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Llama Models

53 modelsGeneral models from $0.2/M inputUp to 1.05M context

Usage

Last 29 days · 2026-08-10 to 2026-09-07

Tokens

725M

Requests

22.3K

Models in use

27 of 53

Tokens per day, stacked by model

0262M524M08-1008-1708-2408-3109-072026-08-10 — 524,070,090 tokens llama-4-maverick: 523,920,695 llama-4-scout: 132,535 6 more models: 5,995 llama-3.1-70b: 5,285 llama-3.3-70b: 5,075 groq-llama-3.3-70b-versatile: 300 deepinfra-llama-4-scout-17b-16e-instruct: 2052026-08-11 — 1,530,430 tokens groq-llama-3.3-70b-versatile: 1,516,465 llama-4-maverick: 8,020 llama-4-scout: 5,875 deepinfra-llama-4-scout-17b-16e-instruct: 702026-08-12 — 496,225 tokens llama-4-scout: 495,880 llama-4-maverick: 275 deepinfra-llama-4-scout-17b-16e-instruct: 702026-08-13 — 140 tokens llama-4-maverick: 70 deepinfra-llama-4-scout-17b-16e-instruct: 702026-08-14 — 445 tokens llama-4-scout: 210 llama-4-maverick: 165 deepinfra-llama-4-scout-17b-16e-instruct: 702026-08-15 — 1,865,825 tokens llama-3.1-70b: 1,037,115 llama-4-maverick: 385,325 deepinfra-llama-4-scout-17b-16e-instruct: 315,270 llama-4-scout: 126,325 6 more models: 1,235 groq-llama-3.3-70b-versatile: 235 llama-3.3-70b: 200 deepinfra-llama-4-maverick-17b-128e-instruct: 1202026-08-16 — 545,425 tokens llama-3.1-70b: 531,420 llama-4-scout: 14,0052026-08-17 — 46,345 tokens llama-4-maverick: 46,260 llama-4-scout: 852026-08-18 — 216,615 tokens llama-4-maverick: 202,925 llama-4-scout: 13,6902026-08-19 — 39,160 tokens llama-4-scout: 38,830 llama-4-maverick: 3302026-08-20 — 63,550 tokens llama-4-scout: 62,945 llama-4-maverick: 370 deepinfra-llama-4-maverick-17b-128e-instruct: 2352026-08-21 — 3,697,940 tokens llama-4-maverick: 2,842,810 llama-4-scout: 825,665 llama-3.3-70b: 29,320 deepinfra-llama-4-maverick-17b-128e-instruct: 1452026-08-22 — 12,165 tokens llama-4-scout: 11,845 llama-4-maverick: 3202026-08-23 — 745,155 tokens llama-3.3-70b: 380,670 llama-3.1-70b: 362,400 llama-4-scout: 1,915 llama-4-maverick: 95 6 more models: 752026-08-24 — 210,595 tokens llama-4-scout: 207,965 llama-4-maverick: 2,6302026-08-25 — 2,835 tokens llama-4-maverick: 2,8352026-08-26 — 33,960 tokens llama-4-maverick: 32,540 llama-4-scout: 1,4202026-08-27 — 67,570 tokens llama-4-scout: 43,605 llama-3.3-70b: 23,9652026-08-28 — 19,550 tokens llama-4-scout: 18,890 llama-4-maverick: 470 llama-3.1-70b: 1902026-08-29 — 2,161,830 tokens llama-4-scout: 2,140,210 llama-3.3-70b: 21,235 6 more models: 3852026-08-30 — 35,247,495 tokens llama-4-scout: 35,159,930 deepinfra-llama-4-maverick-17b-128e-instruct: 44,715 deepinfra-llama-4-scout-17b-16e-instruct: 42,605 llama-4-maverick: 2452026-08-31 — 113,945,485 tokens llama-3.3-70b: 74,103,345 llama-4-scout: 39,719,140 llama-4-maverick: 122,010 6 more models: 535 llama-3.1-70b: 190 deepinfra-llama-4-scout-17b-16e-instruct: 135 deepinfra-llama-4-maverick-17b-128e-instruct: 1302026-09-01 — 135,135 tokens llama-4-maverick: 110,085 llama-4-scout: 22,980 llama-3.1-70b: 1,040 6 more models: 585 llama-3.3-70b: 190 deepinfra-llama-4-scout-17b-16e-instruct: 135 deepinfra-llama-4-maverick-17b-128e-instruct: 1202026-09-02 — 168,575 tokens llama-4-scout: 168,405 llama-4-maverick: 1702026-09-03 — 5,571,285 tokens llama-3.3-70b: 5,557,400 llama-4-scout: 13,585 llama-4-maverick: 3002026-09-04 — 13,639,210 tokens llama-4-scout: 13,639,020 llama-4-maverick: 1902026-09-05 — 345 tokens llama-4-scout: 220 llama-3.3-70b-instruct: 65 llama-4-maverick: 602026-09-06 — 19,971,790 tokens llama-4-scout: 19,776,165 llama-3.3-70b-instruct: 193,020 llama-4-maverick: 1,215 6 more models: 680 deepinfra-llama-4-scout-17b-16e-instruct: 240 llama-3.3-70b: 195 llama-3.1-70b: 145 deepinfra-llama-4-maverick-17b-128e-instruct: 1302026-09-07 — 132,025 tokens llama-4-maverick: 93,765 llama-4-scout: 33,200 llama-3.3-70b: 4,915 llama-3.3-70b-instruct: 145
  • llama-4-maverick
  • llama-4-scout
  • llama-3.3-70b
  • llama-3.1-70b
  • groq-llama-3.3-70b-versatile
  • deepinfra-llama-4-scout-17b-16e-instruct
  • llama-3.3-70b-instruct
  • deepinfra-llama-4-maverick-17b-128e-instruct
  • 6 more models

Which models that traffic went to

  1. Llama 4 Maverick72.8%528M
  2. Llama 4 Scout15.5%113M
  3. Llama 3.3 70B11.1%80.1M
  4. Llama 3.1 70B0.3%1.9M
  5. Groq Llama 3.3 70B Versatile0.2%1.5M
  6. Deepinfra Llama 4 Scout 17B 16e Instruct<0.1%359K
  7. Llama 3.3 70B Instruct<0.1%193K
  8. Deepinfra Llama 4 Maverick 17B 128e Instruct<0.1%45.6K
  9. 6 more models<0.1%9.5K

Share of 725M tokens. 13 models with traffic report no token counts and cannot be ranked here, including qianfan-llama-vl-8b and deepseek-r1-distill-qianfan-llama-8b — they are in the request view.

The two views disagree on purpose: a model can take a large share of the calls and a small share of the tokens — many short requests — or the reverse. Which one matters depends on whether your cost is driven by call volume or by prompt length. Measured on AIHubMix over the last 29 days, counting the 53 model IDs listed on this page; traffic routed through upstream-specific IDs that are not in the public catalog is not included.

All 53 Llama Models

Open in model list
Llama models on AIHubMix with input and output modalities, context length, maximum output, price per million tokens including cache read and cache write rates, and measured throughput and latency.
Modalities
llama-4-maverickTakes text, vision, returns text.1.05M32K$0.2$0.2/M98 tok/s0.23 s
llama-3.3-70b-instructTakes text, returns text.131K—$0.6$1.2/M14 tok/s1.91 s
llama-4-scoutTakes text, vision, returns text.131K—$0.2$0.2/M2637 tok/s0.29 s
llama-3.3-70b66K8K$0.6$0.6/M3250 tok/s0.25 s
llama3-groq-8b-8192-tool-use-preview——$0.00019$0.00019/M——
llama3-groq-70b-8192-tool-use-preview——$0.00089$0.00089/M——
meta-llama/llama-3.1-405b-instruct:free——$0.02$0.02/M——
meta-llama/llama-3.1-70b-instruct:free——$0.02$0.02/M——
meta-llama/llama-3.1-8b-instruct:free——$0.02$0.02/M——
meta-llama/llama-3.2-11b-vision-instruct:free——$0.02$0.02/M——
meta-llama/llama-3.2-3b-instruct:free——$0.02$0.02/M——
deepinfra-llama-3.1-8b-instant——$0.033$0.055/M——
groq-llama-3.1-8b-instant——$0.055$0.088/M——
llama3-8b-8192——$0.06$0.12/M——
deepinfra-llama-4-scout-17b-16e-instruct——$0.088$0.33/M——
llama2-7b-2048——$0.1$0.1/M——
deepinfra-llama-3.3-70b-instant-turbo——$0.11$0.352/M——
groq-llama-4-scout-17b-16e-instruct——$0.122$0.366/M——
deepseek-r1-distill-qianfan-llama-8b——$0.137$0.548/M——
llama-3.2-11b-vision-preview——$0.2$0.2/M——
llama-3.2-1b-preview——$0.2$0.2/M——
llama-3.2-3b-preview——$0.2$0.2/M——
groq-llama-4-maverick-17b-128e-instruct——$0.22$0.66/M——
qianfan-llama-vl-8b——$0.274$0.685/M——
aihubmix-Llama-3-1-8B-Instruct——$0.3$0.6/M——
llama-3.1-8b-instant——$0.3$0.6/M——
llama3-8b-8192(33)——$0.3$0.3/M——
llama3.1-8b——$0.3$0.6/M107 tok/s0.17 s
meta/llama3-8B-chat——$0.3$0.3/M——
deepinfra-llama-4-maverick-17b-128e-instruct——$0.33$1.32/M——
aihubmix-Llama-3-2-11B-Vision——$0.4$0.4/M——
Gryphe/MythoMax-L2-13b——$0.4$0.4/M——
llama-3.1-70b——$0.44$0.44/M12 tok/s0.30 s
llama2-70b-4096Takes , returns text.——$0.5$0.5/M——
llama2-70b-40960Takes , returns text.——$0.5$0.5/M——
meta-llama/Llama-3.2-90B-Vision-Instruct——$0.5$0.5/M——
meta-llama-3-8b——$0.548$0.548/M——
aihubmix-Llama-3-1-70B-Instruct——$0.6$0.78/M——
cerebras-llama-3.3-70b——$0.6$0.6/M——
llama-3.1-70b-versatile——$0.6$0.6/M——
groq-llama-3.3-70b-versatile——$0.649$0.869/M——
aihubmix-Llama-3-70B-Instruct——$0.7$0.7/M——
llama3-70b-8192——$0.7$0.9373/M——
qianfan-chinese-llama-2-13b——$0.822$0.822/M——
WizardLM/WizardCoder-Python-34B-V1.0——$0.9$0.9/M——
aihubmix-Llama-3-2-90B-Vision——$2.4$2.4/M——
llama-3.2-90b-vision-preview——$2.4$2.4/M——
llama3-70b-8192(33)——$2.65$2.65/M——
llama-3.1-405b-instruct——$4$4/M——
llama-3.1-405b-reasoning——$4$4/M——
meta-llama-3-70b——$4.795$4.795/M——
aihubmix-Llama-3-1-405B-Instruct——$5$15/M——
meta/llama-3.1-405b-instruct——$5$5/M——

Prices are USD per million tokens; cache read and cache write are the rates for prompt-cache hits and for writing a prompt into the cache. Throughput and latency are measured on AIHubMix — the same figures the model detail page shows — not vendor claims. A dash means the catalog does not publish that field for that model, which is not the same as the model not supporting it.

Common Comparisons

llama-3.3-70b-instruct vs llama-4-maverickllama-3.3-70b-instruct vs llama-4-scoutllama-3.3-70b-instruct vs llama2-70b-4096llama-3.3-70b-instruct vs qwen3.8-max-previewkimi-k3 vs llama-3.3-70b-instruct

Llama on AIHubMix

Which Llama model should I start with?

llama-4-maverick at $0.2/M input — the cheapest entry here that declares tool calling, and it carries a 1.05M context. Move up to aihubmix-Llama-3-1-405B-Instruct when answer quality matters more than cost.

Why are there several entries for the same model?

Because each row is a route you can call, not a model release. Some IDs name an upstream (azure-, alicloud-, cc-), some are the open-weight repository form (meta-llama/…), and some differ only in capitalisation, kept so older integrations keep working.

The catalog does not carry a field saying which of those a given row is, so this page does not sort them into buckets it would have to invent. Every row shows that route’s own price, context and speed — compare those directly, and open a model to see the upstreams that serve it.

Do I need a separate Llama account?

No. One AIHubMix key covers every model on this page, and switching between them is a change to the model string — billing, rate limits, and logs stay in one place.

Other Model Authors

See all model authors →
Qwen
144 modelsOpenAI
134 modelsGoogle
85 modelsZ.AI
71 modelsByteDance
46 models

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