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Kimi K2 Thinking

Kimi K2 Thinking adds extended chain-of-thought (CoT) reasoning to the K2 architecture, supporting many sequential tool calls for agentic workflows through AI Gateway.

ReasoningTool UseImplicit Caching
index.ts
import { streamText } from 'ai'


const result = streamText({
  model: 'moonshotai/kimi-k2-thinking',
  prompt: 'Why is the sky blue?'
})
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Frequently Asked Questions

  • How does the reasoning trace change what I get back from the API?

    You get two parts: a thinking section with the chain-of-thought trace, and a final answer section with the conclusion. The trace shows problem decomposition, intermediate steps, considered alternatives, and the logical path to the answer. Both sections count toward output token usage.

  • What kinds of problems benefit most from the thinking mode?

    Multi-step proofs, debugging where the root cause isn't immediately apparent, algorithmic optimization with competing approaches, and problems where the model needs to try and discard wrong paths. Simple factual questions and routine code generation often don't justify the added cost and latency.

  • How long are the reasoning traces in practice?

    Length varies with problem difficulty. A moderately complex coding problem might produce 500 to 1,000 tokens of reasoning. A hard mathematical proof or multi-step debugging session can generate 3,000 to 5,000+ tokens. The model scales its deliberation to the perceived difficulty of the task.

  • Can I use reasoning traces for model evaluation and quality assurance?

    Yes. Traces show where the model reasons correctly, where it makes assumptions, and where it backtracks. You can check whether the model reached a correct answer through step-by-step reasoning or pattern matching, which helps on domain-specific tasks.

  • What makes long tool-call chains important for reasoning workflows?

    Each tool call is a reasoning decision: the model decides what to call, interprets the result, and picks the next step. Long chains let the model keep coherent task reasoning across more steps than many models support, so you can run automation pipelines that would otherwise need multiple sessions.

  • Does K2 Thinking always produce a reasoning trace, or can I turn it off?

    It always produces a reasoning trace. For direct answers without traces, use standard Kimi K2 or Kimi K2-0905. They share the same K2 architecture without the deliberative reasoning layer.