ai/kimi-k3 repository overview
Read our How to Run Kimi K3 Guide!
See Unsloth Dynamic 2.0 GGUFs for our quantization benchmarks.
- To run Kimi K3, use our llama.cpp PR fork or run in Unsloth Studio
- Full precision lossless = Q8 (UD-Q8_K_XL), which is 50GB bigger than Q4 (UD-Q4_K_XL).
- Has vision support!
- Kimi K3 has toggles for High and Max thinking in Unsloth Studio
- Read our Kimi K3 guide for analysis and instructions.
Kimi K3
1. Model Introduction
Kimi K3 is an open-weight, native multimodal agentic model and our most capable model to date. It is a 2.8T-parameter model built on Kimi Delta Attention (KDA) and Attention Residuals (AttnRes), with native vision capabilities and a 1-million-token context window. It is the world's first open 3T-class model, designed for frontier intelligence across long-horizon coding, knowledge work, and reasoning.
Key Features
- New Architecture: Kimi K3 is built on Kimi Delta Attention (KDA) and Attention Residuals (AttnRes), and scales up MoE sparsity with a Stable LatentMoE framework that activates 16 out of 896 experts — yielding an approximate 2.5× improvement in overall scaling efficiency over Kimi K2.
- Long-Horizon Coding: Operating with minimal human oversight, Kimi K3 sustains long engineering sessions, navigates massive repositories, and orchestrates terminal tools — from GPU kernel optimization and compiler development to vision-in-the-loop game dev, CAD, and even chip design.
- Agentic Knowledge Work: Kimi K3 advances end-to-end knowledge work, producing deep research with interactive visualizations, widgets and dashboards, and motion design and video editing, powered by its native multimodal architecture.
- Native Multimodality & Long Context: Kimi K3 understands text, images, and video within the same model, and supports a 1-million-token context window.
- Open Frontier Weights: We release the full Kimi K3 model weights under the Kimi K3 License, making frontier intelligence openly available for research, deployment, and further innovation.
2. Model Summary
| Architecture | Mixture-of-Experts (MoE) |
| Total Parameters | 2.8T |
| Activated Parameters | 104B |
| Number of Layers | 93 |
| Number of Dense Layers | 1 |
| Attention-Layer Composition | 69 KDA + 24 Gated MLA |
| Attention Hidden Dimension | 7168 |
| Number of Attention Heads | 96 |
| Latent MoE Dimension | 3584 |
| MoE Hidden Dimension (per Expert) | 3072 |
| Number of Experts | 896 |
| Selected Experts per Token | 16 |
| Number of Shared Experts | 2 |
| Vocabulary Size | 160K |
| Context Length | 1048576 |
| Attention Mechanism | KDA & Gated MLA |
| Activation Function | SiTU-GLU |
| Vision Encoder | MoonViT-V2 |
| Parameters of Vision Encoder | 401M |
| Quantization | MXFP4 weights / MXFP8 activations (quantization-aware training) |
| Modality | Text, Image |
3. Evaluation Results
| 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 & Knowledge | ||||||
| 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 | 82.7 |
| 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 |
| SpreadsheetB
…(truncated — see the full README on HuggingFace) |
Tag summary
Content type
Unrecognized
Digest
sha256:11dfdacbf…
Size
1.4 TB
Last updated
7 days ago
docker pull ai/kimi-k3This week's pulls
Pulls:
9,242
Last week