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kimi-k3

Kimi K3 is a 2.8T parameter open-weight model from Moonshot AI designed specifically for high-reasoning workloads. Unlike standard LLMs that prioritize quick chat responses, K3 is architected for long-horizon agentic workflows and complex logic chains. For developers, the standout feature is its ability to maintain coherence across massive contexts, making it a viable backbone for autonomous agents and sophisticated coding assistants. It bridges the gap between massive proprietary models and open-weight accessibility, offering a specialized focus on deep reasoning and multi-step problem solving. Whether you are building RAG pipelines that require dense information retrieval or complex software engineering agents, K3 provides the parameter scale necessary to handle non-trivial instruction following and nuanced knowledge work without the overhead of much larger closed-source alternatives.

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01 / MODEL CARD

Model card

Kimi K3 is a 2.8T parameter open-weight model from Moonshot AI designed specifically for high-reasoning workloads. Unlike standard LLMs that prioritize quick chat responses, K3 is architected for long-horizon agentic workflows and complex logic chains. For developers, the standout feature is its ability to maintain coherence across massive contexts, making it a viable backbone for autonomous agents and sophisticated coding assistants. It bridges the gap between massive proprietary models and open-weight accessibility, offering a specialized focus on deep reasoning and multi-step problem solving. Whether you are building RAG pipelines that require dense information retrieval or complex software engineering agents, K3 provides the parameter scale necessary to handle non-trivial instruction following and nuanced knowledge work without the overhead of much larger closed-source alternatives.

Model typetext generation
Providermoonshotai
LicenseAPI
02 / FILES & VERSIONS

Model files and versions

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Source repositoryhttps://openrouter.ai/moonshotai/kimi-k3
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03 / DOWNLOAD

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04 / WORKFLOW

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  1. 01
    Step 1

    Read the model card and source information.

  2. 02
    Step 2

    Start with a small, non-sensitive evaluation.

  3. 03
    Step 3

    Review quality, licensing and usage limits.

  4. 04
    Step 4

    Adopt it only after validation.

05 / DISCUSSIONS

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