月之暗面发布Kimi K3.1 Pro开源权重及详细评测

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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

* Compared with Kimi K3, we replace the full-attention Gated MLA layers with Gated DeepSeek Sparse Attention (Gated DSA) layers for more efficient long-context modeling.

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.

Evaluation Footnotes

Coding benchmarks

• 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. All remaining scores are the maximum values reported in model blog posts or on the official Terminal-Bench leaderboard.
• 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.
• 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.
• 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; remaining scores are from Vals AI.
• DeepSWE v1.1. K3.1 Pro is evaluated with Claude Code. Other scores come from the respective technical reports for GPT-6 Astra, Claude Opus 5.5, Claude Fable 5.1, DeepSeek-V4.1-Flash-1017, and GLM-5.3.
• 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.
• 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.
• 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

• BioMysteryBench. Uses the official v11 release, 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.
• Agents' Last Exam. Uses the official v1.1 release, 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.