> ## Documentation Index
> Fetch the complete documentation index at: https://platform.kimi.ai/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Best Practices for Benchmarking

Benchmarking is an **engineering task** that needs stability and reproducibility. You'll be calling the model thousands of times; even tiny drifts in system setup or network latency can compromise result accuracy. Here's what we've learned to keep things reproducible and trustworthy.

**Quick notes**

* For any **unlisted** or **closed-source** benchmark:  set`temperature = 1.0`, `stream = true`, `top_p = 0.95`
* **Reasoning benchmarks**: `max_tokens = 128k`, and run at least **500–1000 samples** to get low variance (e.g. `AIME 2025`: 32 runs -> 30 × 32 = 960 questions)
* **Coding benchmarks**: `max_tokens = 256k`
* **Agentic task benchmarks:**
  * For multi-hop search: `max_tokens = 256k` + context management
  * Others: `max_tokens ≥ 16k–64k`

## K2.6 Models Benchmark Recommended Settings

<div style={{ overflowX: 'auto' }}>
  <table style={{ minWidth: '900px' }}>
    <thead>
      <tr>
        <th style={{ whiteSpace: 'nowrap' }}>Benchmark Category</th>
        <th style={{ whiteSpace: 'nowrap' }}>Benchmark</th>
        <th style={{ whiteSpace: 'nowrap' }}>Temperature</th>
        <th style={{ whiteSpace: 'nowrap' }}>Recommended max tokens</th>
        <th style={{ whiteSpace: 'nowrap' }}>Recommended runs</th>
        <th style={{ whiteSpace: 'nowrap' }}>Top-p</th>
        <th style={{ whiteSpace: 'nowrap' }}>Others (e.g. test log)</th>
      </tr>
    </thead>

    <tbody>
      <tr>
        <td rowSpan="7">Multi-modal</td>
        <td>MMMU-Pro</td>
        <td>1.0</td>
        <td>max tokens = 96k</td>
        <td>3</td>
        <td>top\_p=0.95</td>
        <td>thinking={`{"type": "enabled"}`}</td>
      </tr>

      <tr>
        <td>MMMU-Pro w/ python</td>
        <td>1.0</td>
        <td>per step tokens = 64k;<br />total max tokens = 256k</td>
        <td>3</td>
        <td>top\_p=0.95</td>
        <td>Recommended max steps = 50<br />thinking={`{"type": "enabled"}`}</td>
      </tr>

      <tr>
        <td>CharXiv (RQ)</td>
        <td>1.0</td>
        <td>max tokens = 96k</td>
        <td>3</td>
        <td>top\_p=0.95</td>
        <td>thinking={`{"type": "enabled"}`}</td>
      </tr>

      <tr>
        <td>CharXiv (RQ) w/ python</td>
        <td>1.0</td>
        <td>per step tokens = 64k;<br />total max tokens = 256k</td>
        <td>3</td>
        <td>top\_p=0.95</td>
        <td>Recommended max steps = 50<br />thinking={`{"type": "enabled"}`}</td>
      </tr>

      <tr>
        <td>MathVision</td>
        <td>1.0</td>
        <td>max tokens = 96k</td>
        <td>3</td>
        <td>top\_p=0.95</td>
        <td>thinking={`{"type": "enabled"}`}</td>
      </tr>

      <tr>
        <td>MathVision w/ python</td>
        <td>1.0</td>
        <td>per step tokens = 64k;<br />total max tokens = 256k</td>
        <td>3</td>
        <td>top\_p=0.95</td>
        <td>Recommended max steps = 50<br />thinking={`{"type": "enabled"}`}</td>
      </tr>

      <tr>
        <td>V\* w/ python</td>
        <td>1.0</td>
        <td>per step tokens = 64k;<br />total max tokens = 256k</td>
        <td>3</td>
        <td>top\_p=0.95</td>
        <td>Recommended max steps = 50<br />thinking={`{"type": "enabled"}`}</td>
      </tr>

      <tr>
        <td rowSpan="8">Agent</td>
        <td>HLE-Full w/ tools</td>
        <td>1.0</td>
        <td>per step tokens = 48k;<br />total max tokens = 256k</td>
        <td>1</td>
        <td>top\_p=0.95</td>
        <td>Recommended max steps = 300<br />thinking={`{"type": "enabled"}`}</td>
      </tr>

      <tr>
        <td>BrowseComp</td>
        <td>1.0</td>
        <td>per step tokens = 48k;<br />total max tokens = 256k</td>
        <td>1</td>
        <td>top\_p=0.95</td>
        <td>Recommended max steps = 300<br />thinking={`{"type": "enabled"}`}</td>
      </tr>

      <tr>
        <td>DeepSearchQA</td>
        <td>1.0</td>
        <td>per step tokens = 48k;<br />total max tokens = 256k</td>
        <td>1</td>
        <td>top\_p=0.95</td>
        <td>Recommended max steps = 300<br />thinking={`{"type": "enabled"}`}</td>
      </tr>

      <tr>
        <td>WideSearch</td>
        <td>1.0</td>
        <td>per step tokens = 48k;<br />total max tokens = 256k</td>
        <td>4</td>
        <td>top\_p=0.95</td>
        <td>Recommended max steps = 300<br />thinking={`{"type": "enabled"}`}</td>
      </tr>

      <tr>
        <td>Toolathlon</td>
        <td>1.0</td>
        <td>per step tokens = 48k;<br />total max tokens = 256k</td>
        <td>4</td>
        <td>top\_p=0.95</td>
        <td>Recommended max steps = 300<br />thinking={`{"type": "enabled"}`}</td>
      </tr>

      <tr>
        <td>MCPMark</td>
        <td>1.0</td>
        <td>per step tokens = 48k;<br />total max tokens = 256k</td>
        <td>4</td>
        <td>top\_p=0.95</td>
        <td>Recommended max steps = 300<br />thinking={`{"type": "enabled"}`}</td>
      </tr>

      <tr>
        <td>Claw Eval</td>
        <td>1.0</td>
        <td>per step tokens = 48k;<br />total max tokens = 256k</td>
        <td>4</td>
        <td>top\_p=0.95</td>
        <td>Recommended max steps = 300<br />thinking={`{"type": "enabled"}`}</td>
      </tr>

      <tr>
        <td>APEX-Agents</td>
        <td>1.0</td>
        <td>per step tokens = 48k;<br />total max tokens = 256k</td>
        <td>4</td>
        <td>top\_p=0.95</td>
        <td>Recommended max steps = 300<br />thinking={`{"type": "enabled"}`}</td>
      </tr>

      <tr>
        <td rowSpan="7">Coding</td>
        <td>Terminal-Bench 2.0 (Terminus-2)</td>
        <td>1.0</td>
        <td>max tokens = 256k</td>
        <td>3</td>
        <td>top\_p=0.95</td>
        <td>thinking={`{"type": "enabled"}`}</td>
      </tr>

      <tr>
        <td>SWE-Bench Pro</td>
        <td>1.0</td>
        <td>per step tokens = 32k;<br />total max tokens = 256k</td>
        <td>5</td>
        <td>top\_p=0.95</td>
        <td>Recommended max steps = 300<br />thinking={`{"type": "enabled"}`}</td>
      </tr>

      <tr>
        <td>SWE-Bench Multilingual</td>
        <td>1.0</td>
        <td>per step tokens = 32k;<br />total max tokens = 256k</td>
        <td>5</td>
        <td>top\_p=0.95</td>
        <td>Recommended max steps = 300<br />thinking={`{"type": "enabled"}`}</td>
      </tr>

      <tr>
        <td>SWE-Bench Verified</td>
        <td>1.0</td>
        <td>per step tokens = 32k;<br />total max tokens = 256k</td>
        <td>5</td>
        <td>top\_p=0.95</td>
        <td>Recommended max steps = 300<br />thinking={`{"type": "enabled"}`}</td>
      </tr>

      <tr>
        <td>SciCode</td>
        <td>1.0</td>
        <td>max tokens = 96k</td>
        <td>4</td>
        <td>top\_p=0.95</td>
        <td>thinking={`{"type": "enabled"}`}</td>
      </tr>

      <tr>
        <td>OJBench (python)</td>
        <td>1.0</td>
        <td>max tokens = 96k</td>
        <td>8</td>
        <td>top\_p=0.95</td>
        <td>thinking={`{"type": "enabled"}`}</td>
      </tr>

      <tr>
        <td>LiveCodeBench (v6)</td>
        <td>1.0</td>
        <td>max tokens = 96k</td>
        <td>1</td>
        <td>top\_p=0.95</td>
        <td>thinking={`{"type": "enabled"}`}</td>
      </tr>

      <tr>
        <td rowSpan="3">Math</td>
        <td>AIME 2026</td>
        <td>1.0</td>
        <td>max tokens = 96k</td>
        <td>32</td>
        <td>top\_p=0.95</td>
        <td>thinking={`{"type": "enabled"}`}</td>
      </tr>

      <tr>
        <td>HMMT 2026 (Feb)</td>
        <td>1.0</td>
        <td>max tokens = 96k</td>
        <td>32</td>
        <td>top\_p=0.95</td>
        <td>thinking={`{"type": "enabled"}`}</td>
      </tr>

      <tr>
        <td>IMO-AnswerBench</td>
        <td>1.0</td>
        <td>max tokens = 96k</td>
        <td>4</td>
        <td>top\_p=0.95</td>
        <td>thinking={`{"type": "enabled"}`}</td>
      </tr>

      <tr>
        <td rowSpan="2">Knowledge</td>
        <td>HLE-Full</td>
        <td>1.0</td>
        <td>max tokens = 96k</td>
        <td>1</td>
        <td>top\_p=0.95</td>
        <td>thinking={`{"type": "enabled"}`}</td>
      </tr>

      <tr>
        <td>GPQA-Diamond</td>
        <td>1.0</td>
        <td>max tokens = 96k</td>
        <td>8</td>
        <td>top\_p=0.95</td>
        <td>thinking={`{"type": "enabled"}`}</td>
      </tr>
    </tbody>
  </table>
</div>

## K2.5 Models Benchmark Recommended Settings

<div style={{ overflowX: 'auto' }}>
  <table style={{ minWidth: '900px' }}>
    <thead>
      <tr>
        <th style={{ whiteSpace: 'nowrap' }}>Benchmark Category</th>
        <th style={{ whiteSpace: 'nowrap' }}>Benchmark</th>
        <th style={{ whiteSpace: 'nowrap' }}>Temperature</th>
        <th style={{ whiteSpace: 'nowrap' }}>Recommended max tokens</th>
        <th style={{ whiteSpace: 'nowrap' }}>Recommended runs</th>
        <th style={{ whiteSpace: 'nowrap' }}>Top-p</th>
        <th style={{ whiteSpace: 'nowrap' }}>Others (e.g. test log)</th>
      </tr>
    </thead>

    <tbody>
      <tr>
        <td rowSpan="10">Multi-modal</td>
        <td>MMMU-Pro</td>
        <td> 1.0</td>
        <td>max tokens = 64k</td>
        <td>3</td>
        <td>top\_p=0.95</td>
        <td>thinking={`{"type": "enabled"}`}</td>
      </tr>

      <tr>
        <td>CharXiv (RQ)</td>
        <td>1.0</td>
        <td>max tokens = 64k</td>
        <td>3</td>
        <td>top\_p=0.95</td>
        <td>thinking={`{"type": "enabled"}`}</td>
      </tr>

      <tr>
        <td>MathVision</td>
        <td>1.0</td>
        <td>max tokens = 64k</td>
        <td>3</td>
        <td>top\_p=0.95</td>
        <td>thinking={`{"type": "enabled"}`}</td>
      </tr>

      <tr>
        <td>MathVista</td>
        <td>1.0</td>
        <td>max tokens = 64k</td>
        <td>3</td>
        <td>top\_p=0.95</td>
        <td>thinking={`{"type": "enabled"}`}</td>
      </tr>

      <tr>
        <td>OCRBench</td>
        <td>1.0</td>
        <td>max tokens = 64k</td>
        <td>3</td>
        <td>top\_p=0.95</td>
        <td>thinking={`{"type": "enabled"}`}</td>
      </tr>

      <tr>
        <td>ZeroBench</td>
        <td>1.0</td>
        <td>max tokens = 64k</td>
        <td>3</td>
        <td>top\_p=0.95</td>
        <td>thinking={`{"type": "enabled"}`}</td>
      </tr>

      <tr>
        <td>WorldVQA</td>
        <td>1.0</td>
        <td>max tokens = 64k</td>
        <td>3</td>
        <td>top\_p=0.95</td>
        <td>thinking={`{"type": "enabled"}`}</td>
      </tr>

      <tr>
        <td>InfoVQA (val)</td>
        <td>1.0</td>
        <td>max tokens = 64k</td>
        <td>3</td>
        <td>top\_p=0.95</td>
        <td>thinking={`{"type": "enabled"}`}</td>
      </tr>

      <tr>
        <td>SimpleVQA</td>
        <td>1.0</td>
        <td>max tokens = 64k</td>
        <td>3</td>
        <td>top\_p=0.95</td>
        <td>thinking={`{"type": "enabled"}`}</td>
      </tr>

      <tr>
        <td>ZeroBench w/ tools</td>
        <td>1.0</td>
        <td>max tokens = 64k</td>
        <td>3</td>
        <td>top\_p=0.95</td>
        <td>Recommended max steps = 30<br />thinking={`{"type": "enabled"}`}</td>
      </tr>

      <tr>
        <td rowSpan="3">Code</td>
        <td>SWE Series</td>
        <td>1.0</td>
        <td>per step tokens = 16k;<br />total max tokens = 256k</td>
        <td>5</td>
        <td>top\_p=0.95</td>
        <td>thinking={`{"type": "enabled"}`}</td>
      </tr>

      <tr>
        <td>Lcb + OJBench</td>
        <td>1.0</td>
        <td>max tokens = 128k</td>
        <td>1</td>
        <td>top\_p=0.95</td>
        <td>thinking={`{"type": "enabled"}`}</td>
      </tr>

      <tr>
        <td>TerminalBench</td>
        <td>1.0</td>
        <td>max tokens = 128k</td>
        <td>3</td>
        <td>top\_p=0.95</td>
        <td>thinking={`{"type": "enabled"}`}</td>
      </tr>

      <tr>
        <td rowSpan="9">Reasoning</td>
        <td>AIME2025 no tools</td>
        <td>1.0</td>
        <td>total max tokens = 96k</td>
        <td>32</td>
        <td>top\_p=0.95</td>
        <td>thinking={`{"type": "enabled"}`}</td>
      </tr>

      <tr>
        <td>AIME2025 w/ tools</td>
        <td>1.0</td>
        <td>per turn tokens = 96k;<br />total max tokens = 96k</td>
        <td>32</td>
        <td>top\_p=0.95</td>

        <td>
          thinking={`{"type": "enabled"}`}

          <br />

          Recommended max steps = 120
        </td>
      </tr>

      <tr>
        <td>HLE no tools</td>
        <td>1.0</td>
        <td>max tokens = 96k</td>
        <td>1</td>
        <td>top\_p=0.95</td>
        <td>thinking={`{"type": "enabled"}`}</td>
      </tr>

      <tr>
        <td>HLE w/ tools</td>
        <td>1.0</td>
        <td>total max tokens = 128k;<br />per step tokens = 48k</td>
        <td>1</td>
        <td>top\_p=0.95</td>

        <td>
          thinking={`{"type": "enabled"}`}

          <br />

          Recommended max steps = 120
        </td>
      </tr>

      <tr>
        <td>HLE heavy</td>
        <td>1.0</td>
        <td>total max tokens = 128k;<br />per step tokens = 48k</td>
        <td>1</td>
        <td>top\_p=0.95</td>

        <td>
          thinking={`{"type": "enabled"}`}

          <br />

          Recommended max steps = 200

          <br />

          parallel n=8
        </td>
      </tr>

      <tr>
        <td>HMMT2025 no tools</td>
        <td>1.0</td>
        <td>max tokens = 96k</td>
        <td>32</td>
        <td>top\_p=0.95</td>
        <td>thinking={`{"type": "enabled"}`}</td>
      </tr>

      <tr>
        <td>HMMT2025 w/tools</td>
        <td>1.0</td>
        <td>per step tokens = 96k;<br />total tokens = 96k</td>
        <td>32</td>
        <td>top\_p=0.95</td>

        <td>
          thinking={`{"type": "enabled"}`}

          <br />

          Recommended max steps = 120
        </td>
      </tr>

      <tr>
        <td>IMO-AnswerBench</td>
        <td>1.0</td>
        <td>max tokens = 96k</td>
        <td>3</td>
        <td>top\_p=0.95</td>
        <td>thinking={`{"type": "enabled"}`}</td>
      </tr>

      <tr>
        <td>GPQA-Diamond</td>
        <td>1.0</td>
        <td>max tokens = 96k</td>
        <td>8</td>
        <td>top\_p=0.95</td>
        <td>thinking={`{"type": "enabled"}`}</td>
      </tr>

      <tr>
        <td>Agentic Search Task</td>
        <td>BrowseComp / BrowseComp-ZH / Seal-0 / Frames</td>
        <td>1.0</td>
        <td>per step tokens = 24k;<br />total max tokens = 256k</td>
        <td>4</td>
        <td>top\_p=0.95</td>

        <td>
          thinking={`{"type": "enabled"}`}

          <br />

          Recommended max steps = 250

          <br />

          Recommend using a context management mechanism to prevent overly long context and ensure enough tool calls

          <br />

          Include today's date in the system prompt and let the model search when it is uncertain
        </td>
      </tr>

      <tr>
        <td>Agentic Task</td>
        <td>Tau</td>
        <td>1.0</td>
        <td>>=16k</td>
        <td>4</td>
        <td>top\_p=0.95</td>

        <td>
          thinking={`{"type": "enabled"}`}

          <br />

          Recommended max steps = 100
        </td>
      </tr>
    </tbody>
  </table>
</div>

For third-party providers, refer to Kimi Vendor Verifier (KVV) to choose high-accuracy services. Details: [https://kimi.com/blog/kimi-vendor-verifier.html](https://kimi.com/blog/kimi-vendor-verifier.html)

**Tool Use Compatibility**

When using tools, if the thinking parameter is set to `{"type": "enabled"}`, please note the following constraints to ensure model performance:

* `tool_choice` can only be set to "auto" or "none" (default is "auto") to avoid conflicts between reasoning content and the specified tool\_choice. Any other value will result in an error;
* During multi-step tool calling, you must keep the `reasoning_content` from the assistant message in the current turn's tool call within the context, otherwise an error will be thrown;
* The official builtin `$web_search` tool is temporarily incompatible with Kimi K2.5/K2.6 thinking mode, you can choose to disable thinking mode first and then use the `$web_search` tool.

You can refer to [Use Thinking Models](/guide/use-kimi-k2-thinking-model) for correct usage of tool calling.

## K2-Thinking Series Models Benchmark Recommended Settings

<div style={{ overflowX: 'auto' }}>
  <table style={{ minWidth: '800px' }}>
    <thead>
      <tr>
        <th style={{ whiteSpace: 'nowrap' }}>Category</th>
        <th style={{ whiteSpace: 'nowrap' }}>Benchmark</th>
        <th style={{ whiteSpace: 'nowrap' }}>Temperature</th>
        <th style={{ whiteSpace: 'nowrap' }}>Max token</th>
        <th style={{ whiteSpace: 'nowrap' }}>Suggested runs</th>
        <th style={{ whiteSpace: 'nowrap' }}>Notes</th>
      </tr>
    </thead>

    <tbody>
      <tr>
        <td rowSpan="3">Code</td>
        <td>SWE</td>
        <td>0.7(recommended)<br />1.0 (ok)</td>
        <td>per step tokens = 16k;<br />total max token = 256k</td>
        <td>5</td>

        <td />
      </tr>

      <tr>
        <td>Lcb + OJBench</td>
        <td>1.0</td>
        <td>max tokens = 128k</td>
        <td>1</td>

        <td />
      </tr>

      <tr>
        <td>TerminalBench</td>
        <td>1.0</td>
        <td>max tokens = 128k</td>
        <td>3</td>

        <td />
      </tr>

      <tr>
        <td rowSpan="9">Reasoning</td>
        <td>AIME2025 no tools</td>
        <td>1.0</td>
        <td>total max tokens = 96k</td>
        <td>32</td>

        <td />
      </tr>

      <tr>
        <td>AIME2025 w/ tools</td>
        <td>1.0</td>
        <td>per step tokens = 48k;<br />total max tokens = 128k</td>
        <td>16</td>
        <td>max steps = 120</td>
      </tr>

      <tr>
        <td>HLE no tools</td>
        <td>1.0</td>
        <td>max tokens = 96k</td>
        <td>1</td>

        <td />
      </tr>

      <tr>
        <td>HLE w/ tools</td>
        <td>1.0</td>
        <td>total max tokens = 128k;<br />per step tokens = 48k</td>
        <td>1</td>
        <td>max steps = 120</td>
      </tr>

      <tr>
        <td>HLE heavy</td>
        <td>1.0</td>
        <td>total max tokens = 128k;<br />per step tokens = 48k</td>
        <td>1</td>
        <td>max steps = 200<br />parallel n=8</td>
      </tr>

      <tr>
        <td>HMMT2025 no tools</td>
        <td>1.0</td>
        <td>max tokens = 96k</td>
        <td>32</td>

        <td />
      </tr>

      <tr>
        <td>HMMT2025 w/tools</td>
        <td>1.0</td>
        <td>per step tokens = 96k;<br />total tokens = 96k</td>
        <td>32</td>
        <td>max steps = 120</td>
      </tr>

      <tr>
        <td>IMO-AnswerBench</td>
        <td>1.0</td>
        <td>max tokens = 96k</td>
        <td>3</td>

        <td />
      </tr>

      <tr>
        <td>GPQA-Diamond</td>
        <td>1.0</td>
        <td>max tokens = 96k</td>
        <td>8</td>

        <td />
      </tr>

      <tr>
        <td>Agentic Search Task</td>
        <td>BrowseComp/ BrowseComp-ZH/Seal-0/ Frames</td>
        <td>1.0</td>
        <td>per step tokens = 24k;<br />total max tokens = 256k</td>
        <td>4</td>
        <td>max steps = 250<br />Enable context management to prevent context overflow and ensure enough tool calls.<br />Include today's date in the system prompt, and tell the model to search when unsure.</td>
      </tr>

      <tr>
        <td>Agentic Task</td>
        <td>Tau</td>
        <td>0.0</td>
        <td>>=16k</td>
        <td>4</td>
        <td>max steps = 100</td>
      </tr>
    </tbody>
  </table>
</div>

## API Recommendations & Notes

* **Use the official API:** some 3rd-party endpoints show noticeable accuracy drift.
* Use the recommended models for testing
  * For K2.6: use **`kimi-k2.6`** for testing
  * For K2.5: use **`kimi-k2.5`** for testing
  * For K2 series: use **`kimi-k2-thinking-turbo`** for faster inference
* **Must set:** `stream = true`
  * Non-streaming mode can lead to random mid-connection interruptions that are hard to control.
* **Current API default settings:**
  * Kimi K2.6:
    * default max\_tokens = 32768
    * default thinking = `{"type": "enabled", "keep": null}`
    * default temperature = 1.0
    * default top\_p = 0.95
    * default n = 1
    * default presence\_penalty = 0.0
    * default frequency\_penalty = 0.0
  * Kimi K2 Thinking:
    * default temp = 1.0
    * default max token = 64000
  * Kimi K2.5:
    * default max\_tokens = 32768
    * default thinking = `{"type": "enabled"}`
    * default temperature = 1.0
    * default top\_p = 0.95
    * default n = 1
    * default presence\_penalty = 0.0
    * default frequency\_penalty = 0.0
* **Timeouts:**
  * With `stream = false`, `api.moonshot.ai` timeout = **2 hours**, but some ISPs may terminate earlier.
  * So again we recommend you to set `stream = true`
* **Concurrency:**
  * Keep concurrency low to avoid rate limiting
* **Retry logic** is not optional:
  * handle overloaded
  * handle unexpected finish reason due to random server issues
  * handle errors due to complicated network issues

## FAQ

**Q1. Is the temperature setting consistent across models?**

**A.** No. Different model families use different recommended temperatures:

* k2.6 model: temperature = 1.0
* k2.5 model: temperature = 1.0
* k2-thinking series: temperature = 1.0
* k2 other series: temperature = 0.6

**Q2. Why use stream = true?**

**A.** Long outputs can take minutes. Idle TCP connections may be terminated by firewalls, load balancers, or NAT gateways. **Streaming keeps the connection alive** and significantly improves reliability. In production, requests with stream = false fail far more often than with stream = true.

**Q3. How much concurrency should I use?**

**A.** Your API account has specific rate limits (see [Recharge and Rate Limits](/pricing/limits)). Start low. If you hit **HTTP 429** (rate limit), your concurrency is too high. **Accuracy > speed,** so tune concurrency to stay within limits.

**Q5. Why should I add retry?**

**A.** Even with streaming, requests can fail due to transient network issues. **Retry** on temporary faults (network jitter, server overload, rate limiting) to avoid avoidable failures.

**Q6. Why should multi-turn or multi-step tasks include full context and reasoning?**

**A.** The model needs full context to stay logically consistent. Without previous reasoning steps, later turns can go off track or produce incomplete answers.

## Contact Us

Hit any issues? Drop us an email at [**api-service@moonshot.ai**](mailto:api-service@moonshot.ai) with your logs. We'll take a look!