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Moonshot AI's Kimi K3.1 Pro Arrives on Amazon Bedrock

Moonshot AI has made its Kimi K3.1 Pro model available on Amazon Bedrock, offering multiple API integration options and prompt caching.

This article was AI-generated and published automatically. Context, labelling and all sources at the end of the article.

(KI-generiertes Symbolbild: Gemini / AI Connect)

Amazon Bedrock has added Moonshot AI's Kimi K3.1 Pro to its platform. Developers can access and integrate the model through several endpoints, including OpenAI-compatible Chat Completions, Responses, and Converse APIs. This range of entry points allows technical teams to interface with the model using standard API specifications.

The deployment on Amazon Bedrock also supports explicit prompt caching designed to optimize inference workflows. Furthermore, teams can implement the model as a dedicated coding assistant inside the OpenCode development environment.

What this means for you

The arrival of Kimi K3.1 Pro on Amazon Bedrock provides developers with an alternative model option supported by standard API formats. Explicit prompt caching allows organizations running repetitive prompts to manage operational latency and inference costs while evaluating the model for programming support.

Evidence

Solidly sourced
46/100

  • Moonshot AI's Kimi K3.1 Pro has been made available on Amazon Bedrock.

    single source
    Quote

    „Kimi K3.1 Pro from Moonshot AI is now available on Amazon Bedrock.“

    AWS, Machine Learning

  • The model can be accessed through OpenAI-compatible Chat Completions, Responses, and Converse APIs.

    single source
    Quote

    „OpenAI-compatible Chat Completions, Responses, and Converse APIs“

    AWS, Machine Learning

  • Inference can be optimized using explicit prompt caching.

    single source
    Quote

    „optimize inference with explicit prompt caching“

    AWS, Machine Learning

  • The model can serve as a coding assistant in OpenCode.

    single source
    Quote

    „use the model as a coding assistant in OpenCode“

    AWS, Machine Learning

Sources

The evidence score is computed, not hand-set: from confidence, the number of sources and the share of verified statements.

Source & transparency

As of: November 11, 2026

AI-generatedAI-generated: produced automatically from vetted sources with technical quality checks (source, quote and figure verification); no human sign-off of each item before publication

Sources
1
Verified statements
0 / 4
Evidence score
46Solidly sourced
Generative AIUse Cases

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