Kimi-K2.5
Version: 1
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Key capabilities
About this model
Kimi K2.5 is an open-source, native multimodal agentic model built through continual pretraining on approximately 15 trillion mixed visual and text tokens atop Kimi-K2-Base. It seamlessly integrates vision and language understanding with advanced agentic capabilities, instant and thinking modes, as well as conversational and agentic paradigms.Key model capabilities
- Native Multimodality: Pre-trained on vision–language tokens, K2.5 excels in visual knowledge, cross-modal reasoning, and agentic tool use grounded in visual inputs.
- Coding with Vision: K2.5 generates code from visual specifications (UI designs, video workflows) and autonomously orchestrates tools for visual data processing.
- Agent Swarm: K2.5 transitions from single-agent scaling to a self-directed, coordinated swarm-like execution scheme. It decomposes complex tasks into parallel sub-tasks executed by dynamically instantiated, domain-specific agents.
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
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The provider has not supplied this information.Output formats
The provider has not supplied this information.Supported languages
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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-Thinking model card here.Training disclosure
Training, testing and validation
The provider has not supplied this information.Distribution
Distribution channels
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The provider has not supplied this information.Responsible AI considerations
Safety techniques
Kimi-K2.5 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
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Acceptable use policy
The provider has not supplied this information.| Benchmark | Kimi K2.5 (Thinking) | GPT-5.2 (xhigh) | Claude 4.5 Opus (Extended Thinking) | Gemini 3 Pro (High Thinking Level) | DeepSeek V3.2 (Thinking) | Qwen3-VL- 235B-A22B- Thinking | |
|---|---|---|---|---|---|---|---|
| Reasoning & Knowledge | |||||||
| HLE-Full | 30.1 | 34.5 | 30.8 | 37.5 | 25.1† | - | |
| HLE-Full (w/ tools) | 50.2 | 45.5 | 43.2 | 45.8 | 40.8† | - | |
| AIME 2025 | 96.1 | 100 | 92.8 | 95.0 | 93.1 | - | |
| HMMT 2025 (Feb) | 95.4 | 99.4 | 92.9* | 97.3* | 92.5 | - | |
| IMO-AnswerBench | 81.8 | 86.3 | 78.5* | 83.1* | 78.3 | - | |
| GPQA-Diamond | 87.6 | 92.4 | 87.0 | 91.9 | 82.4 | - | |
| MMLU-Pro | 87.1 | 86.7* | 89.3* | 90.1 | 85.0 | - | |
| Image & Video | |||||||
| MMMU-Pro | 78.5 | 79.5* | 74.0 | 81.0 | - | 69.3 | |
| CharXiv (RQ) | 77.5 | 82.1 | 67.2* | 81.4 | - | 66.1 | |
| MathVision | 84.2 | 83.0 | 77.1* | 86.1* | - | 74.6 | |
| MathVista (mini) | 90.1 | 82.8* | 80.2* | 89.8* | - | 85.8 | |
| ZeroBench | 9 | 9* | 3* | 8* | - | 4* | |
| ZeroBench (w/ tools) | 11 | 7* | 9* | 12* | - | 3* | |
| OCRBench | 92.3 | 80.7* | 86.5* | 90.3* | - | 87.5 | |
| OmniDocBench 1.5 | 88.8 | 85.7 | 87.7* | 88.5 | - | 82.0* | |
| InfoVQA (val) | 92.6 | 84* | 76.9* | 57.2* | - | 89.5 | |
| SimpleVQA | 71.2 | 55.8* | 69.7* | 69.7* | - | 56.8* | |
| WorldVQA | 46.3 | 28.0 | 36.8 | 47.4 | - | 23.5 | |
| VideoMMMU | 86.6 | 85.9 | 84.4* | 87.6 | - | 80.0 | |
| MMVU | 80.4 | 80.8* | 77.3 | 77.5 | - | 71.1 | |
| MotionBench | 70.4 | 64.8 | 60.3 | 70.3 | - | - | |
| VideoMME | 87.4 | 86.0* | - | 88.4* | - | 79.0 | |
| LongVideoBench | 79.8 | 76.5* | 67.2* | 77.7* | - | 65.6* | |
| LVBench | 75.9 | - | - | 73.5* | - | 63.6 | |
| Coding | |||||||
| SWE-Bench Verified | 76.8 | 80.0 | 80.9 | 76.2 | 73.1 | - | |
| SWE-Bench Pro | 50.7 | 55.6 | 55.4* | - | - | - | |
| SWE-Bench Multilingual | 73.0 | 72.0 | 77.5 | 65.0 | 70.2 | - | |
| Terminal Bench 2.0 | 50.8 | 54.0 | 59.3 | 54.2 | 46.4 | - | |
| PaperBench | 63.5 | 63.7* | 72.9* | - | 47.1 | - | |
| CyberGym | 41.3 | - | 50.6 | 39.9* | 17.3* | - | |
| SciCode | 48.7 | 52.1 | 49.5 | 56.1 | 38.9 | - | |
| OJBench (cpp) | 57.4 | - | 54.6* | 68.5* | 54.7* | - | |
| LiveCodeBench (v6) | 85.0 | - | 82.2* | 87.4* | 83.3 | - | |
| Long Context | |||||||
| Longbench v2 | 61.0 | 54.5* | 64.4* | 68.2* | 59.8* | - | |
| AA-LCR | 70.0 | 72.3* | 71.3* | 65.3* | 64.3* | - | |
| Agentic Search | |||||||
| BrowseComp | 60.6 | 65.8 | 37.0 | 37.8 | 51.4 | - | |
| BrowseComp (w/ctx manage) | 74.9 | 57.8 | 59.2 | 67.6 | - | ||
| BrowseComp (Agent Swarm) | 78.4 | - | - | - | - | - | |
| WideSearch (item-f1) | 72.7 | - | 76.2* | 57.0 | 32.5* | - | |
| WideSearch (item-f1 Agent Swarm) | 79.0 | - | - | - | - | - | |
| DeepSearchQA | 77.1 | 71.3* | 76.1* | 63.2* | 60.9* | - | |
| FinSearchCompT2&T3 | 67.8 | - | 66.2* | 49.9 | 59.1* | - | |
| Seal-0 | 57.4 | 45.0 | 47.7* | 45.5* | 49.5* | - | |
Model Specifications
Context Length262144
Quality Index0.76
LicenseOther
Last UpdatedFebruary 2026
Input TypeText,Image
Output TypeText
ProviderMoonshot AI
Languages1 Language
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