Kimi-K2.6
Version: 2026-04-20
Direct from Azure models
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Key capabilities
About this model
Kimi K2.6 is an open-source, native multimodal agentic model that advances practical capabilities in long-horizon coding, coding-driven design, proactive autonomous execution, and swarm-based task orchestration.Key model capabilities
- Long-Horizon Coding: K2.6 achieves 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: K2.6 is capable of transforming 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: Scaling horizontally to 300 sub-agents executing 4,000 coordinated steps, K2.6 can dynamically decompose 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: For autonomous tasks, K2.6 demonstrates strong performance in powering persistent, 24/7 background agents that proactively manage schedules, execute code, and orchestrate cross-platform operations without human oversight.
Use cases
See Responsible AI for additional considerations for responsible use.Key use cases
The provider has not supplied this information.Out of scope use cases
The provider has not supplied this information.Pricing
Pricing is based on a number of factors, including deployment type and tokens used. See pricing details here.Technical specs
| Architecture | Mixture-of-Experts (MoE) |
| Total Parameters | 1T |
| Activated Parameters | 32B |
| Number of Layers (Dense layer included) | 61 |
| Number of Dense Layers | 1 |
| Attention Hidden Dimension | 7168 |
| MoE Hidden Dimension (per Expert) | 2048 |
| Number of Attention Heads | 64 |
| Number of Experts | 384 |
| Selected Experts per Token | 8 |
| Number of Shared Experts | 1 |
| Vocabulary Size | 160K |
| Context Length | 256K |
| Attention Mechanism | MLA |
| Activation Function | SwiGLU |
| Vision Encoder | MoonViT |
| Parameters of Vision Encoder | 400M |
Training cut-off date
The provider has not supplied this information.Training time
The provider has not supplied this information.Input formats
Text, ImageOutput formats
TextSupported languages
EnglishSample JSON response
The provider has not supplied this information.Model architecture
The provider has not supplied this information.Long context
The provider has not supplied this information.Optimizing model performance
The provider has not supplied this information.Additional assets
Please see MoonshotAI's Kimi-K2.6 model card here .Training disclosure
Training, testing and validation
The provider has not supplied this information.Distribution
Distribution channels
The provider has not supplied this information.More information
The provider has not supplied this information.Responsible AI considerations
Safety techniques
Kimi-K2.6 poses an elevated risk of producing content that would be blocked by the Foundry Models Protected Material Detection filter . When deployed via Microsoft Foundry, prompts and completions are passed through a default configuration of classification models to detect and prevent the output of harmful content. We recommend customers use the Protected Material Detection filter in conjunction with this model. As with any model, customers should conduct thorough evaluations on production systems before launching, as well as appropriate post-launch monitoring. All customers must comply with the Microsoft Enterprise AI Services Code of Conduct. Configuration options for content filtering vary when you deploy a model for production in Azure AI; learn more .Safety evaluations
The provider has not supplied this information.Known limitations
The provider has not supplied this information.Acceptable use
Acceptable use policy
The provider has not supplied this information.Evaluation Results
| Benchmark | Kimi K2.6 | GPT-5.4 (xhigh) | Claude Opus 4.6 (max effort) | Gemini 3.1 Pro (thinking high) | Kimi K2.5 |
|---|---|---|---|---|---|
| Agentic | |||||
| HLE-Full (w/ tools) | 54.0 | 52.1 | 53.0 | 51.4 | 50.2 |
| BrowseComp | 83.2 | 82.7 | 83.7 | 85.9 | 74.9 |
| BrowseComp (Agent Swarm) | 86.3 | 78.4 | |||
| DeepSearchQA (f1-score) | 92.5 | 78.6 | 91.3 | 81.9 | 89.0 |
| DeepSearchQA (accuracy) | 83.0 | 63.7 | 80.6 | 60.2 | 77.1 |
| WideSearch (item-f1) | 80.8 | - | - | - | 72.7 |
| Toolathlon | 50.0 | 54.6 | 47.2 | 48.8 | 27.8 |
| MCPMark | 55.9 | 62.5* | 56.7* | 55.9* | 29.5 |
| Claw Eval (pass3) | 62.3 | 60.3 | 70.4 | 57.8 | 52.3 |
| Claw Eval (pass@3) | 80.9 | 78.4 | 82.4 | 82.9 | 75.4 |
| APEX-Agents | 27.9 | 33.3 | 33.0 | 32.0 | 11.5 |
| OSWorld-Verified | 73.1 | 75.0 | 72.7 | - | 63.3 |
| Coding | |||||
| Terminal-Bench 2.0 (Terminus-2) | 66.7 | 65.4* | 65.4 | 68.5 | 50.8 |
| SWE-Bench Pro | 58.6 | 57.7 | 53.4 | 54.2 | 50.7 |
| SWE-Bench Multilingual | 76.7 | - | 77.8 | 76.9* | 73.0 |
| SWE-Bench Verified | 80.2 | - | 80.8 | 80.6 | 76.8 |
| SciCode | 52.2 | 56.6 | 51.9 | 58.9 | 48.7 |
| OJBench (python) | 60.6 | - | 60.3 | 70.7 | 54.7 |
| LiveCodeBench (v6) | 89.6 | - | 88.8 | 91.7 | 85.0 |
| Reasoning & Knowledge | |||||
| HLE-Full | 34.7 | 39.8 | 40.0 | 44.4 | 30.1 |
| AIME 2026 | 96.4 | 99.2 | 96.7 | 98.3 | 95.8 |
| HMMT 2026 (Feb) | 92.7 | 97.7 | 96.2 | 94.7 | 87.1 |
| IMO-AnswerBench | 86.0 | 91.4 | 75.3 | 91.0* | 81.8 |
| GPQA-Diamond | 90.5 | 92.8 | 91.3 | 94.3 | 87.6 |
| Vision | |||||
| MMMU-Pro | 79.4 | 81.2 | 73.9 | 83.0* | 78.5 |
| MMMU-Pro (w/ python) | 80.1 | 82.1 | 77.3 | 85.3* | 77.7 |
| CharXiv (RQ) | 80.4 | 82.8* | 69.1 | 80.2* | 77.5 |
| CharXiv (RQ) (w/ python) | 86.7 | 90.0* | 84.7 | 89.9* | 78.7 |
| MathVision | 87.4 | 92.0* | 71.2* | 89.8* | 84.2 |
| MathVision (w/ python) | 93.2 | 96.1* | 84.6* | 95.7* | 85.0 |
| BabyVision | 39.8 | 49.7 | 14.8 | 51.6 | 36.5 |
| BabyVision (w/ python) | 68.5 | 80.2* | 38.4* | 68.3* | 40.5 |
| V* (w/ python) | 96.9 | 98.4* | 86.4* | 96.9* | 86.9 |
Model Specifications
Context Length262144
LicenseOther
Last UpdatedApril 2026
Input TypeText,Image
Output TypeText
ProviderMoonshot AI
Languages1 Language