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Kimi K3: the complete developer guide

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Everything you need to run Moonshot AI's 2.8T open-weights model on the Together AI API: benchmarks, pricing, and copy-paste code.

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  • What is Kimi K3, and what makes it different?
  • What is under the hood: KDA, Attention Residuals, and the Stable LatentMoE architecture
  • How do you use reasoning effort, streaming, tools, vision, and 1M context?
  • How do you take it from a first API call to production?
  • How does Kimi K3 compare to the frontier on coding and agentic benchmarks?
  • How much does Kimi K3 cost on Together AI?

    • Kimi K3 is Moonshot AI's most capable model to date: a 2.8-trillion-parameter model and the world's first open-source model in the 3-trillion-parameter class. It is designed for frontier intelligence work like long-horizon coding, end-to-end knowledge work, and deep reasoning. It is also the first open-weights model competing at the GPT 5.6 Sol and Claude Fable 5 tier, and Together AI is working directly with the Moonshot team to serve it.

    The Kimi team is deeply committed to scaling, and it shows: in nine of the twelve months from July 2025 to July 2026, Kimi models set the upper bound of open-model scale. At 2.8 trillion parameters, K3 is now the largest open-weight model ever released.

    Two architectural updates form K3's backbone, both designed to help information flow more easily through longer sequences and deeper into the network:

  • Kimi Delta Attention (KDA): a hybrid linear attention mechanism that provides an efficient foundation for scaling attention across very long contexts. This is the first Kimi model to support a 1M context length.
  • Attention Residuals (AttnRes): selectively retrieves representations across model depth rather than accumulating them uniformly.

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