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.
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. Evaluated using the official protocol, preserving the original input order and prepending images to the text input.
PerceptionBench. An in-house benchmark focused on atomic visual perception capabilities.
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
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 , Anthropic , and GLM-5.3 .
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.
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
You can access Kimi K3.1 Pro's API on 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:
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 and Thinking Effort .
6. License
Kimi K3.1 Pro is released under the Kimi K3.1 Pro License .
7. Contact Us
If you have any questions, please reach out at support@moonshot.ai .
Model tree for moonshotai/Kimi-K3.1-Pro
Space using moonshotai/Kimi-K3.1-Pro 1
Evaluation results
Idavidrein/gpqa · Diamond View evaluation results leaderboard 94.2
harborframework/terminal-bench · Terminalbench 4 View evaluation results leaderboard 65.7 *
datacurve/deep-swe · Deep Swe View evaluation results leaderboard 75.5 *
mercor/apex-agents · Apex Agents View evaluation results leaderboard 69.4
harborframework/terminal-bench-science · Terminalbenchscience 0 1 View evaluation results leaderboard 38.1 *
agents-last-exam/agents-last-exam leaderboard
Overall Pass Rate View evaluation results ALE v1.1 (ALL), Kimi Code harness. Card does not name the metric; mapped to pass rate. 43.2 *
Ale Cli Pass Rate View evaluation results ALE v1.1 (CLI), Kimi Code harness. Card does not name the metric; mapped to pass rate. 44.2 *