本周热门 · 2026-07-27#1
MoonshotAI/Kimi-K3
Open Frontier Intelligence
4.7k35911 issuesOther
创建于 2026-07-27最近推送 2026-07-28
Open Frontier Intelligence
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.
| 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 |
| 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 |
| 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 | — |
| τ³-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) | 90.0 | — | 89.5 | 86.0 | 89.3 | — |
| MMVU | 82.1 | — | 81.2 | 79.2 | 81.7 | — |
| BabyVision w/ python | 85.7 | 90.5 | 88.9 | 81.2 | 83.6 | — |
| MMMU-Pro | 81.6 / 83.4 | 81.2 / 86.5 | 83.0 / 84.6 | 78.9 / 82.7 | 81.2 / 83.2 | — |
| CharXiv (RQ) | 84.8 / 91.3 | 88.9 / 93.5 | 84.6 / 89.1 | 80.5 / 89.9 | 84.1 / 89.0 | — |
| MathVision | 94.3 / 97.8 | 94.8 / 98.6 | 95.8 / 97.8 | 86.7 / 97.1 | 92.2 / 96.8 | — |
| ZeroBench (pass@5) | 23.0 / 41.0 | 23.0 / 46.0 | 17.0 / 35.0 | 17.0 / 34.0 | 22.0 / 41.0 | — |
All Kimi K3 results are obtained with reasoning effort set to 'max' and temperature = 1.0. For single-step tasks, such as GPQA Diamond, HLE-Full, and vision benchmarks without tools, we set top-p = 0.95; for agentic tasks, we set top-p = 1.0. For HLE-Full, MMMU-Pro, CharXiv (RQ), MathVision, and ZeroBench, each cell reports the scores without and with tool augmentation (general tools for HLE-Full, Python for the vision benchmarks), in that order.
Kimi K3 applies quantization-aware training from the SFT stage onward, using MXFP4 weights with MXFP8 activations for broad hardware compatibility.
[!Note] You can access Kimi K3's API on https://platform.kimi.ai by selecting
kimi-k3, and we provide OpenAI/Anthropic-compatible API for you. Currently, Kimi K3 is recommended to run on the following inference engines:
Kimi K3 always has thinking enabled, and will return reasoning_content. Thinking effort is configured with the top-level reasoning_effort request field, which supports "low", "high", and "max" (default "max").
Kimi K3 was trained in the preserved thinking history mode. For multi-turn conversations and tool calls, Kimi K3 requires the complete assistant message returned by the API to be passed back to messages as-is — including reasoning_content and tool_calls, not just content:
){kind=icon}
import openai
def chat_with_preserved_thinking(client: openai.OpenAI, model_name: str):
messages = [
{
"role": "user",
"content": "Tell me three random numbers."
},
{
"role": "assistant",
"reasoning_content": "I'll start by listing five numbers: 473, 921, 235, 215, 222, and I'll tell you the first three.",
"content": "473, 921, 235"
},
{
"role": "user",
"content": "What are the other two numbers you have in mind?"
}
]
response = client.chat.completions.create(
model=model_name,
messages=messages,
stream=False,
max_tokens=4096,
reasoning_effort="max",
)
# the assistant should mention 215 and 222 that appear in the prior reasoning content
print(f"response: {response.choices[0].message.reasoning}")
return response.choices[0].message.content
For full guides and examples (vision input, structured output, partial mode, tool choice, dynamic tool loading, context caching), see the Kimi K3 Quickstart and Thinking Effort.
Kimi K3 works best with Kimi Code CLI as its agent framework. We warmly invite you to give it a try — run Kimi Code in your terminal and select Kimi K3 using the /model command. We hope you enjoy building with Kimi K3, and we would love to hear your feedback!
Both the code repository and the model weights are released under the Kimi K3 License.
If you have any questions, please reach out at [email protected].