# Kimi K3 Tech Report > Source: > Published: 2026-07-27 15:10:37+00:00 πŸ“° [Tech Blog](https://www.kimi.com/blog/kimi-k3) | **πŸ“„ Full Report** 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. **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 β€” yielding an approximate 2.5Γ— 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 β€” 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. 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 | β€” | 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 | β€” | | DeepSearchQA (F1) | 95.0 | 94.2 | β€” | 93.1 | β€” | β€” | | ResearchRubrics | 76.2 | β€” | 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 | β€” | | 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 | β€” | | OSWorld 2.0 | 58.3 | 66.1 | 62.6 | 55.7 | 49.5 | β€” | | SaaS-Bench | 60.1 | β€” | 61.4 | 56.1 | 43.8 | β€” | | 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 | β€” | | OmniDocBench | 91.1 | 89.8 | 85.8 | 87.9 | 89.4 | β€” | | PerceptionBench | 58.5 | 57.2 | 59.7 | 47.2 | 55.8 | β€” | | Video-MME (w. sub) | 90.0 | β€” | 89.5 | 86.0 | 89.3 | β€” | | MMVU | 82.1 | β€” | 81.2 | 79.2 | 81.7 | β€” | | BabyVision w/ python | 85.7 | 90.5 | 88.9 | 81.2 | 83.6 | β€” | | MMMU-Pro | 81.6 / 83.4 | 81.2 / 86.5 | 83.0 / 84.6 | 78.9 / 82.7 | 81.2 / 83.2 | β€” | | CharXiv (RQ) | 84.8 / 91.3 | 88.9 / 93.5 | 84.6 / 89.1 | 80.5 / 89.9 | 84.1 / 89.0 | β€” | | MathVision | 94.3 / 97.8 | 94.8 / 98.6 | 95.8 / 97.8 | 86.7 / 97.1 | 92.2 / 96.8 | β€” | | ZeroBench (pass@5) | 23.0 / 41.0 | 23.0 / 46.0 | 17.0 / 35.0 | 17.0 / 34.0 | 22.0 / 41.0 | β€” | **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. **Reasoning & knowledge benchmarks****CritPt and AA-LCR.** Scores are cited from[Artificial Analysis](https://artificialanalysis.ai/)as of July 23, 2026. **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) β€” 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. **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. **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. 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](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 β€” 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). 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 β€” 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](/MoonshotAI/Kimi-K3/blob/main/LICENSE). If you have any questions, please reach out at [support@moonshot.ai](mailto:support@moonshot.ai).