# Kimi K3.1 Pro

URL: https://interfaze.ai/models/moonshotaikimi-k31-pro

[All models](https://interfaze.ai/models)

Kimi K3.1 Pro by moonshotai, a image-text-to-text model with multimodal capabilities. Understand and compare multimodal features, benchmarks, and capabilities.

## Comparison

| Feature | Kimi K3.1 Pro | Interfaze |
| --- | --- | --- |
| Input Modalities | text, image, video | image, text, audio, video, document |
| Native OCR | No | Yes |
| Long Document Processing | Yes | Yes |
| Language Support | unknown | 162+ |
| Native Speech-to-Text | No | Yes |
| Native Object Detection | No | Yes |
| Guardrail Controls | No | Yes |
| Context Input Size | 1M | 1M |
| Tool Calling | Yes | Tool calling supported + built in browser, code execution and web search |

### Scaling

| Feature | Kimi K3.1 Pro | Interfaze |
| --- | --- | --- |
| Scaling | Self-hosted/Provider-hosted with quantization | Unlimited |

[Try Interfaze](https://interfaze.ai/dashboard)[Read the Docs](https://interfaze.ai/docs)

View model card on [Hugging Face](https://huggingface.co/moonshotai/Kimi-K3.1-Pro)

## 1\. Model Introduction

Kimi K3.1 Pro is an open-weight, natively multimodal agentic model, and our most capable 3T-class model to date. Post-trained from Kimi K3 with large-scale joint reinforcement learning across hundreds of downstream domains, it is the first model in the K3.1 series. With a 1-million-token context window, Kimi K3.1 Pro delivers substantial improvements in coding, 3D creation, financial research, factual accuracy, and other agentic capabilities.

## 2\. Model Summary

## 3\. Evaluation Results

Unless otherwise specified, all Kimi K3.1 Pro results use reasoning effort `max`, `temperature = 1.0`, and `top-p = 0.95`. A dash (`—`) means that the source table does not report a score. Higher scores are better except where the benchmark name explicitly states `lower is better`; Elo scores and other benchmark-specific metrics are not directly comparable with accuracy or pass-rate scores.

#### Coding benchmarks

1.  **Terminal-Bench 4.0.** Kimi K3.1 Pro is evaluated with the Claude Code harness. The DeepSeek-V4.1-Flash-1017 score is taken from its [release blog](https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash-1017). All remaining scores are the maximum values reported in model blog posts or on the [official Terminal-Bench leaderboard](https://www.tbench.ai/).
2.  **Terminal-Bench-Science 0.1.** Kimi K3.1 Pro is evaluated with Claude Code. Internal evaluation of DeepSeek-V4.1-Flash-1017 yields 9.3; the table instead reports 15.7 for the older DeepSeek-V4.1-Flash from the official leaderboard. All remaining scores are from the [Terminal-Bench-Science leaderboard](https://www.terminal-bench-science.ai/).
3.  **SWE-Marathon v1.1.** Kimi K3.1 Pro and DeepSeek-V4.1-Flash-1017 are evaluated with Claude Code. Other scores are from the [official leaderboard](https://www.swe-marathon.org/).
4.  **ProgramBench.** The table reports binary scores: a task receives 1 if its ProgramBench score is at least 95%, and 0 otherwise. K3.1 Pro is evaluated with Claude Code. DeepSeek and GLM scores are taken from the [DeepSeek release blog](https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash-1017); remaining scores are from [Vals AI](https://www.vals.ai/benchmarks/programbench).
5.  **DeepSWE v1.1.** K3.1 Pro is evaluated with Claude Code. Other scores come from the respective technical reports for [GPT-6 Astra](https://openai.com/index/gpt-6-astra/), [Claude Opus 5.5](https://www.anthropic.com/claude-opus-5-5-system-card), [Claude Fable 5.1](https://www.anthropic.com/claude-fable-5-1-mythos-5-1-system-card), [DeepSeek-V4.1-Flash-1017](https://api-docs.deepseek.com/news/news261017/), and [GLM-5.3](https://z.ai/blog/glm-5.3).
6.  **PostTrainBench.** K3.1 Pro and DeepSeek-V4.1-Flash-1017 are evaluated internally on v1.2 with Claude Code and H20 GPUs rather than H100 GPUs. For stable evaluation, all judge models use DeepSeek-V4.1-Flash-1017 at maximum effort, which best matched the official GPT-5.6-Terra (xhigh) judge in testing. Other scores are from the [official leaderboard](https://posttrainbench.com/).
7.  **Kimi Code Bench 2.0 and Kimi Code Bench 3.0 Long-horizon.** These are internal benchmarks. K3.1 Pro is evaluated with Claude Code; all other models use their maximum-effort setting. Proprietary-model testing may involve fallback behavior.
8.  **BenchCAD.** Open models are evaluated on 1,000-case subsets randomly sampled from the 17,900-case Vision2Code dataset at maximum reasoning effort. With-tools results use Claude Code; without-tools results use single-shot generation with official prompts and scoring. Claude scores are from the Opus 5.5 System Card, and GPT-6 Astra's with-tools score is from its official report. GPT-6 Astra has no reported without-tools score.

#### Agentic benchmarks

1.  **BioMysteryBench.** Uses the [official v11 release](https://huggingface.co/datasets/Anthropic/BioMysteryBench-full/commit/b5a889c), with Kimi K3 as judge. K3.1 Pro uses Kimi Code; GLM-5.3 and DeepSeek-V4.1-Flash-1017 use Claude Code; GPT-6 Astra uses Codex. Claude scores are from official system cards.
2.  **Agents' Last Exam.** Uses the [official v1.1 release](https://github.com/rdi-berkeley/agents-last-exam), comprising 122 tasks, including 86 CLI tasks. Runs that bypass stated requirements receive zero. DeepSeek and Kimi are evaluated with Claude Code and Kimi Code, respectively; other results are from the official benchmark website.
3.  **OfficeQA Pro and OfficeQA Pro v2.** GPT-6 Astra uses Codex; other models use Claude Code. Each task provides the complete PDF corpus as page images without machine-readable text. External-model trajectories that retrieve reference answers online receive zero for those tasks.
4.  **Frontier Finance.** Evaluated with the official Finance Agent v2 harness.
5.  **HANDBOOK.md.** Evaluated with the official OpenHands-based harness, using tasks from the benchmark's early open-source v1 release.
6.  **Harvey Legal Agent.** Evaluated on 1,251 public tasks with Artificial Analysis's Stirrup harness. The reported score is the all-pass rate: the percentage of tasks passing every rubric criterion.
7.  **OSWorld V2.1.** Kimi models use the Kimi Agent harness in the official OSWorld v2 repository. GPT-6 Astra's score is taken from Muse Wonder's technical report.
8.  **DECK-Arena and Design Bench.** DECK-Arena evaluates presentation generation on real-world queries; Design Bench covers graphic design through direct and reference-image few-shot prompting. Both evaluate python-pptx, pptxjs, and Kimi's in-house DSL in a harness with integrated search and image generation. The automated grader is calibrated on more than 10,000 human judgments.
9.  **BrowseComp.** Uses context compaction triggered at 200K tokens and a 500-step maximum. If the limit is reached, the agent is forced to answer before judging. All runs use `temperature = 1.0` and `top-p = 1.0`.
10.  **GDPval-AA v2.1, AA-Briefcase v1.1, and AutomationBench-AA.** Scores are cited from [Artificial Analysis](https://artificialanalysis.ai/). **APEX-Agents.** Scores are cited from the [official leaderboard](https://www.mercor.com/apex/apex-agents-leaderboard/).
11.  **Tool-augmented multimodal evaluations.** GDP.pdf w/ python, ZeroBench w/ python, and ChartoGraphy w/ python are averaged over five runs; other multimodal benchmarks with Python are averaged over four. For GDP.pdf w/ python, each original PDF is sent to a sandbox for IPython processing. Responses are graded against each rubric criterion using Gemini-3.5-Flash at medium reasoning effort and the official judging prompt. The reported score is the task-macro mean rubric pass rate.

#### Reasoning, knowledge, and multimodal benchmarks

1.  **GDP.pdf.** Each PDF is provided as page images resized to a longest edge of 1,152 pixels, interleaved with Docling-extracted text in page order. Responses are graded against each rubric criterion using Gemini-3.5-Flash Medium and the official judging prompt. The table reports the strict pass rate among fully scored responses. GLM-5.3 Max and DeepSeek-V4.1-Flash-1017 Max scores are cited from [Artificial Analysis](https://artificialanalysis.ai/).
2.  **HLE-Full and HLE-Diamond.** The full evaluation sets contain 2,500 and 1,000 questions, respectively. For scores marked `*`, accuracy is reported on the text-only subset.
3.  **Run counts.** GDP.pdf, ZeroBench, and ChartoGraphy are averaged over five runs; other multimodal benchmarks are averaged over four.
4.  **MMMU-Pro.** Evaluated using the official protocol, preserving the original input order and prepending images to the text input.
5.  **PerceptionBench.** An in-house benchmark focused on atomic visual perception capabilities.
6.  **Kimi Hallucination Benchmark.** Evaluates factual accuracy on an internal set of challenging, de-identified prompts drawn from practical work tasks. The table reports claim-level hallucination rates assessed by fact-checking agents.

#### Cybersecurity benchmarks

1.  **ExploitBench.** K3.1 Pro is evaluated with the official harness under a 300-turn budget. The table reports the pass@5 maximum-capability score: for each environment, the best of five seeds is scored by capability flags reached out of 16; arbitrary code execution counts as all 16 capabilities. Comparison scores are from [OpenAI](https://openai.com/index/gpt-6-astra/), [Anthropic](https://www.anthropic.com/claude-opus-5-5-system-card), and [GLM-5.3](https://z.ai/blog/glm-5.3).
2.  **ExploitGym.** K3.1 Pro is evaluated with Kimi Code under a six-hour wall-clock budget per task. GPT-6 Astra, Claude, and GLM comparison scores are taken from their respective official reports, with Claude results using the same six-hour budget.
3.  **xdev-bench-hard.** K3.1 Pro is evaluated with Kimi Code; GLM-5.3 and DeepSeek-V4.1-Flash-1017 use Claude Code. The internal benchmark contains expert-curated, source-code-based vulnerability discovery and exploitation tasks. The final model is evaluated at pass@3 over a random subset of runs, without hard timeouts or output-token limits.

## 4\. Deployment and Availability

> \[!Note\] You can access Kimi K3.1 Pro's API on [https://platform.kimi.ai](https://platform.kimi.ai) by selecting `kimi-k3.1-pro`, and we provide OpenAI/Anthropic-compatible API for you. Currently, Kimi K3.1 Pro is recommended to run on the following inference engines:

-   [vLLM](https://github.com/vllm-project/vllm) — see [recipes](https://recipes.vllm.ai/moonshotai/Kimi-K3.1-Pro)
-   [SGLang](https://github.com/sgl-project/sglang) — see [cookbook](https://docs.sglang.io/cookbook/autoregressive/Moonshotai/Kimi-K3.1-Pro)
-   [TokenSpeed](https://github.com/lightseekorg/tokenspeed) — see [recipes](https://lightseek.org/tokenspeed/recipes/models#kimi-k3-1-pro)

* * *

## 5\. Model Usage

The thinking effort of Kimi K3.1 Pro is configured with the top-level `reasoning_effort` request field, which supports `"low"`, `"high"`, and `"max"` (default `"max"`).

For multi-turn conversations and tool calls, Kimi K3.1 Pro requires the complete assistant message returned by the API to be passed back to `messages` as-is — including `reasoning_content` and `tool_calls`, not just `content`:

```
import openai

def chat_with_preserved_thinking(client: openai.OpenAI, model_name: str):
    messages = [
        {
            "role": "user",
            "content": "Tell me three random numbers."
        },
        {
            "role": "assistant",
            "reasoning_content": "I'll start by listing five numbers: 473, 921, 235, 215, 222, and I'll tell you the first three.",
            "content": "473, 921, 235"
        },
        {
            "role": "user",
            "content": "What are the other two numbers you have in mind?"
        }
    ]

    response = client.chat.completions.create(
        model=model_name,
        messages=messages,
        stream=False,
        max_tokens=4096,
        reasoning_effort="max",
    )
    # the assistant should mention 215 and 222 that appear in the prior reasoning content
    print(f"response: {response.choices[0].message.reasoning}")
    return response.choices[0].message.content
```

For full guides and examples (vision input, structured output, partial mode, tool choice, dynamic tool loading, context caching), see the [Kimi K3.1 Pro Quickstart](https://platform.kimi.ai/docs/guide/kimi-k3-1-pro-quickstart) and [Thinking Effort](https://platform.kimi.ai/docs/guide/use-thinking-effort).

## 6\. License

Kimi K3.1 Pro is released under the [Kimi K3.1 Pro License](https://huggingface.co/moonshotai/Kimi-K3.1-Pro/blob/main/LICENSE).

## 7\. Contact Us

If you have any questions, please reach out at [support@moonshot.ai](mailto:support@moonshot.ai).

## Want more deterministic results?

[Try Interfaze](https://interfaze.ai/dashboard)[Read the Docs](https://interfaze.ai/docs)