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## 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
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	—

Footnotes

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. 1. **Reasoning & knowledge benchmarks** - **CritPt and AA-LCR.** Scores are cited from [Artificial Analysis](https://artificialanalysis.ai/) as of July 23, 2026. 2. **Coding benchmarks** - **DeepSWE.** Kimi K3 is evaluated with the Kimi Code harness. The GLM-5.2 score is taken from the [GLM-5.2 release blog](https://z.ai/blog/glm-5.2); all remaining scores are from the official [DeepSWE leaderboard](https://deepswe.datacurve.ai/), under which Kimi K3 attains 67.3 with the mini-SWE-agent harness. We report the DeepSWE v1.1 tasks. - **Terminal-Bench 2.1.** Kimi K3 is evaluated with the Kimi Code harness. For all other models, we report the best score across harnesses: GLM-5.2 with Claude Code ([GLM-5.2 release blog](https://z.ai/blog/glm-5.2)); Claude Opus 4.8 and Claude Fable 5 with Terminus 2 ([Artificial Analysis](https://artificialanalysis.ai/evaluations/terminalbench-v2-1)); GPT-5.5 and GPT-5.6 Sol with Codex ([OpenAI](https://openai.com/index/previewing-gpt-5-6-sol/)). - **ProgramBench.** Kimi K3 is evaluated with the Kimi Code harness. The GLM-5.2 score is from the [GLM-5.2 release blog](https://z.ai/blog/glm-5.2); all other scores are from [Vals AI](https://www.vals.ai/benchmarks/programbench). - **SWE-Marathon.** Kimi K3, Claude Opus 4.8, and Claude Fable 5 are evaluated with the Claude Code harness; GPT-5.6 Sol is evaluated with the Codex harness. The GLM-5.2 score is from the [GLM-5.2 release blog](https://z.ai/blog/glm-5.2). Our evaluation is based on an H20-calibrated branch of the [official tasks](https://www.swe-marathon.org/) as of July 9, 2026, prior to the final v1.1 release: the Docker images, performance gates, and reference oracles for the GPU tasks have been recalibrated for H20, while the correctness and anti-cheat validators remain unchanged. Additionally, Claude Fable 5 hit fallbacks on 35% of the tasks in our evaluation, which may have negatively impacted its measured performance. - **FrontierSWE.** Kimi K3 is evaluated with the Kimi Code harness and GPT-5.6 Sol with the Codex harness; all other results are from [FrontierSWE](https://www.frontierswe.com/). Dominance scores are recomputed from the raw scores using the official evaluation script and are current as of July 16, 2026. - **PostTrainBench.** Scores for GLM-5.2, GPT-5.5, and Claude Opus 4.8 are adopted from the official [PostTrainBench](https://posttrainbench.com/) results. Kimi K3, Claude Fable 5, and GPT-5.6 Sol are evaluated with the official Harbor implementation at maximum reasoning effort, averaged over three runs on H20 GPUs (instead of H100 in the official setting) — Kimi K3 and Claude Fable 5 with the Claude Code harness, and GPT-5.6 Sol with the Codex harness. - **MLS-Bench-Lite.** Kimi K3 is evaluated with the Kimi Code harness; GLM-5.2 and the Claude models with the Claude Code harness; GPT-5.5 and GPT-5.6 Sol with the Codex harness. - **SciCode.** Scores are cited from [Artificial Analysis](https://artificialanalysis.ai/) as of July 23, 2026. - **Kimi Code Bench 2.0 (in-house).** Kimi K3 is evaluated with the Kimi Code harness (it attains 73.7 with the Claude Code harness); GLM-5.2, Claude Opus 4.8, and Claude Fable 5 with the Claude Code harness; GPT-5.5 and GPT-5.6 Sol with the Codex harness. All models are evaluated at maximum reasoning effort, except GPT-5.5, which uses the "xhigh" setting. As the benchmark includes cybersecurity and safety-related tasks, we also disclose the fraction of refused or fallback tasks: Claude Fable 5 hit 13 fallbacks and 1 refusal out of 80 tasks; 10 refusals out of 80 tasks entered GPT-5.6 Sol's cyber guard; GPT-5.5 had 3 refusals out of 80 tasks. 3. **Agentic benchmarks** - **OfficeQA Pro.** Each test case provides the agent with the entire PDF corpus, with all PDFs rendered as images and no machine-readable text available. - **OfficeQA Pro and SpreadsheetBench 2.** Kimi K3, GLM-5.2, Claude Opus 4.8, and Claude Fable 5 are evaluated with the Claude Code harness; GPT-5.5 and GPT-5.6 Sol are evaluated with the Codex harness. - **MCP-Atlas.** All models are evaluated on the 500-task public subset with a 100-turn limit, using Gemini 3.1 Pro as the judge. - **AutomationBench.** All models are evaluated on the 600-task public subset, following the official GitHub setup in all other respects. - **BrowseComp.** We adopt a context-compaction strategy triggered at 300K tokens. When evaluated with the full 1M-token context window and no context management, Kimi K3 achieves a score of 90.4. The results of Claude Fable 5, Claude Opus 4.8, GPT-5.6 Sol, and GPT-5.5 are cited from [Anthropic](https://www.anthropic.com/news/claude-fable-5-mythos-5) and [OpenAI](https://openai.com/index/gpt-5-6/). - **GDPval-AA v2, AA-Briefcase, τ³-Banking, Harvey Lab-AA, and APEX-Agents.** Scores are cited from [Artificial Analysis](https://artificialanalysis.ai/) and the [APEX-Agents leaderboard](https://www.mercor.com/apex/apex-agents-leaderboard/) as of July 23, 2026. For Harvey Lab-AA, we report the criterion pass rate. - **CorpFin v2, Finance Agent v2, and Legal Research Bench.** Scores are cited from [Vals AI](https://www.vals.ai/). - **Agents' Last Exam.** Scores are cited from the [official leaderboard](https://agents-last-exam.org/leaderboard) as of July 23, 2026; we report the leaderboard's primary pass-rate metric. On the leaderboard, each model is paired with a specific harness: Kimi K3 with Kimi Code; GPT-5.6 Sol and GPT-5.5 with Codex; Claude Fable 5, Claude Opus 4.8, and GLM-5.2 with Claude Code. ^† The Claude Fable 5 entry runs at xhigh effort with 40% of tasks annotated as downgraded. 4. **Multimodal benchmarks** - Except for ZeroBench, which follows the official setting and is run five times, all multimodal scores are averaged over three runs. MMMU-Pro is evaluated following the official protocol, preserving the original input order and prepending images to the text input. - **PerceptionBench** is an in-house benchmark that focuses on atomic visual perception capabilities.

## 4. Native MXFP4 Quantization Kimi K3 applies quantization-aware training from the SFT stage onward, using MXFP4 weights with MXFP8 activations for broad hardware compatibility. ## 5. Deployment > [!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: - [vLLM](https://github.com/vllm-project/vllm) — see [recipes](https://recipes.vllm.ai/moonshotai/Kimi-K3) - [SGLang](https://github.com/sgl-project/sglang) — see [cookbook](https://docs.sglang.io/cookbook/autoregressive/Moonshotai/Kimi-K3) - [TokenSpeed](https://lightseek.org/tokenspeed) — see [recipes](https://lightseek.org/tokenspeed/recipes/models#kimi-k3) --- ## 6. Model Usage 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`: ```python 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](https://platform.kimi.ai/docs/guide/kimi-k3-quickstart) and [Thinking Effort](https://platform.kimi.ai/docs/guide/use-thinking-effort). ### Coding Agent Framework Kimi K3 works best with [Kimi Code CLI](https://www.kimi.com/code) 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! --- ## 7. License Both the code repository and the model weights are released under the [Kimi K3 License](LICENSE). --- ## 8. Contact Us If you have any questions, please reach out at [support@moonshot.ai](mailto:support@moonshot.ai).