[!IMPORTANT] This is an experimental, text-only MTPLX expert-streaming checkpoint, not Moonshot AI's original Kimi K3 weights and not a standard Transformers or llama.cpp checkpoint. The 92 routed MoE layers were requantized from GrEarl/Kimi-K3-GGUF Q2_K to MTPLX t158 (1.875 physical bits/weight). No downstream quality evaluation has been completed.

Artifact contract

Source GGUF: GrEarl/Kimi-K3-GGUF@0169245
Official metadata: moonshotai/Kimi-K3@9f62e4e
Routed bank: 92 layer parts, 896 experts/layer, top 16, 638,142,382,080 bytes total
Expert codec: t158, with separate packed/scales tensors for K3 SITU gate, up, and down projections
Resident tensors: stored in their source BF16/F32 representation
Text runtime: compatible resident Linear/Embedding modules are dynamically quantized to q8/gs64 at construction; routers, directly-read residual/MLA projections, norms, convolutions, and recurrent vectors retain source precision
Runtime mode: autoregressive, text-only, no MTP
Manifest SHA-256: 5dc510958199ad6b9d5701e47dd96bff14c41fbec1531a7a93ed16dc0176bcc1

The artifact was fully verified locally: 188 sidecar/resident shards and 82,432 expert records. The retained serialization pilot measured cosine 0.903718 against decoded Q2_K for its selected expert; that is a narrow serialization diagnostic, not a model-quality score.

Initial runtime smoke

Measured on the local MTPLX K3 AR runtime at a 131,072-token configured window, with a 96 GiB process plan and an 80 GiB MLX cap:

Prompt	Output	Prefill	Decode	Request time
28 tokens	8 tokens	1.905 tok/s	1.699 tok/s	19.42 s
38 tokens	64 tokens	1.778 tok/s	0.651 tok/s	119.68 s
38 tokens	64 tokens	1.595 tok/s	0.666 tok/s	119.88 s

Peak vmmap physical footprint was 84.8 GiB. Across the three requests the runtime performed 236,145 positional reads with zero short reads, integrity errors, deadline errors, or I/O errors. These are bring-up measurements, not headline performance claims.

The ordinary fully resident t158 serialization is published separately as OpensourceWTF/Kimi-K3-Q2_K-t158-MTPLX; this repository is the SSD-streaming layout.

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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.

Reasoning & knowledge benchmarks
- CritPt and AA-LCR. Scores are cited from Artificial Analysis 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; all remaining scores are from the official DeepSWE leaderboard, 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); Claude Opus 4.8 and Claude Fable 5 with Terminus 2 (Artificial Analysis); GPT-5.5 and GPT-5.6 Sol with Codex (OpenAI).
- ProgramBench. Kimi K3 is evaluated with the Kimi Code harness. The GLM-5.2 score is from the GLM-5.2 release blog; all other scores are from Vals AI.
- 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. Our evaluation is based on an H20-calibrated branch of the official tasks 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. 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 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 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 and OpenAI.
- GDPval-AA v2, AA-Briefcase, τ³-Banking, Harvey Lab-AA, and APEX-Agents. Scores are cited from Artificial Analysis and the 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.
- Agents' Last Exam. Scores are cited from the official 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.

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 — see recipes
SGLang — see cookbook
TokenSpeed — see recipes

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 and Thinking Effort.

Coding Agent Framework

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!

7. License

Both the code repository and the model weights are released under the Kimi K3 License.

8. Contact Us

If you have any questions, please reach out at support@moonshot.ai.

OpensourceWTF/Kimi-K3-Q2_K-t158-MTPLX-streaming

Kimi K3 Q2_K-to-t158 MTPLX streaming