable Qwen 3.8 streaming"},blocks:{zh:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"通过 "},{tag:"code",children:[{tag:"text",text:"/v1/responses"}]},{tag:"text",text:" 和 "},{tag:"code",children:[{tag:"text",text:"/v1/messages"}]},{tag:"text",text:" 调用 "},{tag:"code",children:[{tag:"text",text:"qwen3.8-max-preview"}]},{tag:"text",text:" 的流式请求更稳定；此前可能被误判为 "},{tag:"code",children:[{tag:"text",text:"empty_stream"}]},{tag:"text",text:" 或 502 的有效流式响应，现在可正常完成。"}]},{tag:"text",text:`
`}]}],en:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"Streaming calls to "},{tag:"code",children:[{tag:"text",text:"qwen3.8-max-preview"}]},{tag:"text",text:" through "},{tag:"code",children:[{tag:"text",text:"/v1/responses"}]},{tag:"text",text:" and "},{tag:"code",children:[{tag:"text",text:"/v1/messages"}]},{tag:"text",text:" are more reliable. Valid streams that could previously surface as "},{tag:"code",children:[{tag:"text",text:"empty_stream"}]},{tag:"text",text:" or 502 can now complete normally."}]},{tag:"text",text:`
`}]}]}}]},{id:"release-2026-07-21",category:"changelog",kind:"release",publishedAt:"2026-07-21T00:00:00+08:00",title:{zh:"图像参数、工具调用与分时计费优化",en:"Image parameters, tools and time-based billing"},sourceTitle:{zh:"Gemini、Responses 与分时计费优化",en:"Gemini / Responses: Cross-protocol compatibility"},releaseLabel:"Release 2026.07.21",source:{zh:"https://docs.aihubmix.com/cn/update/News#release-2026-07-21",en:"https://docs.aihubmix.com/en/update/News#release-2026-07-21"},sections:[{heading:{zh:"Gemini 图像参数透传",en:"Gemini image parameters passthrough"},blocks:{zh:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"通过 OpenAI 兼容的 Chat Completions 调用 Gemini/Vertex 图像输出模型时，现在可在 "},{tag:"code",children:[{tag:"text",text:"extra_body.generationConfig.imageConfig"}]},{tag:"text",text:" 中传递 "},{tag:"code",children:[{tag:"text",text:"aspectRatio"}]},{tag:"text",text:" 和 "},{tag:"code",children:[{tag:"text",text:"imageSize"}]},{tag:"text",text:"。"}]},{tag:"text",text:`
`}]}],en:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"When calling Gemini or Vertex image-output models through the OpenAI-compatible Chat Completions API, clients can now pass "},{tag:"code",children:[{tag:"text",text:"aspectRatio"}]},{tag:"text",text:" and "},{tag:"code",children:[{tag:"text",text:"imageSize"}]},{tag:"text",text:" in "},{tag:"code",children:[{tag:"text",text:"extra_body.generationConfig.imageConfig"}]},{tag:"text",text:"."}]},{tag:"text",text:`
`}]}]}},{heading:{zh:"Gemini embedding 计费更贴近真实用量",en:"More accurate Gemini embedding billing"},blocks:{zh:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"Gemini embedding 响应提供真实 "},{tag:"code",children:[{tag:"text",text:"usageMetadata"}]},{tag:"text",text:" 时，平台会优先按模型推理厂商返回的 token 用量计费；缺失时仍保留原本地估算兜底。"}]},{tag:"text",text:`
`}]}],en:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"When Gemini embedding responses include real "},{tag:"code",children:[{tag:"text",text:"usageMetadata"}]},{tag:"text",text:", AIHubMix now bills from the token usage returned by the model provider first, while keeping the previous local estimate as a fallback when usage is missing."}]},{tag:"text",text:`
`}]}]}},{heading:{zh:"Responses 动态工具兼容性提升",en:"Improved Responses dynamic tool compatibility"},blocks:{zh:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"code",children:[{tag:"text",text:"/v1/responses"}]},{tag:"text",text:" 请求中的 system/developer 消息现在可携带 message-local function tools，并在桥接到 Chat 模型时保留工具声明。依赖 Kimi K3 等动态工具工作流的客户端，工具调用链路更稳定。"}]},{tag:"text",text:`
`}]}],en:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"code",children:[{tag:"text",text:"/v1/responses"}]},{tag:"text",text:" requests can now carry message-local function tools on system/developer messages, and those tool declarations are preserved when bridged to Chat models. Clients using dynamic tool workflows with models such as Kimi K3 should see more reliable tool calls."}]},{tag:"text",text:`
`}]}]}},{heading:{zh:"Responses stop 参数修复",en:"Responses stop parameter fixed"},blocks:{zh:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"通过 "},{tag:"code",children:[{tag:"text",text:"/v1/responses"}]},{tag:"text",text:" 调用会桥接到 Chat 的模型时，"},{tag:"code",children:[{tag:"text",text:"stop"}]},{tag:"text",text:" 现在会随请求传给模型推理厂商。单个停止词可正常生效，超过模型推理厂商限制的数量或长度会返回与 Chat Completions 一致的边界错误。"}]},{tag:"text",text:`
`}]}],en:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"For "},{tag:"code",children:[{tag:"text",text:"/v1/responses"}]},{tag:"text",text:" calls that are bridged to Chat models, "},{tag:"code",children:[{tag:"text",text:"stop"}]},{tag:"text",text:" is now forwarded to the model provider. A single stop sequence can take effect normally, while stop counts or lengths beyond model provider limits return boundary errors consistent with Chat Completions."}]},{tag:"text",text:`
`}]}]}},{heading:{zh:"DeepSeek V4 分时段计费支持",en:"DeepSeek V4 time-based billing support"},blocks:{zh:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"DeepSeek V4 相关模型可按配置支持峰谷分时段计费；预扣与最终结算使用同一请求提交时间口径，跨峰谷边界的流式请求也按提交时刻计价。预扣保护仅作用于配置了分时段计费的模型，避免影响其他普通模型的余额冻结体验。"}]},{tag:"text",text:`
`}]}],en:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"DeepSeek V4 models can now support configured peak/off-peak billing. Pre-deduction and final billing use the same request submission time, so streaming requests that cross a time boundary are still charged by submission time. Pre-deduction protection is limited to models configured for time-based billing, avoiding balance-freeze changes for ordinary models."}]},{tag:"text",text:`
`}]}]}}]},{id:"release-2026-07-20",category:"changelog",kind:"release",publishedAt:"2026-07-20T00:00:00+08:00",title:{zh:"思考内容兼容与长流式连接优化",en:"Thinking compatibility and long-running streams"},sourceTitle:{zh:"Claude 思考兼容与长流式连接优化",en:"Claude: Streaming reliability and compatibility"},releaseLabel:"Release 2026.07.20",source:{zh:"https://docs.aihubmix.com/cn/update/News#release-2026-07-20",en:"https://docs.aihubmix.com/en/update/News#release-2026-07-20"},sections:[{heading:{zh:"Claude Messages thinking 兼容性修复",en:"Claude Messages thinking compatibility"},blocks:{zh:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"通过 "},{tag:"code",children:[{tag:"text",text:"/v1/messages"}]},{tag:"text",text:" 调用 OpenAI/Azure Chat 桥接模型时，Claude "},{tag:"code",children:[{tag:"text",text:"thinking"}]},{tag:"text",text:" 配置现在会转换为模型推理厂商可识别的思考强度，减少带 thinking 请求返回 400 的情况。Opus 4.8 等 Claude 原生思考请求仍保持兼容。"}]},{tag:"text",text:`
`}]}],en:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"When "},{tag:"code",children:[{tag:"text",text:"/v1/messages"}]},{tag:"text",text:" requests are bridged to OpenAI or Azure Chat models, Claude "},{tag:"code",children:[{tag:"text",text:"thinking"}]},{tag:"text",text:" settings are now mapped to a reasoning effort supported by the model provider, reducing 400 errors for thinking-enabled requests. Native Claude thinking requests such as Opus 4.8 remain compatible."}]},{tag:"text",text:`
`}]}]}},{heading:{zh:"长时间流式请求更稳定",en:"More stable long streaming requests"},blocks:{zh:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"生产环境网关的最长流式连接时间已从 1 小时放宽到 2 小时。Kimi K3 等长思考、长生成任务更不容易在思考或生成过程中被网关提前切断。"}]},{tag:"text",text:`
`}]}],en:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"The production gateway stream limit has been raised from 1 hour to 2 hours. Long-thinking and long-generation workloads such as Kimi K3 are less likely to be cut off while reasoning or generating."}]},{tag:"text",text:`
`}]}]}}]},{id:"models-2026-07-19-0-0",category:"models",kind:"launch",publishedAt:"2026-07-19T00:00:00+08:00",models:["qwen3.8-max-preview"],model:"qwen3.8-max-preview",source:{zh:"https://docs.aihubmix.com/cn/update/News#release-2026-07-19",en:"https://docs.aihubmix.com/en/update/News#release-2026-07-19"},title:{zh:"qwen3.8-max-preview 上线",en:"qwen3.8-max-preview now available"},body:{zh:"新增 qwen3.8-max-preview：限时福利：Qwen3.8-Max-Preview 调用价格按 1 折计算，相当于用量直接扩大 10 倍，活动限时，先到先用。 Qwen3.8-Max-Preview 是通义千问系列最新一代基座模型，参数量达 2.4T，并仍在持续进化中。相较上一代旗舰 Qwen3.7-Max，它在代码工程（Coding）、专业办公（Cowork）等核心能力上实现显著提升，在全栈开发、数据分析、Office 办公工作流等复杂长程任务中展现出全球领先的综合能力。",en:"Added qwen3.8-max-preview:Limited-time offer: Qwen3.8-Max-Preview is billed at just 10% of the standard price, effectively 10× your usage. Limited time, first come, first served.Qwen3.8-Max-Preview is the latest-generation foundation model in the Qwen family, with 2.4T parameters and still evolving. Compared with the previous flagship Qwen3.7-Max, it delivers major gains in core capabilities such as Coding and Cowork (professional productivity), and shows world-leading overall performance on complex, long-horizon tasks like full-stack development, data analysis, and Office workflows."},blocks:{zh:[{tag:"li",children:[{tag:"text",text:"新增 "},{tag:"a",href:"https://aihubmix.com/model/qwen3.8-max-preview",children:[{tag:"text",text:"qwen3.8-max-preview"}]},{tag:"text",text:"："},{tag:"br",children:[]},{tag:"text",text:"限时福利：Qwen3.8-Max-Preview 调用价格按 1 折计算，相当于用量直接扩大 10 倍，活动限时，先到先用。 "},{tag:"br",children:[]},{tag:"text",text:"Qwen3.8-Max-Preview 是通义千问系列最新一代基座模型，参数量达 2.4T，并仍在持续进化中。相较上一代旗舰 Qwen3.7-Max，它在代码工程（Coding）、专业办公（Cowork）等核心能力上实现显著提升，在全栈开发、数据分析、Office 办公工作流等复杂长程任务中展现出全球领先的综合能力。"}]}],en:[{tag:"li",children:[{tag:"text",text:"Added "},{tag:"a",href:"https://aihubmix.com/model/qwen3.8-max-preview",children:[{tag:"text",text:"qwen3.8-max-preview"}]},{tag:"text",text:":"},{tag:"br",children:[]},{tag:"text",text:"Limited-time offer: Qwen3.8-Max-Preview is billed at just 10% of the standard price, effectively 10× your usage. Limited time, first come, first served."},{tag:"br",children:[]},{tag:"br",children:[]},{tag:"text",text:"Qwen3.8-Max-Preview is the latest-generation foundation model in the Qwen family, with 2.4T parameters and still evolving. Compared with the previous flagship Qwen3.7-Max, it delivers major gains in core capabilities such as Coding and Cowork (professional productivity), and shows world-leading overall performance on complex, long-horizon tasks like full-stack development, data analysis, and Office workflows."}]}]}},{id:"models-2026-07-17-0-0",category:"models",kind:"launch",publishedAt:"2026-07-17T00:00:00+08:00",models:["kimi-k3"],model:"kimi-k3",source:{zh:"https://docs.aihubmix.com/cn/update/News#release-2026-07-17",en:"https://docs.aihubmix.com/en/update/News#release-2026-07-17"},title:{zh:"kimi-k3 上线",en:"kimi-k3 now available"},body:{zh:"新增 kimi-k3：Kimi K3 是 Moonshot AI 发布的开放 3T 级长上下文模型，采用 2.8T 参数、1M 上下文窗口，并原生支持视觉输入，适合长程编码、知识工作、复杂推理和多模态理解场景。调用方式见 Kimi K3 实测调用指南（三接口示例与能力支持矩阵）。",en:"Added kimi-k3: Kimi K3 is Moonshot AI’s open 3T-class long-context model, with 2.8T parameters, a 1M-token context window, and native vision input support. It is suited for long-horizon coding, knowledge work, complex reasoning, and multimodal understanding. See the Kimi K3 hands-on guide for usage (three-API examples and a capability support matrix)."},blocks:{zh:[{tag:"li",children:[{tag:"text",text:"新增 "},{tag:"a",href:"https://aihubmix.com/model/kimi-k3",children:[{tag:"code",children:[{tag:"text",text:"kimi-k3"}]}]},{tag:"text",text:"：Kimi K3 是 Moonshot AI 发布的开放 3T 级长上下文模型，采用 2.8T 参数、1M 上下文窗口，并原生支持视觉输入，适合长程编码、知识工作、复杂推理和多模态理解场景。调用方式见 "},{tag:"a",href:"https://docs.aihubmix.com/cn/blogs/kimi-k3-guide",children:[{tag:"text",text:"Kimi K3 实测调用指南"}]},{tag:"text",text:"（三接口示例与能力支持矩阵）。"}]}],en:[{tag:"li",children:[{tag:"text",text:"Added "},{tag:"a",href:"https://aihubmix.com/model/kimi-k3",children:[{tag:"code",children:[{tag:"text",text:"kimi-k3"}]}]},{tag:"text",text:": Kimi K3 is Moonshot AI’s open 3T-class long-context model, with 2.8T parameters, a 1M-token context window, and native vision input support. It is suited for long-horizon coding, knowledge work, complex reasoning, and multimodal understanding. See the "},{tag:"a",href:"https://docs.aihubmix.com/en/blogs/kimi-k3-guide",children:[{tag:"text",text:"Kimi K3 hands-on guide"}]},{tag:"text",text:" for usage (three-API examples and a capability support matrix)."}]}]}},{id:"models-2026-07-17-0-1",category:"models",kind:"launch",publishedAt:"2026-07-17T00:00:00+08:00",models:["hy-3d-3.1"],model:"hy-3d-3.1",source:{zh:"https://docs.aihubmix.com/cn/update/News#release-2026-07-17",en:"https://docs.aihubmix.com/en/update/News#release-2026-07-17"},title:{zh:"hy-3d-3.1 上线",en:"hy-3d-3.1 now available"},body:{zh:"新增 hy-3d-3.1：腾讯混元生 3D 专业版模型，支持文生 3D、图生 3D 与多视图输入，面向游戏、电商展示、3D 打印、产品设计等 3D 资产生成场景；3.1 版本在几何精度与纹理细节上进一步提升，并支持八视图多角度输入。",en:"Added hy-3d-3.1: Tencent Hunyuan 3D Professional model for text-to-3D, image-to-3D, and multi-view 3D generation. It is suited for game assets, ecommerce presentation, 3D printing, and product-design workflows; the 3.1 model improves geometry and texture quality and supports eight-view input."},blocks:{zh:[{tag:"li",children:[{tag:"text",text:"新增 "},{tag:"a",href:"https://aihubmix.com/model/hy-3d-3.1",children:[{tag:"code",children:[{tag:"text",text:"hy-3d-3.1"}]}]},{tag:"text",text:"：腾讯混元生 3D 专业版模型，支持文生 3D、图生 3D 与多视图输入，面向游戏、电商展示、3D 打印、产品设计等 3D 资产生成场景；3.1 版本在几何精度与纹理细节上进一步提升，并支持八视图多角度输入。"}]}],en:[{tag:"li",children:[{tag:"text",text:"Added "},{tag:"a",href:"https://aihubmix.com/model/hy-3d-3.1",children:[{tag:"code",children:[{tag:"text",text:"hy-3d-3.1"}]}]},{tag:"text",text:": Tencent Hunyuan 3D Professional model for text-to-3D, image-to-3D, and multi-view 3D generation. It is suited for game assets, ecommerce presentation, 3D printing, and product-design workflows; the 3.1 model improves geometry and texture quality and supports eight-view input."}]}]}},{id:"release-2026-07-17",category:"changelog",kind:"release",publishedAt:"2026-07-17T00:00:00+08:00",title:{zh:"3D 生成接口与工具、计费兼容升级",en:"3D generation API, tools and billing compatibility"},sourceTitle:{zh:"Kimi K3 与混元 3D 生成上线",en:"Kimi K3 / Hunyuan 3D: Models and platform improvements"},releaseLabel:"Release 2026.07.17",source:{zh:"https://docs.aihubmix.com/cn/update/News#release-2026-07-17",en:"https://docs.aihubmix.com/en/update/News#release-2026-07-17"},sections:[{heading:{zh:"3D 生成接口上线",en:"3D generation API is available"},blocks:{zh:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"新增 "},{tag:"code",children:[{tag:"text",text:"/v1/3d/generations"}]},{tag:"text",text:" 异步 3D 生成接口，首期接入腾讯 TokenHub 混元 3D。支持提交生成任务和查询任务结果，便于把 3D 资产生成接入自动化工作流。"}]},{tag:"text",text:`
`}]}],en:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"Added the asynchronous "},{tag:"code",children:[{tag:"text",text:"/v1/3d/generations"}]},{tag:"text",text:" API, initially connected to Tencent TokenHub Hunyuan 3D. It supports task submission and result querying, making 3D asset generation easier to integrate into automated workflows."}]},{tag:"text",text:`
`}]}]}},{heading:{zh:"Kimi K3 动态工具兼容",en:"Kimi K3 dynamic tool compatibility"},blocks:{zh:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"Kimi K3 请求中的 message-level 动态工具声明会被保留并透传，模型推理厂商不再因为工具声明丢失而返回 400；使用 Kimi 动态加载工具的客户端兼容性更好。"}]},{tag:"text",text:`
`}]}],en:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"Message-level dynamically loaded tools in Kimi K3 requests are now preserved and forwarded, so model providers no longer return 400 errors caused by dropped tool declarations. Clients that use Kimi dynamic tool loading should see better compatibility."}]},{tag:"text",text:`
`}]}]}},{heading:{zh:"请求转换错误更清晰",en:"Clearer request-conversion errors"},blocks:{zh:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"Chat Completions 中的 custom tool、Cohere 文本块和 "},{tag:"code",children:[{tag:"text",text:"assistant.content: null"}]},{tag:"text",text:" 等请求形态兼容性提升。合法请求更稳定地到达模型推理厂商；畸形或不支持的输入会返回结构化错误，不再被误判为空 200 成功。"}]},{tag:"text",text:`
`}]}],en:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"Compatibility has improved for Chat Completions custom tools, Cohere text content blocks, and requests with "},{tag:"code",children:[{tag:"text",text:"assistant.content: null"}]},{tag:"text",text:". Valid requests reach model providers more reliably, while malformed or unsupported inputs now return structured errors instead of being misreported as empty HTTP 200 successes."}]},{tag:"text",text:`
`}]}]}},{heading:{zh:"Seedream 图片计费口径更新",en:"Updated Seedream image billing"},blocks:{zh:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"code",children:[{tag:"text",text:"doubao-seedream-5-0-pro"}]},{tag:"text",text:" 的图片生成计费现在支持按输出像素档和输入图片张数计价，文生图、图生图等请求的费用更贴近实际生成规格。"}]},{tag:"text",text:`
`}]}],en:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"code",children:[{tag:"text",text:"doubao-seedream-5-0-pro"}]},{tag:"text",text:" image generation now supports billing by output pixel tier and input-image count, so text-to-image and image-to-image requests are charged closer to their actual generation specs."}]},{tag:"text",text:`
`}]}]}}]},{id:"release-2026-07-15",category:"changelog",kind:"release",publishedAt:"2026-07-15T00:00:00+08:00",title:{zh:"工具调用与结构化输出兼容修复",en:"Tool calling and structured output compatibility fixes"},sourceTitle:{zh:"Gemini 工具与结构化输出兼容优化",en:"Gemini: Tool calling improvements"},releaseLabel:"Release 2026.07.15",source:{zh:"https://docs.aihubmix.com/cn/update/News#release-2026-07-15",en:"https://docs.aihubmix.com/en/update/News#release-2026-07-15"},sections:[{heading:{zh:"Gemini 工具调用兼容性修复",en:"Improved Gemini tool-call compatibility"},blocks:{zh:[{tag:"ul",children:[{tag:"text",text:`
`},{tag:"li",children:[{tag:"text",text:"通过 OpenAI 兼容接口调用 Gemini/Vertex 时，工具定义或结构化输出 schema 中包含空字符串枚举值的请求不再因模型推理厂商校验返回 400；工具调用和 JSON Schema 工作流更稳定。"}]},{tag:"text",text:`
`}]}],en:[{tag:"ul",children:[{tag:"text",text:`
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Z=="string"&&Z.startsWith("zh")},w=function(Z){return Z?(Array.isArray(Z)?Z:String(Z).split(",")).map(J=>String(J).trim()).filter(Boolean):[]},I=function(Z){return Number(Z).toLocaleString("en-US")},P=function(Z){const J=Number(Z)||0;if(J<=0)return"";if(J<1e3)return String(J);const[oe,ie]=J>=1e6?[J/1e6,"M"]:[J/1e3,"K"],ce=oe>=100?0:oe>=10?1:2;return`${Number(oe.toFixed(ce))}${ie}`},_=function(Z,J){return Z.length?A(J)?Z.join("、"):Z.length===1?Z[0]:`${Z.slice(0,-1).join(", ")} and ${Z[Z.length-1]}`:""},D=function(Z,J,oe){const ie=A(oe)?1:0;return Z.map(ce=>{const ae=J[ce];return ae?ae[ie]:A(oe)?ce:ce.replace(/_/g," ")})},F=function(Z,J){return D(w(Z),le,J)},N=function(Z,J){return D(w(Z),fe,J)},L=function(Z){const J=w(Z&&Z.output_modalities);if(J.length){const ie={};return J.forEach(ce=>{ie[ce]=!0}),ye.filter(ce=>ie[ce])}const oe={};return w(Z&&Z.types).forEach(ie=>{const ce=he[ie];ce&&(oe[ce]=!0)}),ye.filter(ie=>oe[ie])},O=function(Z,J){return D(L(Z),fe,J)},z=function(Z,J,oe){const 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Please top up to avoid service interruption."],invoice_rejected:["Invoice {invoice_no} was rejected","Invoice {invoice_no} was rejected. Reason: {reason}"],auto_recharge_succeeded:["Auto top-up succeeded","Your account was automatically topped up with {amount}."],balanceAfter:" Current balance: {balance}.",auto_recharge_incomplete:["Auto top-up did not complete","A recent automatic top-up attempt did not complete. Your current auto top-up settings were not changed by this attempt."],auto_recharge_paused_system:["Auto top-up paused","Your automatic top-up could not be completed due to a temporary processing issue. Auto top-up was paused as a precaution, and your saved card was not charged."],auto_recharge_paused:["Auto top-up failed","Your automatic top-up could not be completed (reason: {reason}). Auto top-up was paused. Please update your payment method before re-enabling it."]},zh:{model_launched:["新模型上线：{model}","模型 {model} 现已可用。"],model_retiring:["模型下线预告：{model}","模型 {model} 即将下线。"],model_retired:["模型已下线：{model}","模型 {model} 已于 {effective} 停止服务。"],model_price_changed:["模型价格调整：{model}","模型 {model} 的价格已调整，{effective} 起生效。"],retiredAt:"下线时间：{retired_at}。",successor:"推荐替代模型：{successor}。",prices:"输入 ${input} / 百万 tokens，输出 ${output} / 百万 tokens。",pricePage:"当前价格请查看模型详情页。",balance_reminder:["余额提醒：剩余 {balance}","您当前的账户余额为 {balance}，请及时充值，以免服务中断。"],invoice_rejected:["发票 {invoice_no} 已被驳回","发票 {invoice_no} 已被驳回。原因：{reason}"],auto_recharge_succeeded:["自动充值成功","您的账户已自动充值 {amount}。"],balanceAfter:"当前余额：{balance}。",auto_recharge_incomplete:["自动充值未完成","最近一次自动充值未完成，本次尝试未改变您的自动充值设置。"],auto_recharge_paused_system:["自动充值已暂停","由于临时处理问题，自动充值未能完成。为安全起见已暂停自动充值，您保存的银行卡未被扣款。"],auto_recharge_paused:["自动充值失败","自动充值未能完成（原因：{reason}），自动充值已暂停。请更新支付方式后再重新开启。"]},"zh-TW":{model_launched:["新模型上線：{model}","模型 {model} 現已可用。"],model_retiring:["模型下線預告：{model}","模型 {model} 即將下線。"],model_retired:["模型已下線：{model}","模型 {model} 已於 {effective} 停止服務。"],model_price_changed:["模型價格調整：{model}","模型 {model} 的價格已調整，{effective} 起生效。"],retiredAt:"下線時間：{retired_at}。",successor:"推薦替代模型：{successor}。",prices:"輸入 ${input} / 百萬 tokens，輸出 ${output} / 百萬 tokens。",pricePage:"目前價格請查看模型詳情頁。",balance_reminder:["餘額提醒：剩餘 {balance}","您目前的帳戶餘額為 {balance}，請及時儲值，以免服務中斷。"],invoice_rejected:["發票 {invoice_no} 已被駁回","發票 {invoice_no} 已被駁回。原因：{reason}"],auto_recharge_succeeded:["自動儲值成功","您的帳戶已自動儲值 {amount}。"],balanceAfter:"目前餘額：{balance}。",auto_recharge_incomplete:["自動儲值未完成","最近一次自動儲值未完成，本次嘗試未改變您的自動儲值設定。"],auto_recharge_paused_system:["自動儲值已暫停","由於暫時的處理問題，自動儲值未能完成。為安全起見已暫停自動儲值，您儲存的信用卡未被扣款。"],auto_recharge_paused:["自動儲值失敗","自動儲值未能完成（原因：{reason}），自動儲值已暫停。請更新付款方式後再重新開啟。"]},ja:{model_launched:["新モデル提供開始：{model}","モデル {model} が利用可能になりました。"],model_retiring:["モデル提供終了予定：{model}","モデル {model} は提供終了予定です。"],model_retired:["モデル提供終了：{model}","モデル {model} は {effective} をもって提供を終了しました。"],model_price_changed:["モデル価格改定：{model}","{model} の価格が改定されました（{effective} から適用）。"],retiredAt:"提供終了日時：{retired_at}。",successor:"推奨代替モデル：{successor}。",prices:"入力 ${input} / 100万トークン、出力 ${output} / 100万トークン。",pricePage:"最新の価格はモデル詳細ページをご確認ください。",balance_reminder:["残高のお知らせ：残り {balance}","現在の残高は {balance} です。サービスの中断を避けるため、チャージしてください。"],invoice_rejected:["請求書 {invoice_no} が却下されました","請求書 {invoice_no} が却下されました。理由：{reason}"],auto_recharge_succeeded:["自動チャージ完了","アカウントに {amount} が自動チャージされました。"],balanceAfter:"現在の残高：{balance}。",auto_recharge_incomplete:["自動チャージ未完了","直近の自動チャージが完了しませんでした。自動チャージの設定は変更されていません。"],auto_recharge_paused_system:["自動チャージを一時停止しました","一時的な処理の問題により自動チャージを完了できませんでした。念のため自動チャージを一時停止しました。登録済みのカードには請求されていません。"],auto_recharge_paused:["自動チャージ失敗","自動チャージを完了できませんでした（理由：{reason}）。自動チャージは一時停止されました。お支払い方法を更新してから再度有効にしてください。"]},ko:{model_launched:["새 모델 출시: {model}","{model} 모델을 이제 사용할 수 있습니다."],model_retiring:["모델 지원 종료 예정: {model}","{model} 모델은 지원 종료 예정입니다."],model_retired:["모델 지원 종료: {model}","{model} 모델은 {effective}부터 더 이상 제공되지 않습니다."],model_price_changed:["모델 가격 변경: {model}","{model}의 가격이 변경되었습니다. 적용일: {effective}."],retiredAt:" 종료 시점: {retired_at}.",successor:" 권장 대체 모델: {successor}.",prices:" 입력 ${input} / 100만 토큰, 출력 ${output} / 100만 토큰.",pricePage:" 현재 가격은 모델 상세 페이지에서 확인하세요.",balance_reminder:["잔액 알림: {balance} 남음","현재 계정 잔액은 {balance}입니다. 서비스 중단을 막으려면 충전해 주세요."],invoice_rejected:["청구서 {invoice_no} 반려됨","청구서 {invoice_no}이(가) 반려되었습니다. 사유: {reason}"],auto_recharge_succeeded:["자동 충전 완료","계정에 {amount}이(가) 자동 충전되었습니다."],balanceAfter:" 현재 잔액: {balance}.",auto_recharge_incomplete:["자동 충전 미완료","최근 자동 충전 시도가 완료되지 않았습니다. 자동 충전 설정은 변경되지 않았습니다."],auto_recharge_paused_system:["자동 충전 일시 중지","일시적인 처리 문제로 자동 충전을 완료하지 못했습니다. 예방 차원에서 자동 충전을 일시 중지했으며 저장된 카드에는 청구되지 않았습니다."],auto_recharge_paused:["자동 충전 실패","자동 충전을 완료하지 못했습니다(사유: {reason}). 자동 충전이 일시 중지되었습니다. 결제 수단을 업데이트한 후 다시 활성화하세요."]},de:{model_launched:["Modell verfügbar: {model}","Das Modell {model} ist jetzt verfügbar."],model_retiring:["Modell-Einstellung: {model}","Das Modell {model} wird demnächst eingestellt."],model_retired:["Modell eingestellt: {model}","Das Modell {model} ist seit {effective} nicht mehr verfügbar."],model_price_changed:["Preisänderung: {model}","Die Preise für {model} haben sich geändert, gültig ab {effective}."],retiredAt:" Einstellungszeitpunkt: {retired_at}.",successor:" Empfohlener Ersatz: {successor}.",prices:" Eingabe: ${input} / 1 Mio. Tokens, Ausgabe: ${output} / 1 Mio. Tokens.",pricePage:" Aktuelle Preise finden Sie auf der Modellseite.",balance_reminder:["Guthabenhinweis: {balance} verbleibend","Ihr aktuelles Guthaben beträgt {balance}. Bitte laden Sie auf, um Unterbrechungen zu vermeiden."],invoice_rejected:["Rechnung {invoice_no} abgelehnt","Rechnung {invoice_no} wurde abgelehnt. Grund: {reason}"],auto_recharge_succeeded:["Automatische Aufladung erfolgreich","Ihr Konto wurde automatisch mit {amount} aufgeladen."],balanceAfter:" Aktuelles Guthaben: {balance}.",auto_recharge_incomplete:["Automatische Aufladung nicht abgeschlossen","Ein automatischer Aufladeversuch wurde nicht abgeschlossen. Ihre Einstellungen wurden dadurch nicht geändert."],auto_recharge_paused_system:["Automatische Aufladung pausiert","Die automatische Aufladung konnte wegen eines vorübergehenden Verarbeitungsproblems nicht abgeschlossen werden. Sie wurde vorsorglich pausiert; Ihre gespeicherte Karte wurde nicht belastet."],auto_recharge_paused:["Automatische Aufladung fehlgeschlagen","Die automatische Aufladung konnte nicht abgeschlossen werden (Grund: {reason}) und wurde pausiert. Bitte aktualisieren Sie Ihre Zahlungsmethode, bevor Sie sie wieder aktivieren."]},es:{model_launched:["Modelo disponible: {model}","El modelo {model} ya está disponible."],model_retiring:["Retirada de modelo: {model}","El modelo {model} se retirará próximamente."],model_retired:["Modelo retirado: {model}","El modelo {model} dejó de estar disponible el {effective}."],model_price_changed:["Precio actualizado: {model}","El precio de {model} ha cambiado, vigente desde {effective}."],retiredAt:" Fecha de retirada: {retired_at}.",successor:" Reemplazo recomendado: {successor}.",prices:" Entrada: ${input} / 1M tokens, salida: ${output} / 1M tokens.",pricePage:" Consulta la página del modelo para ver el precio actual.",balance_reminder:["Aviso de saldo: quedan {balance}","Tu saldo actual es {balance}. Recarga para evitar interrupciones del servicio."],invoice_rejected:["Factura {invoice_no} rechazada","La factura {invoice_no} fue rechazada. Motivo: {reason}"],auto_recharge_succeeded:["Recarga automática completada","Tu cuenta se recargó automáticamente con {amount}."],balanceAfter:" Saldo actual: {balance}.",auto_recharge_incomplete:["Recarga automática no completada","Un intento reciente de recarga automática no se completó. Tu configuración no ha cambiado."],auto_recharge_paused_system:["Recarga automática pausada","La recarga automática no se completó por un problema temporal de procesamiento. Se pausó por precaución y no se cobró a tu tarjeta guardada."],auto_recharge_paused:["Recarga automática fallida","La recarga automática no se completó (motivo: {reason}) y se ha pausado. Actualiza tu método de pago antes de volver a activarla."]},fr:{model_launched:["Modèle disponible : {model}","Le modèle {model} est désormais disponible."],model_retiring:["Retrait de modèle : {model}","Le modèle {model} sera bientôt retiré."],model_retired:["Modèle retiré : {model}","Le modèle {model} n'est plus disponible depuis le {effective}."],model_price_changed:["Tarif mis à jour : {model}","Le tarif de {model} a changé, en vigueur à partir du {effective}."],retiredAt:" Date de retrait : {retired_at}.",successor:" Remplacement recommandé : {successor}.",prices:" Entrée : ${input} / 1M tokens, sortie : ${output} / 1M tokens.",pricePage:" Consultez la page du modèle pour le tarif actuel.",balance_reminder:["Alerte de solde : {balance} restant","Votre solde actuel est de {balance}. Rechargez pour éviter une interruption de service."],invoice_rejected:["Facture {invoice_no} refusée","La facture {invoice_no} a été refusée. Motif : {reason}"],auto_recharge_succeeded:["Recharge automatique effectuée","Votre compte a été rechargé automatiquement de {amount}."],balanceAfter:" Solde actuel : {balance}.",auto_recharge_incomplete:["Recharge automatique non terminée","Une tentative récente de recharge automatique n'a pas abouti. Vos paramètres n'ont pas été modifiés."],auto_recharge_paused_system:["Recharge automatique suspendue","La recharge automatique n'a pas abouti en raison d'un problème de traitement temporaire. Elle a été suspendue par précaution et votre carte enregistrée n'a pas été débitée."],auto_recharge_paused:["Échec de la recharge automatique","La recharge automatique n'a pas abouti (motif : {reason}) et a été suspendue. Mettez à jour votre moyen de paiement avant de la réactiver."]},pt:{model_launched:["Modelo disponível: {model}","O modelo {model} já está disponível."],model_retiring:["Descontinuação de modelo: {model}","O modelo {model} será descontinuado em breve."],model_retired:["Modelo descontinuado: {model}","O modelo {model} deixou de estar disponível em {effective}."],model_price_changed:["Preço atualizado: {model}","O preço de {model} mudou, válido a partir de {effective}."],retiredAt:" Data de descontinuação: {retired_at}.",successor:" Substituto recomendado: {successor}.",prices:" Entrada: ${input} / 1M tokens, saída: ${output} / 1M tokens.",pricePage:" Consulte a página do modelo para ver o preço atual.",balance_reminder:["Aviso de saldo: restam {balance}","Seu saldo atual é {balance}. Recarregue para evitar interrupção do serviço."],invoice_rejected:["Fatura {invoice_no} rejeitada","A fatura {invoice_no} foi rejeitada. Motivo: {reason}"],auto_recharge_succeeded:["Recarga automática concluída","Sua conta foi recarregada automaticamente com {amount}."],balanceAfter:" Saldo atual: {balance}.",auto_recharge_incomplete:["Recarga automática não concluída","Uma tentativa recente de recarga automática não foi concluída. Suas configurações não foram alteradas."],auto_recharge_paused_system:["Recarga automática pausada","A recarga automática não foi concluída devido a um problema temporário de processamento. Ela foi pausada por precaução e seu cartão salvo não foi cobrado."],auto_recharge_paused:["Falha na recarga automática","A recarga automática não foi concluída (motivo: {reason}) e foi pausada. Atualize sua forma de pagamento antes de reativá-la."]},ru:{model_launched:["Модель доступна: {model}","Модель {model} теперь доступна."],model_retiring:["Вывод модели: {model}","Модель {model} скоро будет выведена из эксплуатации."],model_retired:["Модель выведена: {model}","Модель {model} недоступна с {effective}."],model_price_changed:["Изменение цены: {model}","Цена {model} изменилась, действует с {effective}."],retiredAt:" Время вывода: {retired_at}.",successor:" Рекомендуемая замена: {successor}.",prices:" Ввод: ${input} / 1 млн токенов, вывод: ${output} / 1 млн токенов.",pricePage:" Актуальные цены — на странице модели.",balance_reminder:["Баланс: осталось {balance}","Текущий баланс вашего аккаунта — {balance}. Пополните его, чтобы избежать перерыва в работе."],invoice_rejected:["Счёт {invoice_no} отклонён","Счёт {invoice_no} отклонён. Причина: {reason}"],auto_recharge_succeeded:["Автопополнение выполнено","Ваш аккаунт автоматически пополнен на {amount}."],balanceAfter:" Текущий баланс: {balance}.",auto_recharge_incomplete:["Автопополнение не завершено","Недавняя попытка автопополнения не завершилась. Ваши настройки не изменились."],auto_recharge_paused_system:["Автопополнение приостановлено","Автопополнение не удалось из-за временной ошибки обработки. Оно приостановлено в целях предосторожности, списания с сохранённой карты не было."],auto_recharge_paused:["Автопополнение не удалось","Автопополнение не удалось (причина: {reason}) и приостановлено. Обновите способ оплаты перед повторным включением."]},tr:{model_launched:["Model kullanıma sunuldu: {model}","{model} modeli artık kullanılabilir."],model_retiring:["Model kullanımdan kalkıyor: {model}","{model} modeli yakında kullanımdan kaldırılacak."],model_retired:["Model kullanımdan kalktı: {model}","{model} modeli {effective} itibarıyla kullanılamıyor."],model_price_changed:["Fiyat güncellendi: {model}","{model} fiyatı değişti, {effective} itibarıyla geçerli."],retiredAt:" Kaldırılma zamanı: {retired_at}.",successor:" Önerilen alternatif: {successor}.",prices:" Girdi: ${input} / 1M token, çıktı: ${output} / 1M token.",pricePage:" Güncel fiyat için model sayfasına bakın.",balance_reminder:["Bakiye uyarısı: {balance} kaldı","Mevcut hesap bakiyeniz {balance}. Hizmet kesintisini önlemek için lütfen yükleme yapın."],invoice_rejected:["{invoice_no} numaralı fatura reddedildi","{invoice_no} numaralı fatura reddedildi. Neden: {reason}"],auto_recharge_succeeded:["Otomatik yükleme başarılı","Hesabınıza otomatik olarak {amount} yüklendi."],balanceAfter:" Mevcut bakiye: {balance}.",auto_recharge_incomplete:["Otomatik yükleme tamamlanmadı","Son otomatik yükleme denemesi tamamlanmadı. Ayarlarınız değiştirilmedi."],auto_recharge_paused_system:["Otomatik yükleme duraklatıldı","Geçici bir işlem sorunu nedeniyle otomatik yükleme tamamlanamadı. Önlem olarak duraklatıldı ve kayıtlı kartınızdan ücret alınmadı."],auto_recharge_paused:["Otomatik yükleme başarısız","Otomatik yükleme tamamlanamadı (neden: {reason}) ve duraklatıldı. Yeniden etkinleştirmeden önce ödeme yönteminizi güncelleyin."]},pl:{model_launched:["Model dostępny: {model}","Model {model} jest już dostępny."],model_retiring:["Wycofanie modelu: {model}","Model {model} zostanie wkrótce wycofany."],model_retired:["Model wycofany: {model}","Model {model} jest niedostępny od {effective}."],model_price_changed:["Zmiana ceny: {model}","Cena modelu {model} uległa zmianie, obowiązuje od {effective}."],retiredAt:" Termin wycofania: {retired_at}.",successor:" Zalecany zamiennik: {successor}.",prices:" Wejście: ${input} / 1 mln tokenów, wyjście: ${output} / 1 mln tokenów.",pricePage:" Aktualne ceny znajdziesz na stronie modelu.",balance_reminder:["Alert salda: pozostało {balance}","Twoje obecne saldo to {balance}. Doładuj konto, aby uniknąć przerwy w działaniu usługi."],invoice_rejected:["Faktura {invoice_no} odrzucona","Faktura {invoice_no} została odrzucona. Powód: {reason}"],auto_recharge_succeeded:["Automatyczne doładowanie wykonane","Twoje konto zostało automatycznie doładowane kwotą {amount}."],balanceAfter:" Obecne saldo: {balance}.",auto_recharge_incomplete:["Automatyczne doładowanie nieukończone","Ostatnia próba automatycznego doładowania nie została ukończona. Twoje ustawienia nie zostały zmienione."],auto_recharge_paused_system:["Automatyczne doładowanie wstrzymane","Automatyczne doładowanie nie powiodło się z powodu tymczasowego problemu. Zostało wstrzymane zapobiegawczo, a zapisana karta nie została obciążona."],auto_recharge_paused:["Automatyczne doładowanie nieudane","Automatyczne doładowanie nie powiodło się (powód: {reason}) i zostało wstrzymane. Zaktualizuj metodę płatności przed ponownym włączeniem."]},nl:{model_launched:["Model beschikbaar: {model}","Het model {model} is nu beschikbaar."],model_retiring:["Model wordt uitgefaseerd: {model}","Het model {model} wordt binnenkort uitgefaseerd."],model_retired:["Model uitgefaseerd: {model}","Het model {model} is sinds {effective} niet meer beschikbaar."],model_price_changed:["Prijs gewijzigd: {model}","De prijs van {model} is gewijzigd, geldig vanaf {effective}."],retiredAt:" Uitfaseringsmoment: {retired_at}.",successor:" Aanbevolen vervanger: {successor}.",prices:" Invoer: ${input} / 1M tokens, uitvoer: ${output} / 1M tokens.",pricePage:" Bekijk de modelpagina voor de actuele prijs.",balance_reminder:["Saldomelding: nog {balance}","Je huidige saldo is {balance}. Waardeer op om onderbreking van de dienst te voorkomen."],invoice_rejected:["Factuur {invoice_no} afgewezen","Factuur {invoice_no} is afgewezen. Reden: {reason}"],auto_recharge_succeeded:["Automatisch opwaarderen gelukt","Je account is automatisch opgewaardeerd met {amount}."],balanceAfter:" Huidig saldo: {balance}.",auto_recharge_incomplete:["Automatisch opwaarderen niet voltooid","Een recente poging tot automatisch opwaarderen is niet voltooid. Je instellingen zijn niet gewijzigd."],auto_recharge_paused_system:["Automatisch opwaarderen gepauzeerd","Automatisch opwaarderen is mislukt door een tijdelijk verwerkingsprobleem. Het is uit voorzorg gepauzeerd en je opgeslagen kaart is niet belast."],auto_recharge_paused:["Automatisch opwaarderen mislukt","Automatisch opwaarderen is mislukt (reden: {reason}) en gepauzeerd. Werk je betaalmethode bij voordat je het weer inschakelt."]},it:{model_launched:["Modello disponibile: {model}","Il modello {model} è ora disponibile."],model_retiring:["Ritiro del modello: {model}","Il modello {model} verrà ritirato a breve."],model_retired:["Modello ritirato: {model}","Il modello {model} non è più disponibile dal {effective}."],model_price_changed:["Prezzo aggiornato: {model}","Il prezzo di {model} è cambiato, valido dal {effective}."],retiredAt:" Data di ritiro: {retired_at}.",successor:" Sostituto consigliato: {successor}.",prices:" Input: ${input} / 1M token, output: ${output} / 1M token.",pricePage:" Consulta la pagina del modello per il prezzo attuale.",balance_reminder:["Avviso saldo: restano {balance}","Il saldo attuale del tuo account è {balance}. Ricarica per evitare interruzioni del servizio."],invoice_rejected:["Fattura {invoice_no} respinta","La fattura {invoice_no} è stata respinta. Motivo: {reason}"],auto_recharge_succeeded:["Ricarica automatica completata","Il tuo account è stato ricaricato automaticamente di {amount}."],balanceAfter:" Saldo attuale: {balance}.",auto_recharge_incomplete:["Ricarica automatica non completata","Un recente tentativo di ricarica automatica non è stato completato. Le tue impostazioni non sono cambiate."],auto_recharge_paused_system:["Ricarica automatica sospesa","La ricarica automatica non è stata completata per un problema temporaneo. È stata sospesa per precauzione e la carta salvata non è stata addebitata."],auto_recharge_paused:["Ricarica automatica non riuscita","La ricarica automatica non è riuscita (motivo: {reason}) ed è stata sospesa. Aggiorna il metodo di pagamento prima di riattivarla."]},vi:{model_launched:["Mô hình mới: {model}","Mô hình {model} hiện đã khả dụng."],model_retiring:["Sắp ngừng mô hình: {model}","Mô hình {model} sắp ngừng cung cấp."],model_retired:["Đã ngừng mô hình: {model}","Mô hình {model} không còn khả dụng từ {effective}."],model_price_changed:["Cập nhật giá: {model}","Giá của {model} đã thay đổi, áp dụng từ {effective}."],retiredAt:" Thời điểm ngừng: {retired_at}.",successor:" Mô hình thay thế đề xuất: {successor}.",prices:" Đầu vào: ${input} / 1 triệu token, đầu ra: ${output} / 1 triệu token.",pricePage:" Xem giá hiện tại trên trang mô hình.",balance_reminder:["Cảnh báo số dư: còn {balance}","Số dư tài khoản hiện tại của bạn là {balance}. Vui lòng nạp thêm để tránh gián đoạn dịch vụ."],invoice_rejected:["Hóa đơn {invoice_no} bị từ chối","Hóa đơn {invoice_no} đã bị từ chối. Lý do: {reason}"],auto_recharge_succeeded:["Tự động nạp tiền thành công","Tài khoản của bạn đã được tự động nạp {amount}."],balanceAfter:" Số dư hiện tại: {balance}.",auto_recharge_incomplete:["Tự động nạp tiền chưa hoàn tất","Lần tự động nạp tiền gần đây chưa hoàn tất. Cài đặt của bạn không thay đổi."],auto_recharge_paused_system:["Đã tạm dừng tự động nạp tiền","Tự động nạp tiền không hoàn tất do sự cố xử lý tạm thời. Tính năng đã được tạm dừng để phòng ngừa và thẻ đã lưu của bạn không bị trừ tiền."],auto_recharge_paused:["Tự động nạp tiền thất bại","Tự động nạp tiền không hoàn tất (lý do: {reason}) và đã bị tạm dừng. Vui lòng cập nhật phương thức thanh toán trước khi bật lại."]},uk:{model_launched:["Модель доступна: {model}","Модель {model} тепер доступна."],model_retiring:["Виведення моделі: {model}","Модель {model} незабаром буде виведено з експлуатації."],model_retired:["Модель виведено: {model}","Модель {model} недоступна з {effective}."],model_price_changed:["Зміна ціни: {model}","Ціна {model} змінилася, діє з {effective}."],retiredAt:" Час виведення: {retired_at}.",successor:" Рекомендована заміна: {successor}.",prices:" Вхід: ${input} / 1 млн токенів, вихід: ${output} / 1 млн токенів.",pricePage:" Актуальні ціни — на сторінці моделі.",balance_reminder:["Баланс: залишилось {balance}","Поточний баланс вашого акаунта — {balance}. Поповніть його, щоб уникнути перерви в роботі."],invoice_rejected:["Рахунок {invoice_no} відхилено","Рахунок {invoice_no} відхилено. Причина: {reason}"],auto_recharge_succeeded:["Автопоповнення виконано","Ваш акаунт автоматично поповнено на {amount}."],balanceAfter:" Поточний баланс: {balance}.",auto_recharge_incomplete:["Автопоповнення не завершено","Нещодавня спроба автопоповнення не завершилася. Ваші налаштування не змінилися."],auto_recharge_paused_system:["Автопоповнення призупинено","Автопоповнення не вдалося через тимчасову помилку обробки. Його призупинено з міркувань безпеки, списання зі збереженої картки не було."],auto_recharge_paused:["Автопоповнення не вдалося","Автопоповнення не вдалося (причина: {reason}) і призупинено. Оновіть спосіб оплати перед повторним увімкненням."]},bg:{model_launched:["Наличен модел: {model}","Моделът {model} вече е наличен."],model_retiring:["Спиране на модел: {model}","Моделът {model} скоро ще бъде спрян."],model_retired:["Спрян модел: {model}","Моделът {model} не е наличен от {effective}."],model_price_changed:["Промяна на цената: {model}","Цената на {model} е променена, в сила от {effective}."],retiredAt:" Време на спиране: {retired_at}.",successor:" Препоръчан заместител: {successor}.",prices:" Вход: ${input} / 1 млн токена, изход: ${output} / 1 млн токена.",pricePage:" Вижте актуалната цена на страницата на модела.",balance_reminder:["Известие за баланс: остават {balance}","Текущият баланс на акаунта ви е {balance}. Моля, заредете, за да избегнете прекъсване на услугата."],invoice_rejected:["Фактура {invoice_no} е отхвърлена","Фактура {invoice_no} е отхвърлена. Причина: {reason}"],auto_recharge_succeeded:["Автоматичното зареждане е успешно","Акаунтът ви беше автоматично зареден с {amount}."],balanceAfter:" Текущ баланс: {balance}.",auto_recharge_incomplete:["Автоматичното зареждане не завърши","Скорошен опит за автоматично зареждане не завърши. Настройките ви не са променени."],auto_recharge_paused_system:["Автоматичното зареждане е спряно","Автоматичното зареждане не завърши поради временен проблем при обработката. То е спряно предпазно и запазената ви карта не е таксувана."],auto_recharge_paused:["Неуспешно автоматично зареждане","Автоматичното зареждане не завърши (причина: {reason}) и е спряно. Обновете начина на плащане, преди да го включите отново."]}},Vo=(e,t)=>e.replace(/\{(\w+)\}/g,(n,r)=>t[r]??""),LN={model_launched:["model"],model_retiring:["model"],model_retired:["model","effective"],model_price_changed:["model","effective"],balance_reminder:["balance"],invoice_rejected:["invoice_no","reason"],auto_recharge_succeeded:["amount"],auto_recharge_paused:["reason"]};function _N(e,t,n){const r=Gl[n]||Gl.en,o=r[e];if(!o||(LN[e]||[]).some(l=>!t[l]))return null;let a=Vo(o[1],t);return e==="model_retiring"&&t.retired_at&&(a+=Vo(r.retiredAt,t)),e==="model_price_changed"&&(a+=t.input?Vo(r.prices,t):r.pricePage),(e==="model_retiring"||e==="model_retired")&&t.successor&&(a+=Vo(r.successor,t)),e==="auto_recharge_succeeded"&&t.balance&&(a+=Vo(r.balanceAfter,t)),{title:Vo(o[0],t),body:a}}function W5(e,t){if(!e||!Gl.en[e])return null;const n={},r={};for(const o of Object.keys(Gl)){const a=_N(e,t||{},o);if(!a)return null;n[o]=a.title,r[o]=a.body}return{title:n,body:r}}const 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