Kimi-K3 on HuggingFace
Kimi-K3 on HuggingFace
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🚀 Model Overview
Kimi K3 represents the pinnacle of Moonshot AI's development to date. It is a native multimodal agentic model released with open weights, designed to push the boundaries of frontier intelligence.
At its core, Kimi K3 is a massive parameter model. It leverages innovative architectural components like Kimi Delta Attention (KDA) and Attention Residuals (AttnRes). With a native ability to process visual data and a massive context window of 1 million tokens, it is the first open-weight model in the 3T-parameter class. It is specifically engineered for complex, long-term tasks involving reasoning, professional knowledge work, and advanced coding.
🌟 Primary Capabilities
- Advanced Architecture: By utilizing a Stable LatentMoE framework, Kimi K3 optimizes sparsity by activating only 16 out of 896 experts. This results in a boost in scaling efficiency compared to its predecessor, Kimi K2.
- Long-Horizon Engineering: The model can manage extended coding sessions with minimal human intervention. It is capable of navigating vast codebases and utilizing terminal tools for high-level tasks such as:
GPU kernel optimizationCompiler developmentChip designCAD and vision-integrated game development
- Agentic Knowledge Production: Kimi K3 transforms knowledge work by generating comprehensive research. Its multimodal nature allows it to create interactive dashboards, widgets, motion designs, and perform video editing.
- Native Multimodality & Scale: It processes text, imagery, and video within a single unified model, supported by a token context window.
- Open Access:
Closed-source frontier modelsOpen Frontier Weights. The model is available under the Kimi K3 License to foster global research and innovation.
🛠 Technical Specifications
The following table outlines the structural composition of Kimi K3:
| Feature | Specification |
|---|---|
| Architecture | Mixture-of-Experts (MoE) |
| Total Parameters | |
| Activated Parameters | |
| Total Layers | 93 |
| Dense Layers | 1 |
| Attention Mix | 69 KDA + 24 Gated MLA |
| Hidden Dimension | 7168 |
| Attention Heads | 96 |
| Latent MoE Dimension | 3584 |
| Expert Hidden Dim | 3072 |
| Total Experts | 896 |
| Active Experts/Token | 16 |
| Shared Experts | 2 |
| Vocabulary Size | 160K |
| Context Window | 1,048,576 tokens |
| Attention Mechanism | KDA & Gated MLA |
| Activation Function | SiTU-GLU |
| Vision Encoder | MoonViT-V2 (401M parameters) |
| Quantization | MXFP4 weights / MXFP8 activations (QAT) |
| Supported Modalities | Text, Image |
MoE Activation Logic
📊 Evaluation Results
Kimi K3 was tested against several industry-leading models. All Kimi K3 scores were achieved using reasoning effort = max and temperature = 1.0.
| Benchmark | Kimi K3 (max) | Claude Fable 5 (max, w/ fallback) | GPT-5.6 Sol (max) | Claude Opus 4.8 (max) | GPT-5.5 (xhigh) | GLM-5.2 (max) |
|---|---|---|---|---|---|---|
| Reasoning | ||||||
| GPQA Diamond | 93.5 | 92.6 | 94.1 | 91.0 | 93.5 | 91.2 |
| CritPt | 23.4 | 28.6 | 32.3 | 20.9 | 27.1 | 20.9 |
| AA-LCR | 74.7 | 70.0 | 73.7 | 67.7 | 74.3 | 71.3 |
| HLE-Full | 43.5 / 56.0 | 53.3 / 63.0 | 44.5 / 58.0 | 49.8 / 57.9 | 41.4 / 52.2 | — |
| Coding | ||||||
| DeepSWE | 67.5 | 70.0 | 73.0 | 59.0 | 67.0 | 46.2 |
| ProgramBench | 77.8 | 76.8 | 77.6 | 71.9 | 70.8 | 63.7 |
| Terminal-Bench | 2.1 | 88.3 | 88.0 | 88.8 | 84.6 | 83.4 |
| FrontierSWE | 81.2 | 86.6 | 71.3 | 66.7 | 64.9 | 67.3 |
| SWE-Marathon | 42.0 | 35.0 | 39.0 | 40.0 | 14.0 | 13.0 |
| PostTrainBench | 36.6 | 41.4 | 34.6 | 34.1 | 28.4 | 34.3 |
| MLS-Bench-Lite | 48.3 | 49.9 | 46.2 | 42.8 | 35.5 | 40.4 |
| SciCode | 58.7 | 60.2 | 56.1 | 53.5 | 56.1 | 50.5 |
| Kimi Code Bench 2.0 | 72.9 | 76.9 | 64.8 | 71.7 | 69.0 | 64.2 |
| Agentic | ||||||
| BrowseComp | 91.2 | 88.0 | 90.4 | 84.3 | 84.4 | — |
| DeepSearchQA (F1) | 95.0 | 94.2 | — | 93.1 | — | — |
| ResearchRubrics | 76.2 | — | 73.8 | 73.5 | 64.0 | 71.1 |
| GDPval-AA v2 (Elo) | 1686 | 1747 | 1736 | 1593 | 1491 | 1510 |
| Toolathlon-Verified | 76.5 | 77.9 | 74.9 | 76.2 | 73.5 | 59.9 |
| MCPMark-Verified | 94.5 | 87.4 | 92.9 | 76.4 | 92.9 | — |
| MCP-Atlas | 84.2 | 84.7 | 83.6 | 83.6 | 82.8 | 82.6 |
| AutomationBench | 30.8 | 29.1 | 29.7 | 27.2 | 22.7 | 12.9 |
| JobBench | 54.3 | 57.4 | 45.4 | 48.4 | 38.3 | 43.4 |
| AA-Briefcase (Elo) | 1548 | 1583 | 1495 | 1354 | 1158 | 1260 |
| Agents' Last Exam | 28.3 | 25.7 † | 29.6 | 27.0 | 26.6 | 20.4 |
| APEX-Agents | 41.0 | 43.3 | 39.9 | 39.4 | 38.5 | 35.6 |
| OfficeQA Pro | 63.3 | 69.9 | 63.2 | 63.9 | 60.9 | 41.4 |
| SpreadsheetBench 2 | 34.8 | 34.7 | 32.4 | 31.6 | 29.1 | 28.1 |
| OSWorld-Verified | 84.8 | 85.0 | 83.0 | 83.4 | 79.0 | — |
| OSWorld 2.0 | 58.3 | 66.1 | 62.6 | 55.7 | 49.5 | — |
| SaaS-Bench | 60.1 | — | 61.4 | 56.1 | 43.8 | — |
| -Banking | 33.4 | 26.8 | 33.0 | 27.6 | 31.3 | 26.8 |
| Harvey Lab-AA | 94.6 | 93.6 | 87.2 | 91.1 | 86.3 | 91.0 |
| CorpFin v2 | 71.6 | 71.8 | 64.4 | 66.7 | 68.4 | 66.1 |
| Finance Agent v2 | 54.4 | 56.3 | 53.8 | 53.9 | 51.8 | 49.7 |
| Legal Research Bench | 44.2 | 49.5 | 48.1 | 43.8 | 40.4 | 31.3 |
| Vision | ||||||
| WorldVQA ForceAnswer | 51.0 | 56.7 | 41.8 | 39.1 | 38.5 | — |
| OmniDocBench | 91.1 | 89.8 | 85.8 | 87.9 | 89.4 | — |
| PerceptionBench | 58.5 | 57.2 | 59.7 | 47.2 | 55.8 | — |
| Video-MME (w. sub) |