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Machine Learning

Kimi K3: Open Frontier Intelligence

Kimi Team, Tongtong Bai, Yifan Bai

Featured July 29, 2026

Original Paper

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Simply

This paper unveils Kimi K3, a massive AI that understands text and images, can remember a million words, and uses specialized mini-brains to learn 2.5 times more efficiently than before.

In depth
The paper introduces Kimi K3, a 2.8T parameter Mixture-of-Experts (MoE) model with a 1-million-token context window and native vision capabilities. Its core innovations include Kimi Delta Attention (KDA) for efficient long-sequence mixing, Attention Residuals (AttnRes) for selective information flow across network depth, and Stable LatentMoE with Quantile Balancing (QB) for robustly scaling sparse expert activation. These architectural and training advances yield an approximately 2.5x improvement in overall scaling efficiency over its predecessor.

Key Takeaways

  • 1
    Kimi K3 achieves frontier-level performance across diverse tasks (coding, agentic, knowledge, reasoning, vision) with a 2.8T parameter MoE model and a 1M token context window.
  • 2
    Architectural innovations like Kimi Delta Attention, Attention Residuals, and Stable LatentMoE significantly improve information flow across sequence length, network depth, and model width, leading to enhanced scaling efficiency.
  • 3
    The model incorporates native multimodal training from scratch and advanced infrastructure for multi-trillion-parameter MoE pre-training and million-token agentic reinforcement learning, enabling robust long-horizon execution.

Conceptual Flow

HIGH LEVEL
1
Methodology: How was it done?

The AI uses special attention and expert mini-brains to handle super long texts and pictures efficiently.

Long Text
Images
Process with Special Attention & Experts
Smart AI Output
2
Results: What did they find?

This new AI performs almost as well as the best private AIs, but much better than other open ones, especially for coding and complex tasks.

Other AIs
Kimi K3
Compare Performance
Kimi K3 Leads Open Models