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ant-pro-page-container-children-container" style="padding-inline:0;padding-block-end:0;padding-block-start:40px"><div class="sc-1c0xtl9-0 kMgEVL"><div class="sc-1c0xtl9-8 jsOPmD"><div class="sc-1c0xtl9-9 cslRFF"><div class="sc-1c0xtl9-1 jpnGFj"><nav class="sc-1c0xtl9-2 feFHco"><a href="/blog" data-discover="true">Blog</a> › <a href="/blog/tag/opinion" data-discover="true">Opinion</a></nav></div><h1 class="sc-1c0xtl9-3 Bwa-Do">DeepSeek V4.1 Flash 1017 vs Claude Haiku 5.5: Which Cheap Model for Terminal Coding Agents?</h1><div class="sc-1c0xtl9-5 gGIGKA"><span>AIHubMix</span><time dateTime="2026-10-19T05:34:24.000+00:00">October 19, 2026</time><span>7 min read</span></div><img src="https://aihubmix.com/blog-media/images/2026/10/e9257d71d96bd665.png" alt="DeepSeek V4.1 Flash 1017 vs Claude Haiku 5.5: Which Cheap Model for Terminal Coding Agents?" fetchPriority="high" class="sc-1c0xtl9-6 dHXlSg"/><article data-blog-article="true" class="sc-1c0xtl9-7 jonNQo"><p>Ten days apart, the two cheapest serious agent models of October 2026 arrived from opposite directions. Anthropic shipped Claude Haiku 5.5 on October 7 at $0.10 input and $0.50 output per 1M tokens, pitched as the subagent that sits under Sonnet and Opus. DeepSeek answered on October 17 with V4.1 Flash 1017, an update aimed squarely at end-to-end coding work.</p>
<p>The decision comes down to one task: a terminal coding session in Claude Code, where the agent reads files, runs tests, and edits code over dozens of turns. On that task:</p>
<ul>
<li><strong>Capability is close, with a small edge to 1017.</strong> The one benchmark both vendors report for it, Terminal-Bench 4.0, has 1017 at 42.3 and Haiku 5.5 at 39.2.</li>
<li><strong>Cost depends on context length.</strong> Under 100K tokens per request, Haiku 5.5 costs about the same as 1017 off-peak and half of 1017 at peak. Past 100K, Haiku bills the whole request at five times the rate, and 1017 becomes the cheaper model by a wide margin.</li>
<li><strong>Haiku wins elsewhere:</strong> knowledge questions, PDF input, computer use, and prompt-injection resistance.</li>
</ul>
<h2 id="why-this-pair">Why this pair</h2>
<p>Both are the cheapest agent-capable tier from their vendors, both launched in the same fortnight, and both run inside Claude Code. Anthropic positions Haiku 5.5 as a coding subagent and made it Claude Code's default <code>haiku</code> model on its API. DeepSeek ships an Anthropic-format endpoint and measured 1017's Terminal-Bench 4.0 score inside the Claude Code harness. Search interest follows the same line: comparison pages for this exact pair appeared within days of the 1017 release.</p>
<p>Gemini 3.8 Flash is the other name in the conversation, and it appears in DeepSeek's own cost chart. It launched in early September at $0.75 input and $3.75 output, several times the price of either model here, so it stays out of this comparison.</p>
<h2 id="the-task-a-terminal-coding-session-in-claude-code">The task: a terminal coding session in Claude Code</h2>
<p>The benchmark that matches this task is Terminal-Bench 4.0: long shell sessions with real tools, scored on whether the agent finishes the job. The figures below are vendor-reported: DeepSeek's from its <a href="https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash-1017">model card</a>, Anthropic's from the <a href="https://www.anthropic.com/news/claude-haiku-5-5">Haiku 5.5 announcement</a>. Nobody has run both under the same harness and settings yet.</p>
<div class="blog-table-scroll"><table style="display: table; margin-left: auto; margin-right: auto; border-collapse: collapse; background-color: #ffffff; color: #333333;">
  <thead>
    <tr>
      <th style="text-align: left; padding: 10px 12px; background-color: #ffffff; color: #333333; border: 1px solid #d0d0d0;">Model</th>
      <th style="text-align: left; padding: 10px 12px; background-color: #ffffff; color: #333333; border: 1px solid #d0d0d0;">Terminal-Bench 4.0</th>
      <th style="text-align: left; padding: 10px 12px; background-color: #ffffff; color: #333333; border: 1px solid #d0d0d0;">Reported by</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td style="padding: 10px 12px; background-color: #ffffff; color: #333333; border: 1px solid #d0d0d0;">Claude Sonnet 5.5</td>
      <td style="padding: 10px 12px; background-color: #ffffff; color: #333333; border: 1px solid #d0d0d0;">70.6</td>
      <td style="padding: 10px 12px; background-color: #ffffff; color: #333333; border: 1px solid #d0d0d0;">Anthropic</td>
    </tr>
    <tr>
      <td style="padding: 10px 12px; background-color: #ffffff; color: #333333; border: 1px solid #d0d0d0;">DeepSeek V4.1 Flash 1017</td>
      <td style="padding: 10px 12px; background-color: #ffffff; color: #333333; border: 1px solid #d0d0d0;">42.3</td>
      <td style="padding: 10px 12px; background-color: #ffffff; color: #333333; border: 1px solid #d0d0d0;">DeepSeek</td>
    </tr>
    <tr>
      <td style="padding: 10px 12px; background-color: #ffffff; color: #333333; border: 1px solid #d0d0d0;">Claude Haiku 5.5</td>
      <td style="padding: 10px 12px; background-color: #ffffff; color: #333333; border: 1px solid #d0d0d0;">39.2</td>
      <td style="padding: 10px 12px; background-color: #ffffff; color: #333333; border: 1px solid #d0d0d0;">Anthropic</td>
    </tr>
    <tr>
      <td style="padding: 10px 12px; background-color: #ffffff; color: #333333; border: 1px solid #d0d0d0;">DeepSeek V4.1 Flash (Sept)</td>
      <td style="padding: 10px 12px; background-color: #ffffff; color: #333333; border: 1px solid #d0d0d0;">31.2</td>
      <td style="padding: 10px 12px; background-color: #ffffff; color: #333333; border: 1px solid #d0d0d0;">DeepSeek</td>
    </tr>
  </tbody>
</table></div>
<p>DeepSeek ran Terminal-Bench 4.0 in Claude Code; Anthropic does not state Haiku's effort level in the headline table.</p>
<p>A three-point gap between two vendor numbers with different settings is not a decisive win. Independent Terminal-Bench 4.0 runs of Haiku 5.5 come in lower than Anthropic's figure and disagree with each other, and no independent result for 1017 has been published yet. Read this as "roughly level, 1017 slightly ahead" rather than a ranking. Anthropic's own guidance points the same way: "Sonnet 5.5 and Opus 5.5 remain better choices for complex agentic coding tasks like those measured by Terminal-Bench 4.0." Neither cheap model is the right pick for the hardest shell sessions.</p>
<p>Beyond that one benchmark, the two vendors publish almost no shared numbers. DeepSeek reports DeepSWE v1.1 (77.0) and ProgramBench (29.1); Anthropic reports FrontierCode 1.1 (46.4) and OSWorld 2.1 (72.4). None of those has a published score for the other model.</p>
<h2 id="where-each-one-wins">Where each one wins</h2>
<div class="blog-table-scroll"><table style="display: table; margin-left: auto; margin-right: auto; border-collapse: collapse; background-color: #ffffff; color: #333333;">
  <thead>
    <tr>
      <th style="text-align: left; padding: 10px 12px; background-color: #ffffff; color: #333333; border: 1px solid #d0d0d0;">Area</th>
      <th style="text-align: left; padding: 10px 12px; background-color: #ffffff; color: #333333; border: 1px solid #d0d0d0;">V4.1 Flash 1017</th>
      <th style="text-align: left; padding: 10px 12px; background-color: #ffffff; color: #333333; border: 1px solid #d0d0d0;">Claude Haiku 5.5</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td style="padding: 10px 12px; background-color: #ffffff; color: #333333; border: 1px solid #d0d0d0;">Terminal-Bench 4.0</td>
      <td style="padding: 10px 12px; background-color: #ffffff; color: #333333; border: 1px solid #d0d0d0;">42.3</td>
      <td style="padding: 10px 12px; background-color: #ffffff; color: #333333; border: 1px solid #d0d0d0;">39.2</td>
    </tr>
    <tr>
      <td style="padding: 10px 12px; background-color: #ffffff; color: #333333; border: 1px solid #d0d0d0;">HLE, no tools</td>
      <td style="padding: 10px 12px; background-color: #ffffff; color: #333333; border: 1px solid #d0d0d0;">42.6</td>
      <td style="padding: 10px 12px; background-color: #ffffff; color: #333333; border: 1px solid #d0d0d0;">45.9</td>
    </tr>
    <tr>
      <td style="padding: 10px 12px; background-color: #ffffff; color: #333333; border: 1px solid #d0d0d0;">Context window</td>
      <td style="padding: 10px 12px; background-color: #ffffff; color: #333333; border: 1px solid #d0d0d0;">1M</td>
      <td style="padding: 10px 12px; background-color: #ffffff; color: #333333; border: 1px solid #d0d0d0;">1M</td>
    </tr>
    <tr>
      <td style="padding: 10px 12px; background-color: #ffffff; color: #333333; border: 1px solid #d0d0d0;">Max output</td>
      <td style="padding: 10px 12px; background-color: #ffffff; color: #333333; border: 1px solid #d0d0d0;">384K</td>
      <td style="padding: 10px 12px; background-color: #ffffff; color: #333333; border: 1px solid #d0d0d0;">128K</td>
    </tr>
    <tr>
      <td style="padding: 10px 12px; background-color: #ffffff; color: #333333; border: 1px solid #d0d0d0;">Input types</td>
      <td style="padding: 10px 12px; background-color: #ffffff; color: #333333; border: 1px solid #d0d0d0;">Text, images</td>
      <td style="padding: 10px 12px; background-color: #ffffff; color: #333333; border: 1px solid #d0d0d0;">Text, images, PDF</td>
    </tr>
    <tr>
      <td style="padding: 10px 12px; background-color: #ffffff; color: #333333; border: 1px solid #d0d0d0;">Price above 100K prompt</td>
      <td style="padding: 10px 12px; background-color: #ffffff; color: #333333; border: 1px solid #d0d0d0;">Unchanged</td>
      <td style="padding: 10px 12px; background-color: #ffffff; color: #333333; border: 1px solid #d0d0d0;">5x</td>
    </tr>
    <tr>
      <td style="padding: 10px 12px; background-color: #ffffff; color: #333333; border: 1px solid #d0d0d0;">Effort levels</td>
      <td style="padding: 10px 12px; background-color: #ffffff; color: #333333; border: 1px solid #d0d0d0;">3</td>
      <td style="padding: 10px 12px; background-color: #ffffff; color: #333333; border: 1px solid #d0d0d0;">5</td>
    </tr>
    <tr>
      <td style="padding: 10px 12px; background-color: #ffffff; color: #333333; border: 1px solid #d0d0d0;">Open weights</td>
      <td style="padding: 10px 12px; background-color: #ffffff; color: #333333; border: 1px solid #d0d0d0;">Yes, MIT</td>
      <td style="padding: 10px 12px; background-color: #ffffff; color: #333333; border: 1px solid #d0d0d0;">No</td>
    </tr>
  </tbody>
</table></div>
<p>Vendor-reported benchmark figures. DeepSeek's effort levels are low, high, and max; Haiku's are low, medium (default), high, xhigh, and max.</p>
<p><strong>1017 is stronger for</strong> long sessions (no long-context surcharge, three times the output ceiling), terminal and program-building work, and teams that want open weights as a fallback.</p>
<p><strong>Haiku 5.5 is stronger for</strong> knowledge-heavy steps, PDF and document work, computer use (72.4 on OSWorld 2.1, which DeepSeek does not report), and agents that read untrusted content: ComputingForGeeks cites Gray Swan coding scenarios where prompt-injection success fell from 58.4% on Haiku 4.5 to 0.08%. It also runs on Anthropic's native protocol in Claude Code, with no translation layer for thinking blocks.</p>
<h2 id="what-the-session-costs-on-each">What the session costs on each</h2>
<p>No published test gives per-task token counts for either model, so this example uses stated assumptions. The session has 40 model calls. The prompt starts at 20K tokens and grows by 4K per call (tool output plus the previous answer), ending at 176K. Each call reads the earlier prompt from cache and adds 4K new tokens, and produces 1.5K output tokens including thinking. Both models get the same token counts, although their tokenizers differ.</p>
<p>Pricing rules applied, at AIHubMix list prices:</p>
<ul>
<li><strong>1017:</strong> new tokens at the cache-miss rate, cached prefix at the cache-hit rate, peak or off-peak.</li>
<li><strong>Haiku 5.5:</strong> new tokens written to the 5-minute cache, cached prefix read from it. Any call whose prompt exceeds 100K is billed entirely at the higher tier ($0.50 input, $2.50 output, $0.05 cache read, $0.625 cache write).</li>
</ul>
<div class="blog-table-scroll"><table style="display: table; margin-left: auto; margin-right: auto; border-collapse: collapse; background-color: #ffffff; color: #333333;">
  <thead>
    <tr>
      <th style="text-align: left; padding: 10px 12px; background-color: #ffffff; color: #333333; border: 1px solid #d0d0d0;">Session</th>
      <th style="text-align: left; padding: 10px 12px; background-color: #ffffff; color: #333333; border: 1px solid #d0d0d0;">1017 peak</th>
      <th style="text-align: left; padding: 10px 12px; background-color: #ffffff; color: #333333; border: 1px solid #d0d0d0;">1017 off-peak</th>
      <th style="text-align: left; padding: 10px 12px; background-color: #ffffff; color: #333333; border: 1px solid #d0d0d0;">Haiku 5.5</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td style="padding: 10px 12px; background-color: #ffffff; color: #333333; border: 1px solid #d0d0d0;">First 21 calls, prompt up to 100K</td>
      <td style="padding: 10px 12px; background-color: #ffffff; color: #333333; border: 1px solid #d0d0d0;">$0.077</td>
      <td style="padding: 10px 12px; background-color: #ffffff; color: #333333; border: 1px solid #d0d0d0;">$0.039</td>
      <td style="padding: 10px 12px; background-color: #ffffff; color: #333333; border: 1px solid #d0d0d0;">$0.040</td>
    </tr>
    <tr>
      <td style="padding: 10px 12px; background-color: #ffffff; color: #333333; border: 1px solid #d0d0d0;">Full 40 calls, prompt up to 176K</td>
      <td style="padding: 10px 12px; background-color: #ffffff; color: #333333; border: 1px solid #d0d0d0;">$0.152</td>
      <td style="padding: 10px 12px; background-color: #ffffff; color: #333333; border: 1px solid #d0d0d0;">$0.076</td>
      <td style="padding: 10px 12px; background-color: #ffffff; color: #333333; border: 1px solid #d0d0d0;">$0.288</td>
    </tr>
  </tbody>
</table></div>
<p>On Haiku 5.5, the 19 calls above 100K cost $0.248 of the $0.288. Arithmetic for 1017 at peak, full session: 176K new × $0.3098 + 3.744M cached × $0.0062 + 60K output × $1.2392 = $0.152.</p>
<p>Two practical levers follow from this:</p>
<ul>
<li><strong>On Haiku 5.5,</strong> keep each request under 100K. Claude Code's <code>/compact</code> and short-lived subagents do that. The same 40 calls billed entirely at the short-prompt rate would cost $0.089.</li>
<li><strong>On 1017,</strong> move batch and overnight agent runs outside 01:00–04:00 and 06:00–10:00 UTC on weekdays, which halves the bill.</li>
</ul>
<h2 id="before-you-switch-in-either-direction">Before you switch, in either direction</h2>
<ul>
<li><strong>Moving to Haiku 5.5:</strong> the API rejects <code>temperature</code> values other than 1, any <code>top_k</code>, manual <code>budget_tokens</code> thinking, and assistant prefill, all with a 400. ComputingForGeeks' review also notes that it over-refused more than any other model in an automated behavioural audit, and that stricter cyber classifiers block some security work.</li>
<li><strong>Moving to 1017:</strong> on DeepSeek's API, thinking mode with tools requires every earlier <code>reasoning_content</code> to go back to the API, or the request fails with a 400, so a client that drops it breaks after the first tool call. In a test run through AIHubMix on October 19, the same request returned 200. The migration post in this series covers this and the peak-hour billing rules.</li>
</ul>
<h2 id="who-should-pick-which">Who should pick which</h2>
<ul>
<li><strong>Pick 1017</strong> for main-loop coding agents with long contexts, overnight or batch agent runs you can schedule off-peak, and tasks that build a program from a spec.</li>
<li><strong>Pick Haiku 5.5</strong> for short, scoped subagent calls under 100K tokens, document-heavy steps, computer use, and pipelines that read untrusted web or repo content.</li>
<li><strong>Use both</strong> in one Claude Code setup: a stronger model plans, 1017 does long execution, and Haiku 5.5 handles quick lookups and summaries.</li>
</ul>
<h2 id="run-both-with-one-key">Run both with one key</h2>
<p>Both models are on AIHubMix: <a href="https://aihubmix.com/model/deepseek-v4.1-flash-1017"><code>deepseek-v4.1-flash-1017</code></a> and <a href="https://aihubmix.com/model/claude-haiku-5-5"><code>claude-haiku-5-5</code></a>. To try them in Claude Code, point it at AIHubMix as described in the <a href="https://docs.aihubmix.com/en/api/Claude-Code">Claude Code guide</a>:</p>
<pre><code class="language-bash">export ANTHROPIC_BASE_URL="https://aihubmix.com"
export ANTHROPIC_AUTH_TOKEN="$AIHUBMIX_API_KEY"
export ANTHROPIC_MODEL="deepseek-v4.1-flash-1017"
claude
</code></pre>
<p>In AIHubMix's October 19 test, 1017 completed a two-turn tool call over the Anthropic Messages protocol that Claude Code uses. A request with no effort setting came back without thinking, so keep an effort level set (for example <code>CLAUDE_CODE_EFFORT_LEVEL=high</code>). Inside a session, <code>/model claude-haiku-5-5</code> switches to Haiku on the same key, so you can run the same failing test through both and compare the diffs and the request logs. For specs and prices side by side, see the <a href="https://aihubmix.com/compare/deepseek-v4.1-flash-1017/claude-haiku-5-5">AIHubMix compare page</a>.</p>
<h2 id="faq">FAQ</h2>
<p><strong>Is DeepSeek V4.1 Flash 1017 better than Claude Haiku 5.5 for coding?</strong><br>
On Terminal-Bench 4.0, the one shared coding benchmark, 1017 reports 42.3 and Haiku 5.5 reports 39.2. Both are vendor numbers with different settings, so treat them as roughly level with a small edge to 1017.</p>
<p><strong>Which one is cheaper?</strong><br>
Under 100K tokens per request, Haiku 5.5 costs about the same as 1017 off-peak and about half of 1017 at peak. Above 100K, Haiku bills the whole request at five times the rate, and 1017 becomes the cheaper option.</p>
<p><strong>Does Haiku 5.5 charge more for long prompts?</strong><br>
Yes. When a prompt exceeds 100K tokens, input, output, and cache prices for that whole request rise to five times the short-prompt rate.</p>
<p><strong>Can I use DeepSeek V4.1 Flash 1017 in Claude Code?</strong><br>
Yes. DeepSeek measured its Terminal-Bench 4.0 score in Claude Code, and you can run it through an Anthropic-compatible endpoint such as AIHubMix's by setting the base URL, key, and model name.</p>
<p><strong>Which one is better for subagents?</strong><br>
For short, scoped subagent calls under 100K tokens, Haiku 5.5 is the natural fit and is Claude Code's default haiku model on Anthropic's API. For long-running execution agents, 1017 avoids the long-prompt surcharge.</p>
<p><strong>Where does Haiku 5.5 clearly win?</strong><br>
Knowledge questions (HLE 45.9 vs 42.6), PDF input, computer use, and prompt-injection resistance, by Anthropic's reported figures.</p>
<p><strong>Is either one good enough to replace Sonnet 5.5?</strong><br>
Not for complex agentic coding. Sonnet 5.5 reports 70.6 on Terminal-Bench 4.0, far above both, and Anthropic itself recommends Sonnet or Opus for those tasks.</p>
<h2 id="keep-reading-the-deepseek-v41-flash-1017-series">Keep reading: the DeepSeek V4.1 Flash 1017 series</h2>
<ul>
<li>Want the full picture of what 1017 changed before choosing it over Haiku? The upgrade post covers the benchmark gains and the per-task cost drop: <a href="https://aihubmix.com/blog/deepseek-v4-1-flash-1017-same-price-shorter-outputs-should-you-switch">DeepSeek V4.1 Flash 1017: Same Price, Shorter Outputs, and AIHubMix vs OpenRouter Pricing</a></li>
<li>Picking 1017 for Claude Code means handling its thinking-mode rules; the migration guide shows the 400 error and the fix: <a href="https://aihubmix.com/blog/deepseek-v4-1-flash-1017-migration">Migrating to DeepSeek V4.1 Flash 1017: The reasoning_content 400, the Silent Alias Swap, and Peak-Hour Billing</a></li>
<li>If 1017 already beats Haiku on your tasks, it may also replace DeepSeek's own pricier tier: <a href="https://aihubmix.com/blog/deepseek-v4-1-flash-1017-vs-v4-pro-is-the-flash-enough-now">DeepSeek V4.1 Flash 1017 vs V4 Pro: Is the Flash Enough Now?</a></li>
</ul>
<h2 id="sources">Sources</h2>
<ul>
<li><a href="https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash-1017">DeepSeek-V4.1-Flash-1017 model card</a> (Hugging Face)</li>
<li><a href="https://www.anthropic.com/news/claude-haiku-5-5">Introducing Claude Haiku 5.5</a> (Anthropic)</li>
<li><a href="https://aihubmix.com/compare/deepseek-v4.1-flash-1017/claude-haiku-5-5">DeepSeek V4.1 Flash 1017 vs Claude Haiku 5.5</a> (AIHubMix)</li>
<li><a href="https://computingforgeeks.com/claude-haiku-5-5-released-features-benchmarks/">Claude Haiku 5.5: Pricing, Benchmarks, Tested</a> (ComputingForGeeks)</li>
<li><a href="https://kingy.ai/blog/deepseek-v4-1-flash-1017-specs-benchmarks-pricing/">DeepSeek V4.1 Flash 1017 Tested: Specs, Benchmarks &amp; Price</a> (Kingy.ai)</li>
</ul>
</article><div class="sc-1c0xtl9-16 hUosnH"><div><span class="brand-logo"><img class="brand-logo-light" src="/brand/light.svg?v=20260909" width="110" height="24" alt="AIHubMix"/></span><p class="sc-1c0xtl9-17 cebqkZ">One gateway to multiple AI providers.
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It separates the three leaderboards, records list prices, and gives a decision table you can re-run when the next model ships.\\n\\n\\nRight now, \\\"the strongest AI model\\\" has three different answers, and they are not interchangeable.\\n\\n\\nGemini 4 Carbon tops the Artificial Analysis Intelligence Index at 60, but Google has released it only to a subset of cyber defenders and partners in its F\",\"https://aihubmix.com/blog-media/images/2026/11/f8ab86dcbe4ecbc2.png\",\"2026-11-05T08:17:34.000+00:00\",14,[66],{\"_23\":49,\"_11\":50},\"DeepSeek V4.1 Flash 1017 vs Claude Haiku 5.5: Which Cheap Model for Terminal Coding Agents?\",\"Ten days apart, the two cheapest serious agent models of October 2026 arrived from opposite directions. Anthropic shipped Claude Haiku 5.5 on October 7 at $0.10 input and $0.50 output per 1M tokens, pitched as the subagent that sits under Sonnet and Opus. DeepSeek answered on October 17 with V4.1 Flash 1017, an update aimed squarely at end-to-end coding work.\\n\\n\\nThe decision comes down to one task: a terminal coding session in Claude Code, where the agent reads files, runs tests, and edits code o\",\"html\",\"\u003cp\u003eTen days apart, the two cheapest serious agent models of October 2026 arrived from opposite directions. Anthropic shipped Claude Haiku 5.5 on October 7 at $0.10 input and $0.50 output per 1M tokens, pitched as the subagent that sits under Sonnet and Opus. DeepSeek answered on October 17 with V4.1 Flash 1017, an update aimed squarely at end-to-end coding work.\u003c/p\u003e\\n\u003cp\u003eThe decision comes down to one task: a terminal coding session in Claude Code, where the agent reads files, runs tests, and edits code over dozens of turns. On that task:\u003c/p\u003e\\n\u003cul\u003e\\n\u003cli\u003e\u003cstrong\u003eCapability is close, with a small edge to 1017.\u003c/strong\u003e The one benchmark both vendors report for it, Terminal-Bench 4.0, has 1017 at 42.3 and Haiku 5.5 at 39.2.\u003c/li\u003e\\n\u003cli\u003e\u003cstrong\u003eCost depends on context length.\u003c/strong\u003e Under 100K tokens per request, Haiku 5.5 costs about the same as 1017 off-peak and half of 1017 at peak. Past 100K, Haiku bills the whole request at five times the rate, and 1017 becomes the cheaper model by a wide margin.\u003c/li\u003e\\n\u003cli\u003e\u003cstrong\u003eHaiku wins elsewhere:\u003c/strong\u003e knowledge questions, PDF input, computer use, and prompt-injection resistance.\u003c/li\u003e\\n\u003c/ul\u003e\\n\u003ch2 id=\\\"why-this-pair\\\"\u003eWhy this pair\u003c/h2\u003e\\n\u003cp\u003eBoth are the cheapest agent-capable tier from their vendors, both launched in the same fortnight, and both run inside Claude Code. Anthropic positions Haiku 5.5 as a coding subagent and made it Claude Code's default \u003ccode\u003ehaiku\u003c/code\u003e model on its API. DeepSeek ships an Anthropic-format endpoint and measured 1017's Terminal-Bench 4.0 score inside the Claude Code harness. Search interest follows the same line: comparison pages for this exact pair appeared within days of the 1017 release.\u003c/p\u003e\\n\u003cp\u003eGemini 3.8 Flash is the other name in the conversation, and it appears in DeepSeek's own cost chart. It launched in early September at $0.75 input and $3.75 output, several times the price of either model here, so it stays out of this comparison.\u003c/p\u003e\\n\u003ch2 id=\\\"the-task-a-terminal-coding-session-in-claude-code\\\"\u003eThe task: a terminal coding session in Claude Code\u003c/h2\u003e\\n\u003cp\u003eThe benchmark that matches this task is Terminal-Bench 4.0: long shell sessions with real tools, scored on whether the agent finishes the job. The figures below are vendor-reported: DeepSeek's from its \u003ca href=\\\"https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash-1017\\\"\u003emodel card\u003c/a\u003e, Anthropic's from the \u003ca href=\\\"https://www.anthropic.com/news/claude-haiku-5-5\\\"\u003eHaiku 5.5 announcement\u003c/a\u003e. Nobody has run both under the same harness and settings yet.\u003c/p\u003e\\n\u003ctable style=\\\"display: table; margin-left: auto; margin-right: auto; border-collapse: collapse; background-color: #ffffff; color: #333333;\\\"\u003e\\n  \u003cthead\u003e\\n    \u003ctr\u003e\\n      \u003cth style=\\\"text-align: left; padding: 10px 12px; background-color: #ffffff; color: #333333; border: 1px solid #d0d0d0;\\\"\u003eModel\u003c/th\u003e\\n      \u003cth style=\\\"text-align: left; padding: 10px 12px; background-color: #ffffff; color: #333333; border: 1px solid #d0d0d0;\\\"\u003eTerminal-Bench 4.0\u003c/th\u003e\\n      \u003cth style=\\\"text-align: left; padding: 10px 12px; background-color: #ffffff; color: #333333; border: 1px solid #d0d0d0;\\\"\u003eReported by\u003c/th\u003e\\n    \u003c/tr\u003e\\n  \u003c/thead\u003e\\n  \u003ctbody\u003e\\n    \u003ctr\u003e\\n      \u003ctd style=\\\"padding: 10px 12px; background-color: #ffffff; color: #333333; border: 1px solid #d0d0d0;\\\"\u003eClaude Sonnet 5.5\u003c/td\u003e\\n      \u003ctd style=\\\"padding: 10px 12px; background-color: #ffffff; color: #333333; border: 1px solid #d0d0d0;\\\"\u003e70.6\u003c/td\u003e\\n      \u003ctd style=\\\"padding: 10px 12px; background-color: #ffffff; color: #333333; border: 1px solid #d0d0d0;\\\"\u003eAnthropic\u003c/td\u003e\\n    \u003c/tr\u003e\\n    \u003ctr\u003e\\n      \u003ctd style=\\\"padding: 10px 12px; background-color: #ffffff; color: #333333; border: 1px solid #d0d0d0;\\\"\u003eDeepSeek V4.1 Flash 1017\u003c/td\u003e\\n      \u003ctd style=\\\"padding: 10px 12px; background-color: #ffffff; color: #333333; border: 1px solid #d0d0d0;\\\"\u003e42.3\u003c/td\u003e\\n      \u003ctd style=\\\"padding: 10px 12px; background-color: #ffffff; color: #333333; border: 1px solid #d0d0d0;\\\"\u003eDeepSeek\u003c/td\u003e\\n    \u003c/tr\u003e\\n    \u003ctr\u003e\\n      \u003ctd style=\\\"padding: 10px 12px; background-color: #ffffff; color: #333333; border: 1px solid #d0d0d0;\\\"\u003eClaude Haiku 5.5\u003c/td\u003e\\n      \u003ctd style=\\\"padding: 10px 12px; background-color: #ffffff; color: #333333; border: 1px solid #d0d0d0;\\\"\u003e39.2\u003c/td\u003e\\n      \u003ctd style=\\\"padding: 10px 12px; background-color: #ffffff; color: #333333; border: 1px solid #d0d0d0;\\\"\u003eAnthropic\u003c/td\u003e\\n    \u003c/tr\u003e\\n    \u003ctr\u003e\\n      \u003ctd style=\\\"padding: 10px 12px; background-color: #ffffff; color: #333333; border: 1px solid #d0d0d0;\\\"\u003eDeepSeek V4.1 Flash (Sept)\u003c/td\u003e\\n      \u003ctd style=\\\"padding: 10px 12px; background-color: #ffffff; color: #333333; border: 1px solid #d0d0d0;\\\"\u003e31.2\u003c/td\u003e\\n      \u003ctd style=\\\"padding: 10px 12px; background-color: #ffffff; color: #333333; border: 1px solid #d0d0d0;\\\"\u003eDeepSeek\u003c/td\u003e\\n    \u003c/tr\u003e\\n  \u003c/tbody\u003e\\n\u003c/table\u003e\\n\u003cp\u003eDeepSeek ran Terminal-Bench 4.0 in Claude Code; Anthropic does not state Haiku's effort level in the headline table.\u003c/p\u003e\\n\u003cp\u003eA three-point gap between two vendor numbers with different settings is not a decisive win. Independent Terminal-Bench 4.0 runs of Haiku 5.5 come in lower than Anthropic's figure and disagree with each other, and no independent result for 1017 has been published yet. Read this as \\\"roughly level, 1017 slightly ahead\\\" rather than a ranking. Anthropic's own guidance points the same way: \\\"Sonnet 5.5 and Opus 5.5 remain better choices for complex agentic coding tasks like those measured by Terminal-Bench 4.0.\\\" Neither cheap model is the right pick for the hardest shell sessions.\u003c/p\u003e\\n\u003cp\u003eBeyond that one benchmark, the two vendors publish almost no shared numbers. DeepSeek reports DeepSWE v1.1 (77.0) and ProgramBench (29.1); Anthropic reports FrontierCode 1.1 (46.4) and OSWorld 2.1 (72.4). None of those has a published score for the other model.\u003c/p\u003e\\n\u003ch2 id=\\\"where-each-one-wins\\\"\u003eWhere each one wins\u003c/h2\u003e\\n\u003ctable style=\\\"display: table; margin-left: auto; margin-right: auto; border-collapse: collapse; background-color: #ffffff; color: #333333;\\\"\u003e\\n  \u003cthead\u003e\\n    \u003ctr\u003e\\n      \u003cth style=\\\"text-align: left; padding: 10px 12px; background-color: #ffffff; color: #333333; border: 1px solid #d0d0d0;\\\"\u003eArea\u003c/th\u003e\\n      \u003cth style=\\\"text-align: left; padding: 10px 12px; background-color: #ffffff; color: #333333; border: 1px solid #d0d0d0;\\\"\u003eV4.1 Flash 1017\u003c/th\u003e\\n      \u003cth style=\\\"text-align: left; padding: 10px 12px; background-color: #ffffff; color: #333333; border: 1px solid #d0d0d0;\\\"\u003eClaude Haiku 5.5\u003c/th\u003e\\n    \u003c/tr\u003e\\n  \u003c/thead\u003e\\n  \u003ctbody\u003e\\n    \u003ctr\u003e\\n      \u003ctd style=\\\"padding: 10px 12px; background-color: #ffffff; color: #333333; border: 1px solid #d0d0d0;\\\"\u003eTerminal-Bench 4.0\u003c/td\u003e\\n      \u003ctd style=\\\"padding: 10px 12px; background-color: #ffffff; color: #333333; border: 1px solid #d0d0d0;\\\"\u003e42.3\u003c/td\u003e\\n      \u003ctd style=\\\"padding: 10px 12px; background-color: #ffffff; color: #333333; border: 1px solid #d0d0d0;\\\"\u003e39.2\u003c/td\u003e\\n    \u003c/tr\u003e\\n    \u003ctr\u003e\\n      \u003ctd style=\\\"padding: 10px 12px; background-color: #ffffff; color: #333333; border: 1px solid #d0d0d0;\\\"\u003eHLE, no tools\u003c/td\u003e\\n      \u003ctd style=\\\"padding: 10px 12px; background-color: #ffffff; color: #333333; border: 1px solid #d0d0d0;\\\"\u003e42.6\u003c/td\u003e\\n      \u003ctd style=\\\"padding: 10px 12px; background-color: #ffffff; color: #333333; border: 1px solid #d0d0d0;\\\"\u003e45.9\u003c/td\u003e\\n    \u003c/tr\u003e\\n    \u003ctr\u003e\\n      \u003ctd style=\\\"padding: 10px 12px; background-color: #ffffff; color: #333333; border: 1px solid #d0d0d0;\\\"\u003eContext window\u003c/td\u003e\\n      \u003ctd style=\\\"padding: 10px 12px; background-color: #ffffff; color: #333333; border: 1px solid #d0d0d0;\\\"\u003e1M\u003c/td\u003e\\n      \u003ctd style=\\\"padding: 10px 12px; background-color: #ffffff; color: #333333; border: 1px solid #d0d0d0;\\\"\u003e1M\u003c/td\u003e\\n    \u003c/tr\u003e\\n    \u003ctr\u003e\\n      \u003ctd style=\\\"padding: 10px 12px; background-color: #ffffff; color: #333333; border: 1px solid #d0d0d0;\\\"\u003eMax output\u003c/td\u003e\\n      \u003ctd style=\\\"padding: 10px 12px; background-color: #ffffff; color: #333333; border: 1px solid #d0d0d0;\\\"\u003e384K\u003c/td\u003e\\n      \u003ctd style=\\\"padding: 10px 12px; background-color: #ffffff; color: #333333; border: 1px solid #d0d0d0;\\\"\u003e128K\u003c/td\u003e\\n    \u003c/tr\u003e\\n    \u003ctr\u003e\\n      \u003ctd style=\\\"padding: 10px 12px; background-color: #ffffff; color: #333333; border: 1px solid #d0d0d0;\\\"\u003eInput types\u003c/td\u003e\\n      \u003ctd style=\\\"padding: 10px 12px; background-color: #ffffff; color: #333333; border: 1px solid #d0d0d0;\\\"\u003eText, images\u003c/td\u003e\\n      \u003ctd style=\\\"padding: 10px 12px; background-color: #ffffff; color: #333333; border: 1px solid #d0d0d0;\\\"\u003eText, images, PDF\u003c/td\u003e\\n    \u003c/tr\u003e\\n    \u003ctr\u003e\\n      \u003ctd style=\\\"padding: 10px 12px; background-color: #ffffff; color: #333333; border: 1px solid #d0d0d0;\\\"\u003ePrice above 100K prompt\u003c/td\u003e\\n      \u003ctd style=\\\"padding: 10px 12px; background-color: #ffffff; color: #333333; border: 1px solid #d0d0d0;\\\"\u003eUnchanged\u003c/td\u003e\\n      \u003ctd style=\\\"padding: 10px 12px; background-color: #ffffff; color: #333333; border: 1px solid #d0d0d0;\\\"\u003e5x\u003c/td\u003e\\n    \u003c/tr\u003e\\n    \u003ctr\u003e\\n      \u003ctd style=\\\"padding: 10px 12px; background-color: #ffffff; color: #333333; border: 1px solid #d0d0d0;\\\"\u003eEffort levels\u003c/td\u003e\\n      \u003ctd style=\\\"padding: 10px 12px; background-color: #ffffff; color: #333333; border: 1px solid #d0d0d0;\\\"\u003e3\u003c/td\u003e\\n      \u003ctd style=\\\"padding: 10px 12px; background-color: #ffffff; color: #333333; border: 1px solid #d0d0d0;\\\"\u003e5\u003c/td\u003e\\n    \u003c/tr\u003e\\n    \u003ctr\u003e\\n      \u003ctd style=\\\"padding: 10px 12px; background-color: #ffffff; color: #333333; border: 1px solid #d0d0d0;\\\"\u003eOpen weights\u003c/td\u003e\\n      \u003ctd style=\\\"padding: 10px 12px; background-color: #ffffff; color: #333333; border: 1px solid #d0d0d0;\\\"\u003eYes, MIT\u003c/td\u003e\\n      \u003ctd style=\\\"padding: 10px 12px; background-color: #ffffff; color: #333333; border: 1px solid #d0d0d0;\\\"\u003eNo\u003c/td\u003e\\n    \u003c/tr\u003e\\n  \u003c/tbody\u003e\\n\u003c/table\u003e\\n\u003cp\u003eVendor-reported benchmark figures. DeepSeek's effort levels are low, high, and max; Haiku's are low, medium (default), high, xhigh, and max.\u003c/p\u003e\\n\u003cp\u003e\u003cstrong\u003e1017 is stronger for\u003c/strong\u003e long sessions (no long-context surcharge, three times the output ceiling), terminal and program-building work, and teams that want open weights as a fallback.\u003c/p\u003e\\n\u003cp\u003e\u003cstrong\u003eHaiku 5.5 is stronger for\u003c/strong\u003e knowledge-heavy steps, PDF and document work, computer use (72.4 on OSWorld 2.1, which DeepSeek does not report), and agents that read untrusted content: ComputingForGeeks cites Gray Swan coding scenarios where prompt-injection success fell from 58.4% on Haiku 4.5 to 0.08%. It also runs on Anthropic's native protocol in Claude Code, with no translation layer for thinking blocks.\u003c/p\u003e\\n\u003ch2 id=\\\"what-the-session-costs-on-each\\\"\u003eWhat the session costs on each\u003c/h2\u003e\\n\u003cp\u003eNo published test gives per-task token counts for either model, so this example uses stated assumptions. The session has 40 model calls. The prompt starts at 20K tokens and grows by 4K per call (tool output plus the previous answer), ending at 176K. Each call reads the earlier prompt from cache and adds 4K new tokens, and produces 1.5K output tokens including thinking. Both models get the same token counts, although their tokenizers differ.\u003c/p\u003e\\n\u003cp\u003ePricing rules applied, at AIHubMix list prices:\u003c/p\u003e\\n\u003cul\u003e\\n\u003cli\u003e\u003cstrong\u003e1017:\u003c/strong\u003e new tokens at the cache-miss rate, cached prefix at the cache-hit rate, peak or off-peak.\u003c/li\u003e\\n\u003cli\u003e\u003cstrong\u003eHaiku 5.5:\u003c/strong\u003e new tokens written to the 5-minute cache, cached prefix read from it. Any call whose prompt exceeds 100K is billed entirely at the higher tier ($0.50 input, $2.50 output, $0.05 cache read, $0.625 cache write).\u003c/li\u003e\\n\u003c/ul\u003e\\n\u003ctable style=\\\"display: table; margin-left: auto; margin-right: auto; border-collapse: collapse; background-color: #ffffff; color: #333333;\\\"\u003e\\n  \u003cthead\u003e\\n    \u003ctr\u003e\\n      \u003cth style=\\\"text-align: left; padding: 10px 12px; background-color: #ffffff; color: #333333; border: 1px solid #d0d0d0;\\\"\u003eSession\u003c/th\u003e\\n      \u003cth style=\\\"text-align: left; padding: 10px 12px; background-color: #ffffff; color: #333333; border: 1px solid #d0d0d0;\\\"\u003e1017 peak\u003c/th\u003e\\n      \u003cth style=\\\"text-align: left; padding: 10px 12px; background-color: #ffffff; color: #333333; border: 1px solid #d0d0d0;\\\"\u003e1017 off-peak\u003c/th\u003e\\n      \u003cth style=\\\"text-align: left; padding: 10px 12px; background-color: #ffffff; color: #333333; border: 1px solid #d0d0d0;\\\"\u003eHaiku 5.5\u003c/th\u003e\\n    \u003c/tr\u003e\\n  \u003c/thead\u003e\\n  \u003ctbody\u003e\\n    \u003ctr\u003e\\n      \u003ctd style=\\\"padding: 10px 12px; background-color: #ffffff; color: #333333; border: 1px solid #d0d0d0;\\\"\u003eFirst 21 calls, prompt up to 100K\u003c/td\u003e\\n      \u003ctd style=\\\"padding: 10px 12px; background-color: #ffffff; color: #333333; border: 1px solid #d0d0d0;\\\"\u003e$0.077\u003c/td\u003e\\n      \u003ctd style=\\\"padding: 10px 12px; background-color: #ffffff; color: #333333; border: 1px solid #d0d0d0;\\\"\u003e$0.039\u003c/td\u003e\\n      \u003ctd style=\\\"padding: 10px 12px; background-color: #ffffff; color: #333333; border: 1px solid #d0d0d0;\\\"\u003e$0.040\u003c/td\u003e\\n    \u003c/tr\u003e\\n    \u003ctr\u003e\\n      \u003ctd style=\\\"padding: 10px 12px; background-color: #ffffff; color: #333333; border: 1px solid #d0d0d0;\\\"\u003eFull 40 calls, prompt up to 176K\u003c/td\u003e\\n      \u003ctd style=\\\"padding: 10px 12px; background-color: #ffffff; color: #333333; border: 1px solid #d0d0d0;\\\"\u003e$0.152\u003c/td\u003e\\n      \u003ctd style=\\\"padding: 10px 12px; background-color: #ffffff; color: #333333; border: 1px solid #d0d0d0;\\\"\u003e$0.076\u003c/td\u003e\\n      \u003ctd style=\\\"padding: 10px 12px; background-color: #ffffff; color: #333333; border: 1px solid #d0d0d0;\\\"\u003e$0.288\u003c/td\u003e\\n    \u003c/tr\u003e\\n  \u003c/tbody\u003e\\n\u003c/table\u003e\\n\u003cp\u003eOn Haiku 5.5, the 19 calls above 100K cost $0.248 of the $0.288. Arithmetic for 1017 at peak, full session: 176K new × $0.3098 + 3.744M cached × $0.0062 + 60K output × $1.2392 = $0.152.\u003c/p\u003e\\n\u003cp\u003eTwo practical levers follow from this:\u003c/p\u003e\\n\u003cul\u003e\\n\u003cli\u003e\u003cstrong\u003eOn Haiku 5.5,\u003c/strong\u003e keep each request under 100K. Claude Code's \u003ccode\u003e/compact\u003c/code\u003e and short-lived subagents do that. The same 40 calls billed entirely at the short-prompt rate would cost $0.089.\u003c/li\u003e\\n\u003cli\u003e\u003cstrong\u003eOn 1017,\u003c/strong\u003e move batch and overnight agent runs outside 01:00–04:00 and 06:00–10:00 UTC on weekdays, which halves the bill.\u003c/li\u003e\\n\u003c/ul\u003e\\n\u003ch2 id=\\\"before-you-switch-in-either-direction\\\"\u003eBefore you switch, in either direction\u003c/h2\u003e\\n\u003cul\u003e\\n\u003cli\u003e\u003cstrong\u003eMoving to Haiku 5.5:\u003c/strong\u003e the API rejects \u003ccode\u003etemperature\u003c/code\u003e values other than 1, any \u003ccode\u003etop_k\u003c/code\u003e, manual \u003ccode\u003ebudget_tokens\u003c/code\u003e thinking, and assistant prefill, all with a 400. ComputingForGeeks' review also notes that it over-refused more than any other model in an automated behavioural audit, and that stricter cyber classifiers block some security work.\u003c/li\u003e\\n\u003cli\u003e\u003cstrong\u003eMoving to 1017:\u003c/strong\u003e on DeepSeek's API, thinking mode with tools requires every earlier \u003ccode\u003ereasoning_content\u003c/code\u003e to go back to the API, or the request fails with a 400, so a client that drops it breaks after the first tool call. In a test run through AIHubMix on October 19, the same request returned 200. The migration post in this series covers this and the peak-hour billing rules.\u003c/li\u003e\\n\u003c/ul\u003e\\n\u003ch2 id=\\\"who-should-pick-which\\\"\u003eWho should pick which\u003c/h2\u003e\\n\u003cul\u003e\\n\u003cli\u003e\u003cstrong\u003ePick 1017\u003c/strong\u003e for main-loop coding agents with long contexts, overnight or batch agent runs you can schedule off-peak, and tasks that build a program from a spec.\u003c/li\u003e\\n\u003cli\u003e\u003cstrong\u003ePick Haiku 5.5\u003c/strong\u003e for short, scoped subagent calls under 100K tokens, document-heavy steps, computer use, and pipelines that read untrusted web or repo content.\u003c/li\u003e\\n\u003cli\u003e\u003cstrong\u003eUse both\u003c/strong\u003e in one Claude Code setup: a stronger model plans, 1017 does long execution, and Haiku 5.5 handles quick lookups and summaries.\u003c/li\u003e\\n\u003c/ul\u003e\\n\u003ch2 id=\\\"run-both-with-one-key\\\"\u003eRun both with one key\u003c/h2\u003e\\n\u003cp\u003eBoth models are on AIHubMix: \u003ca href=\\\"https://aihubmix.com/model/deepseek-v4.1-flash-1017\\\"\u003e\u003ccode\u003edeepseek-v4.1-flash-1017\u003c/code\u003e\u003c/a\u003e and \u003ca href=\\\"https://aihubmix.com/model/claude-haiku-5-5\\\"\u003e\u003ccode\u003eclaude-haiku-5-5\u003c/code\u003e\u003c/a\u003e. To try them in Claude Code, point it at AIHubMix as described in the \u003ca href=\\\"https://docs.aihubmix.com/en/api/Claude-Code\\\"\u003eClaude Code guide\u003c/a\u003e:\u003c/p\u003e\\n\u003cpre\u003e\u003ccode class=\\\"language-bash\\\"\u003eexport ANTHROPIC_BASE_URL=\\\"https://aihubmix.com\\\"\\nexport ANTHROPIC_AUTH_TOKEN=\\\"$AIHUBMIX_API_KEY\\\"\\nexport ANTHROPIC_MODEL=\\\"deepseek-v4.1-flash-1017\\\"\\nclaude\\n\u003c/code\u003e\u003c/pre\u003e\\n\u003cp\u003eIn AIHubMix's October 19 test, 1017 completed a two-turn tool call over the Anthropic Messages protocol that Claude Code uses. A request with no effort setting came back without thinking, so keep an effort level set (for example \u003ccode\u003eCLAUDE_CODE_EFFORT_LEVEL=high\u003c/code\u003e). Inside a session, \u003ccode\u003e/model claude-haiku-5-5\u003c/code\u003e switches to Haiku on the same key, so you can run the same failing test through both and compare the diffs and the request logs. For specs and prices side by side, see the \u003ca href=\\\"https://aihubmix.com/compare/deepseek-v4.1-flash-1017/claude-haiku-5-5\\\"\u003eAIHubMix compare page\u003c/a\u003e.\u003c/p\u003e\\n\u003ch2 id=\\\"faq\\\"\u003eFAQ\u003c/h2\u003e\\n\u003cp\u003e\u003cstrong\u003eIs DeepSeek V4.1 Flash 1017 better than Claude Haiku 5.5 for coding?\u003c/strong\u003e\u003cbr\u003e\\nOn Terminal-Bench 4.0, the one shared coding benchmark, 1017 reports 42.3 and Haiku 5.5 reports 39.2. Both are vendor numbers with different settings, so treat them as roughly level with a small edge to 1017.\u003c/p\u003e\\n\u003cp\u003e\u003cstrong\u003eWhich one is cheaper?\u003c/strong\u003e\u003cbr\u003e\\nUnder 100K tokens per request, Haiku 5.5 costs about the same as 1017 off-peak and about half of 1017 at peak. Above 100K, Haiku bills the whole request at five times the rate, and 1017 becomes the cheaper option.\u003c/p\u003e\\n\u003cp\u003e\u003cstrong\u003eDoes Haiku 5.5 charge more for long prompts?\u003c/strong\u003e\u003cbr\u003e\\nYes. When a prompt exceeds 100K tokens, input, output, and cache prices for that whole request rise to five times the short-prompt rate.\u003c/p\u003e\\n\u003cp\u003e\u003cstrong\u003eCan I use DeepSeek V4.1 Flash 1017 in Claude Code?\u003c/strong\u003e\u003cbr\u003e\\nYes. DeepSeek measured its Terminal-Bench 4.0 score in Claude Code, and you can run it through an Anthropic-compatible endpoint such as AIHubMix's by setting the base URL, key, and model name.\u003c/p\u003e\\n\u003cp\u003e\u003cstrong\u003eWhich one is better for subagents?\u003c/strong\u003e\u003cbr\u003e\\nFor short, scoped subagent calls under 100K tokens, Haiku 5.5 is the natural fit and is Claude Code's default haiku model on Anthropic's API. For long-running execution agents, 1017 avoids the long-prompt surcharge.\u003c/p\u003e\\n\u003cp\u003e\u003cstrong\u003eWhere does Haiku 5.5 clearly win?\u003c/strong\u003e\u003cbr\u003e\\nKnowledge questions (HLE 45.9 vs 42.6), PDF input, computer use, and prompt-injection resistance, by Anthropic's reported figures.\u003c/p\u003e\\n\u003cp\u003e\u003cstrong\u003eIs either one good enough to replace Sonnet 5.5?\u003c/strong\u003e\u003cbr\u003e\\nNot for complex agentic coding. Sonnet 5.5 reports 70.6 on Terminal-Bench 4.0, far above both, and Anthropic itself recommends Sonnet or Opus for those tasks.\u003c/p\u003e\\n\u003ch2 id=\\\"keep-reading-the-deepseek-v41-flash-1017-series\\\"\u003eKeep reading: the DeepSeek V4.1 Flash 1017 series\u003c/h2\u003e\\n\u003cul\u003e\\n\u003cli\u003eWant the full picture of what 1017 changed before choosing it over Haiku? The upgrade post covers the benchmark gains and the per-task cost drop: \u003ca href=\\\"https://aihubmix.com/blog/deepseek-v4-1-flash-1017-same-price-shorter-outputs-should-you-switch\\\"\u003eDeepSeek V4.1 Flash 1017: Same Price, Shorter Outputs, and AIHubMix vs OpenRouter Pricing\u003c/a\u003e\u003c/li\u003e\\n\u003cli\u003ePicking 1017 for Claude Code means handling its thinking-mode rules; the migration guide shows the 400 error and the fix: \u003ca href=\\\"https://aihubmix.com/blog/deepseek-v4-1-flash-1017-migration\\\"\u003eMigrating to DeepSeek V4.1 Flash 1017: The reasoning_content 400, the Silent Alias Swap, and Peak-Hour Billing\u003c/a\u003e\u003c/li\u003e\\n\u003cli\u003eIf 1017 already beats Haiku on your tasks, it may also replace DeepSeek's own pricier tier: \u003ca href=\\\"https://aihubmix.com/blog/deepseek-v4-1-flash-1017-vs-v4-pro-is-the-flash-enough-now\\\"\u003eDeepSeek V4.1 Flash 1017 vs V4 Pro: Is the Flash Enough Now?\u003c/a\u003e\u003c/li\u003e\\n\u003c/ul\u003e\\n\u003ch2 id=\\\"sources\\\"\u003eSources\u003c/h2\u003e\\n\u003cul\u003e\\n\u003cli\u003e\u003ca href=\\\"https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash-1017\\\"\u003eDeepSeek-V4.1-Flash-1017 model card\u003c/a\u003e (Hugging Face)\u003c/li\u003e\\n\u003cli\u003e\u003ca href=\\\"https://www.anthropic.com/news/claude-haiku-5-5\\\"\u003eIntroducing Claude Haiku 5.5\u003c/a\u003e (Anthropic)\u003c/li\u003e\\n\u003cli\u003e\u003ca href=\\\"https://aihubmix.com/compare/deepseek-v4.1-flash-1017/claude-haiku-5-5\\\"\u003eDeepSeek V4.1 Flash 1017 vs Claude Haiku 5.5\u003c/a\u003e (AIHubMix)\u003c/li\u003e\\n\u003cli\u003e\u003ca href=\\\"https://computingforgeeks.com/claude-haiku-5-5-released-features-benchmarks/\\\"\u003eClaude Haiku 5.5: Pricing, Benchmarks, Tested\u003c/a\u003e (ComputingForGeeks)\u003c/li\u003e\\n\u003cli\u003e\u003ca href=\\\"https://kingy.ai/blog/deepseek-v4-1-flash-1017-specs-benchmarks-pricing/\\\"\u003eDeepSeek V4.1 Flash 1017 Tested: Specs, Benchmarks \u0026amp; Price\u003c/a\u003e (Kingy.ai)\u003c/li\u003e\\n\u003c/ul\u003e\\n\",\"https://aihubmix.com/blog-media/images/2026/10/e9257d71d96bd665.png\",\"2026-10-19T05:34:24.000+00:00\",\"2026-10-26T03:46:18.000+00:00\",7,[76],{\"_23\":49,\"_11\":50}]\n");</script><!--$--><script>window.__reactRouterContext.streamController.close();</script><!--/$--><!--/$--></body></html>