Kimi K3.1 Pro

Architectural Overview & Capability Profile

Kimi K3.1 Pro is developed by Moonshot AI as the flagship open-intelligence reasoning model, engineered to surpass leading closed proprietary systems across agentic autonomy, software engineering, long-context analysis, and professional knowledge workflows. On the comprehensive RankLLMs composite evaluation index, Kimi K3.1 Pro achieves an overall rating of 63.2 / 100, establishing itself as the #1 Open-Weights Large Language Model in the world (Rank #5 Overall).

Released with open weights and served via high-performance API endpoints, Kimi K3.1 Pro features native support for a 1-million-token context window with deep chain-of-thought deliberate reasoning.

Benchmark Performance & Real-World Utility

In rigorous standardized benchmarks published in the official Kimi K3.1 Pro technical release, the model achieves unprecedented open-weights breakthroughs:

• Autonomous Agentic Coding:

  • SWE Marathon v1.1 (51.7%): Captures #1 Global SOTA, outperforming Claude Opus 5.5 (46.3%), Claude Fable 5.1 (45.6%), and GPT-6 Astra (42.5%) in multi-hour autonomous software maintenance.
  • Terminal-Bench 4.0 (65.7%): SOTA among all open-weights models, closely matching Claude Opus 5.5 (66.4%) in complex terminal tool use.
  • DeepSWE v1.1 (75.5%): High-accuracy GitHub repository issue localization and patching.
  • Kimi Code Bench 2.0 (77.1%) & 3.0 Long-horizon (61.2%): Superior sustained reasoning on complex engineering tasks.

• Agentic Automation & Tool Use:

  • AutomationBench-AA (73.3%): #1 Global SOTA, surpassing Claude Opus 5.5 (69.5%) and GPT-6 Astra (68.5%) in real-world API workflow automation.
  • BrowseComp (92.1%): #1 Worldwide in autonomous web browsing, search synthesis, and information verification.
  • Humanity’s Last Exam (HLE) w/ tools (70.0%): #1 Global SOTA on multidisciplinary graduate-level problems.
  • Agents’ Last Exam (43.2% ALL / 44.2% CLI): Leads frontier proprietary models in multi-tool command-line execution.
  • OfficeQA Pro v2 (64.4%) & Harvey Legal Agent (23.7%): State-of-the-art enterprise document and legal workflow assistance.

• Multimodal Intelligence & Visual Synthesis:

  • MMMU-Pro w/ python (90.0%): #1 in multimodal mathematical and visual problem solving.
  • GDP.pdf w/ python (85.7%): #1 in high-density document parsing and financial extraction.
  • DECK-Arena (1456 Elo) & GDPval-AA (1846 Elo): High human preference alignment in presentation design and slide generation.

• Scientific Reasoning & Logic:

  • GPQA Diamond (94.2%): PhD-level scientific reasoning across physics, biology, and chemistry.
  • ZeroBench_main w/ python (63.0% pass@5): #1 in zero-shot algorithmic verification.

Inference Latency & Cost Architecture

• Token Context Window: 1,048,576 tokens native context window.
• Inference Speed: Clocked at 75 tokens/second during full deliberate reasoning loops.
• Commercial API Pricing: Billed at $2.00 per 1M input tokens and $10.00 per 1M output tokens, with a 90% cache discount reducing cached input to $0.20 per 1M tokens.

Strengths, Limitations & Ideal Workflows

Primary Strengths

• #1 Open-Weights model globally (63.2 overall), defeating proprietary closed systems across core agent evaluations
• Global SOTA in autonomous agent workflows: #1 on SWE Marathon v1.1 (51.7%), AutomationBench-AA (73.3%), and BrowseComp (92.1%)
• #1 Multimodal Agent Reasoning: 90.0% on MMMU-Pro, 85.7% on GDP.pdf w/ python, and 70.0% on HLE w/ tools
• 1M native context window with $2.00 / $10.00 pricing and 90% cache discount

Known Considerations

• Local self-hosting of full precision weights requires dedicated multi-GPU inference clusters (e.g. 8x H100 / H200).

Recommended Use Cases

Autonomous coding agents, CI/CD pull request verification, complex legal and financial document synthesis, enterprise tool orchestration, and long-horizon research automation.

Related Comparisons & Leaderboard Context

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