Kimi K2.6

Kimi K2.6 is an open-source, native multimodal agentic model from Moonshot AI that advances practical capabilities in long-horizon coding, coding-driven design, proactive autonomous execution, and swarm-based task orchestration. It is a Mixture-of-Experts model with 1 trillion total parameters and 32 billion activated per token, built on the Kimi K2.5 architecture.

Key Features

• Long-Horizon Coding — Significant improvements on complex, end-to-end coding tasks, generalizing robustly across programming languages (Rust, Go, Python) and domains spanning front-end, DevOps, and performance optimization.
• Coding-Driven Design — Transforms simple prompts and visual inputs into production-ready interfaces and lightweight full-stack workflows, generating structured layouts, interactive elements, and rich animations with deliberate aesthetic precision.
• Elevated Agent Swarm — Scales horizontally to 300 sub-agents executing 4,000 coordinated steps; dynamically decomposes tasks into parallel, domain-specialized subtasks, delivering end-to-end outputs from documents to websites to spreadsheets in a single autonomous run.
• Proactive & Open Orchestration — Demonstrates strong performance in powering persistent 24/7 background agents that proactively manage schedules, execute code, and orchestrate cross-platform operations without human oversight.
• Thinking & Instant Modes — Supports reasoning (thinking) mode by default and an instant-response mode; preserve_thinking retains full reasoning content across multi-turn interactions for coding-agent scenarios.
• Multimodal Input — Accepts text, image, and video input via the MoonViT vision encoder (400M parameters).

Model Summary

| | | |:---|:---| | Architecture | Mixture-of-Experts (MoE) | | Total Parameters | 1T | | Activated Parameters | 32B | | Number of Layers | 61 (1 dense + 60 MoE) | | Number of Experts | 384 (8 selected per token, 1 shared) | | Attention Hidden Dimension | 7168 | | MoE Hidden Dimension per Expert | 2048 | | Number of Attention Heads | 64 | | Vocabulary Size | 160K | | Context Length | 256K | | Attention Mechanism | MLA | | Activation Function | SwiGLU | | Vision Encoder | MoonViT (400M parameters) |

Kimi K2.6 ships with native INT4 quantization, using the same method as Kimi K2 Thinking.

Benchmarks

Agentic

• HLE-Full (with tools): 54.0
• BrowseComp: 83.2 (86.3 with Agent Swarm)
• DeepSearchQA (f1-score): 92.5
• DeepSearchQA (accuracy): 83.0
• WideSearch (item-f1): 80.8
• Toolathlon: 50.0
• MCPMark: 55.9
• Claw Eval (pass^3): 62.3; (pass@3): 80.9
• APEX-Agents: 27.9
• OSWorld-Verified: 73.1

Coding

• Terminal-Bench 2.0 (Terminus-2): 66.7
• SWE-Bench Pro: 58.6
• SWE-Bench Multilingual: 76.7
• SWE-Bench Verified: 80.2
• SciCode: 52.2
• OJBench (python): 60.6
• LiveCodeBench (v6): 89.6

Reasoning & Knowledge

• HLE-Full: 34.7
• AIME 2026: 96.4
• HMMT 2026 (Feb): 92.7
• IMO-AnswerBench: 86.0
• GPQA-Diamond: 90.5

Vision

• MMMU-Pro: 79.4 (80.1 with python)
• CharXiv (RQ): 80.4 (86.7 with python)
• MathVision: 87.4 (93.2 with python)
• BabyVision: 39.8 (68.5 with python)
• V* (with python): 96.9

Use Cases

• Autonomous agentic workflows spanning coding, research, and browsing
• Long-horizon software engineering and multi-step code generation
• Coding-driven UI/UX design from prompts and visual inputs
• Document, chart, and image understanding at scale
• Multi-agent task orchestration with parallel sub-agent coordination
• Persistent background agents for schedule management and cross-platform operations