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Home/Open Source
Open Source

Kimi K3.1 Pro Open Weights Released by Moonshot AI

Matt Wolfe·November 6, 2026·high confidence

Why it matters

  • →Expands the pool of high-performance open-weight LLMs beyond US labs.
  • →Provides researchers with access to advanced Chinese AI capabilities.
  • →Increases competition in the open-source model market.
Kimi K3.1 Pro Open Weights Released by Moonshot AI
©Matt Wolfe

Chinese AI startup Moonshot AI has released the open weights for its Kimi K3.1 Pro model. This release makes one of China's leading large language models accessible to the global developer community for research and deployment. The model is noted for its strong performance in reasoning and long-context processing, challenging the dominance of US-based models in the open-weight space. This move increases the diversity of high-quality open-source LLMs available to developers.

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The story around this

TopicMoonshot AI Releases Kimi K3.1 ProRising

Earlier coverage that leads up to this article, and what followed. Lines connect each piece to the closest one after it, converging here.

Moonshot's Kimi 3 to Rival Anthropic's Opus 4.8 — TechCrunch AI1Moonshot AI's Kimi K3 Challenges Frontier Models — The Rundown AI2Moonshot AI's Kimi K3 Model Bets on Memory — AI News3Moonshot's Kimi K3 Model Approaches Fable 5 Benchmarks — The AI Daily Brief4Moonshot releases Kimi K3 with 2.8 trillion parameters — Fireship5Moonshot releases largest open AI model K3 — The Rundown AI6Kimi K3: Largest Open-Weight AI Model Released — Together AI Blog7Moonshot AI releases Kimi K3.1 Pro — Matthew Berman8Kimi K3.1 Pro Open Weights Released by Moonshot AIllama.cpp adds Kimi K3.1-Pro support — llama.cpp Releases9Jul 16You are hereNov 7

How we got here

  1. 1
    Moonshot's Kimi 3 to Rival Anthropic's Opus 4.8

    TechCrunch AI · July 16, 2026 · Same story

  2. 2
    Moonshot AI's Kimi K3 Challenges Frontier Models

    The Rundown AI · July 17, 2026 · Same story

  3. 3
    Moonshot AI's Kimi K3 Model Bets on Memory

    AI News · July 20, 2026 · Same story

  4. 4
    Moonshot's Kimi K3 Model Approaches Fable 5 Benchmarks

    The AI Daily Brief · July 21, 2026 · Same story

  5. 5
    Moonshot releases Kimi K3 with 2.8 trillion parameters

    Fireship · July 22, 2026 · Same story

  6. 6
    Moonshot releases largest open AI model K3

    The Rundown AI · July 28, 2026 · Same story

  7. 7
    Kimi K3: Largest Open-Weight AI Model Released

    Together AI Blog · August 1, 2026 · Same story

  8. 8
    Moonshot AI releases Kimi K3.1 Pro

    Matthew Berman · October 24, 2026 · Same story

What happened next

  1. 9
    llama.cpp adds Kimi K3.1-Pro support

    llama.cpp Releases · November 7, 2026 · Related

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Open the full story →

Kimi K3.1 weights open, NorthCUA agent, Pika H3 video

3 developments

  1. Nov 5 · Matthew Berman
    Kimi K3.1 weights open, NorthCUA agent, Pika H3 video
  2. Nov 6 · Matt Wolfe
    Pika Releases H3 Flex Open-Weight Video Model↳ Pika H3 Flex is now open-weight for self-hosting.
  3. Nov 6 · Matt Wolfe
    Kimi K3.1 Pro Open Weights Released by Moonshot AI (This article)↳ Moonshot AI released open weights for Kimi K3.1 Pro, a high-performance language model competing with top US labs.

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Kimi K3.1 weights open, NorthCUA agent, Pika H3 video

Moonshot AI releasing Kimi K3.1 Pro weights is a major signal for the open-source ecosystem, giving builders access to a top-tier reasoning model without vendor lock-in. Simultaneously, NorthCUA introduces an open computer-use agent that can navigate interfaces autonomously, while Pika Labs pushes video generation forward with its H3 Flex model. This trio of releases shifts power from closed labs to independent developers who can now fine-tune or deploy these capabilities locally or via API.

Matthew Berman·Nov 5, 2026
Ricursive AI Releases Ripple 65B for Chip Design© Sam Witteveen
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Ricursive AI Releases Ripple 65B for Chip Design

Ricursive AI has released Ripple 65B, an open-source model specifically trained to accelerate semiconductor design workflows. By focusing on a niche vertical rather than general-purpose reasoning, the company achieved competitive performance against major US open models while training for just $32 million. The architecture is described as thin and deep, optimized for the specific token distributions found in hardware description languages. This release signals that specialized, domain-locked models can outperform broad foundation models when the data distribution is sufficiently distinct and high-value.

Sam Witteveen·Nov 5, 2026