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📰 Tech Blog | 🚀 API Quickstart 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.
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Model Summary Architecture Mixture-of-Experts (MoE) Total Parameters 2.8T Activated Parameters 104B Number of Layers 93 Number of Dense Layers 1 Attention-Layer Composition 69 KDA + 24 Gated DSA* Attention Hidden Dimension 7168 Number of Attention Heads 96 DSA Index Head Dimension 128 DSA Index Number of Heads 32 DSA Index Top-K 2048 Latent MoE Dimension 3584 MoE Hidden Dimension (per Expert) 3072 Number of Experts 896 Selected Experts per Token 16 Number of Shared Experts 2 Vocabulary Size 160K Context Length 1048576 Attention Mechanism KDA & Gated DSA Activation Function SiTU-GLU Vision Encoder MoonViT-V2 Parameters of Vision Encoder 401M Quantization MXFP4 weights / MXFP8 activations(quantization-aware training) Modality Text, Image * 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.
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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.
Benchmark Kimi K3.1 Pro(max) Claude Fable 5.1(max, w/ fallback) GPT-6 Astra(max, w/ fallback) Claude Opus 5.5(max, w/ fallback) DeepSeek-V4.1-Flash-1017(max) GLM-5.3(max) Coding Terminal-Bench 4.0 65.7 57.9 58.2 66.4 42.3 41.8 Terminal-Bench-Science 0.1 38.1 40.0 68.1 63.3 15.7 8.1 SWE Marathon v1.1 51.7 45.6 42.5 46.3 40.0 42.5 BenchCAD w/o tools 72.8 60.6 — 73.0 41.2 — BenchCAD w/ tools 92.7 92.6 95.9 96.2 81.6 — Program Bench 31.5 53.5 50.0 65.0 29.1 19.0 DeepSWE v1.1 75.5 67.4 74.1 74.2 77.0 66.9 PostTrain Bench v1.2 36.5 44.6 41.9 43.8 30.5 34.5 Kimi Code Bench 2.0 (Internal) 77.1 — — — 76.1 61.7 Kimi Code Bench 3.0 Long-horizon (Internal) 61.2 58.8 66.5 58.5 51.5 45.5 Agentic HLE-Full w/ tools 70.0 65.6 57.2 67.7 58.8 62.5 BrowseComp 92.1 — 91.5 90.8 — — BioMysteryBench 80.6 81.6 78.9 81.9 80.0 79.6 CharXiv (RQ) w/ python 93.4 92.7 92.8 93.8 85.6 — MMMU-Pro w/ python 90.0 88.4 87.8 89.8 82.5 — GDP.pdf w/ python 85.7 83.8 85.5 83.9 73.0 — ChartoGraphy w/ python 90.2 88.4 91.0 89.0 83.0 — BabyVision w/ python 95.4 95.1 96.6 97.7 92.5 — ZeroBench_main w/ python (pass@5) 63.0 59.0 58.0 61.0 51.0 — Agents' Last Exam (ALL) 43.2 — 41.8 42.6 37.7 26.2 Agents' Last Exam (CLI) 44.2 — 44.2 40.7 36.0 29.4 Automation Bench-AA 73.3 59.4 68.5 69.5 66.6 62.2 GDPval-AA v2.1 (Elo-score) 1846 1754 1573 1866 1746 1650 AA-Briefcase v1.1 (Elo-score) 1678 1672 1569 1803 1570 1507 APEX-Agents 69.4 68.6 64.7 73.5 69.8 56.6 OfficeQA Pro v2 64.4 54.5 54.4 62.2 40.4 31.1 OfficeQA Pro 67.4 68.6 66.2 — 62.4 58.6 Frontier Finance 58.6 60.8 45.0 56.7 54.5 57.5 Harvey Legal Agent 23.7 19.1 — — 21.0 — HANDBOOK.md 44.6 38.8 — 41.5 28.9 20.8 OSWorld V2.1 79.6 80.7 75.8 81.8 69.3 — Business Operation (Internal) 37.8 30.7 32.3 — 8.9 18.6 DECK-Arena (Internal, Elo-score) 1456 — 1423 1253 1231 1119 Design Bench (Internal, Elo-score) 957 — 1076 927 863 718 Kimi Hallu Bench Hard w/ tools (Internal, lower is better) 8.3 23.3 5.5 15.0 23.3 22.6 Reasoning & Knowledge & Multimodal GDP.pdf 34.6 27.6 34.2 30.6 17.2 11.2 HLE-Full 51.1 57.6 53.6 59.8 42.6 43.0* HLE-Diamond 39.4 55.1 65.8 60.9 21.6 25.1* GPQA-Diamond 94.2 93.7 96.1 93.9 92.2 91.7 CharXiv (RQ) 89.0 88.8 90.0 92.1 77.0 — MMMU-Pro 84.1 86.3 86.9 87.7 80.6 — ZeroBench_main (avg@5 / pass@5) 29.8 / 39.0 23.0 / 35.0 43.2 / 52.0 35.0 / 43.0 11.8 / 23.0 — PerceptionBench 68.3 63.0 77.6 72.1 47.7 — Kimi Hallu Bench w/o tools (Internal, lower is better) 5.4 11.4 4.2 6.8 13.1 14.8 Cyber Security ExploitBench 74.7 83.0 100 91.0 — 54.4 ExploitGym 20.7 30.4 42.4 34.5 — 15.0 xdev-bench-hard (Internal) 81.0 — — — 85.7 57.1 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.
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.
Frontier Finance. Evaluated with the official Finance Agent v2 harness.
HANDBOOK.md. Evaluated with the official OpenHands-based harness, using tasks from the benchmark's early open-source v1 release.
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.
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.
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.
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.
GDPval-AA v2.1, AA-Briefcase v1.1, and AutomationBench-AA.
Scores are cited from Artificial Analysis. APEX-Agents. Scores are cited from the official leaderboard. 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 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. 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.
Run counts.
GDP.pdf, ZeroBench, and ChartoGraphy are averaged over five runs; other multimodal benchmarks are averaged over four.
MMMU-Pro.