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Kimi-K3 on HuggingFace

Original article
huggingface.co|1042 points|424 comments|by nateb2022|Jul 27, 2026

Kimi-K3 on HuggingFace

Kimi-K3 Logo

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🚀 Model Overview

Kimi K3 represents the pinnacle of Moonshot AI's development to date. It is a native multimodal agentic model released with open weights, designed to push the boundaries of frontier intelligence.

At its core, Kimi K3 is a massive ≈2.8T\approx 2.8\text{T}≈2.8T parameter model. It leverages innovative architectural components like Kimi Delta Attention (KDA) and Attention Residuals (AttnRes). With a native ability to process visual data and a massive context window of 1 million tokens, it is the first open-weight model in the 3T-parameter class. It is specifically engineered for complex, long-term tasks involving reasoning, professional knowledge work, and advanced coding.

🌟 Primary Capabilities

  • Advanced Architecture: By utilizing a Stable LatentMoE framework, Kimi K3 optimizes sparsity by activating only 16 out of 896 experts. This results in a ≈2.5×\approx 2.5\times≈2.5× boost in scaling efficiency compared to its predecessor, Kimi K2.
  • Long-Horizon Engineering: The model can manage extended coding sessions with minimal human intervention. It is capable of navigating vast codebases and utilizing terminal tools for high-level tasks such as:
    • GPU kernel optimization
    • Compiler development
    • Chip design
    • CAD and vision-integrated game development
  • Agentic Knowledge Production: Kimi K3 transforms knowledge work by generating comprehensive research. Its multimodal nature allows it to create interactive dashboards, widgets, motion designs, and perform video editing.
  • Native Multimodality & Scale: It processes text, imagery, and video within a single unified model, supported by a 1,048,5761,048,5761,048,576 token context window.
  • Open Access: Closed-source frontier models →\rightarrow→ Open Frontier Weights. The model is available under the Kimi K3 License to foster global research and innovation.

🛠 Technical Specifications

The following table outlines the structural composition of Kimi K3:

FeatureSpecification
ArchitectureMixture-of-Experts (MoE)
Total Parameters2.8T2.8\text{T}2.8T
Activated Parameters104B104\text{B}104B
Total Layers93
Dense Layers1
Attention Mix69 KDA + 24 Gated MLA
Hidden Dimension7168
Attention Heads96
Latent MoE Dimension3584
Expert Hidden Dim3072
Total Experts896
Active Experts/Token16
Shared Experts2
Vocabulary Size160K
Context Window1,048,576 tokens
Attention MechanismKDA & Gated MLA
Activation FunctionSiTU-GLU
Vision EncoderMoonViT-V2 (401M parameters)
QuantizationMXFP4 weights / MXFP8 activations (QAT)
Supported ModalitiesText, Image

MoE Activation Logic


📊 Evaluation Results

Kimi K3 was tested against several industry-leading models. All Kimi K3 scores were achieved using reasoning effort = max and temperature = 1.0.

BenchmarkKimi 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
GPQA Diamond93.592.694.191.093.591.2
CritPt23.428.632.320.927.120.9
AA-LCR74.770.073.767.774.371.3
HLE-Full43.5 / 56.053.3 / 63.044.5 / 58.049.8 / 57.941.4 / 52.2—
Coding
DeepSWE67.570.073.059.067.046.2
ProgramBench77.876.877.671.970.863.7
Terminal-Bench2.188.388.088.884.683.4
FrontierSWE81.286.671.366.764.967.3
SWE-Marathon42.035.039.040.014.013.0
PostTrainBench36.641.434.634.128.434.3
MLS-Bench-Lite48.349.946.242.835.540.4
SciCode58.760.256.153.556.150.5
Kimi Code Bench 2.072.976.964.871.769.064.2
Agentic
BrowseComp91.288.090.484.384.4—
DeepSearchQA (F1)95.094.2—93.1——
ResearchRubrics76.2—73.873.564.071.1
GDPval-AA v2 (Elo)168617471736159314911510
Toolathlon-Verified76.577.974.976.273.559.9
MCPMark-Verified94.587.492.976.492.9—
MCP-Atlas84.284.783.683.682.882.6
AutomationBench30.829.129.727.222.712.9
JobBench54.357.445.448.438.343.4
AA-Briefcase (Elo)154815831495135411581260
Agents' Last Exam28.325.7 †29.627.026.620.4
APEX-Agents41.043.339.939.438.535.6
OfficeQA Pro63.369.963.263.960.941.4
SpreadsheetBench 234.834.732.431.629.128.1
OSWorld-Verified84.885.083.083.479.0—
OSWorld 2.058.366.162.655.749.5—
SaaS-Bench60.1—61.456.143.8—
τ3\tau^3τ3-Banking33.426.833.027.631.326.8
Harvey Lab-AA94.693.687.291.186.391.0
CorpFin v271.671.864.466.768.466.1
Finance Agent v254.456.353.853.951.849.7
Legal Research Bench44.249.548.143.840.431.3
Vision
WorldVQA ForceAnswer51.056.741.839.138.5—
OmniDocBench91.189.885.887.989.4—
PerceptionBench58.557.259.747.255.8—
Video-MME (w. sub)