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.
import { streamText } from 'ai'
const result = streamText({ model: 'moonshotai/kimi-k2-thinking', prompt: 'Why is the sky blue?'})What To Consider When Choosing a Provider
Zero Data Retention
AI Gateway supports Zero Data Retention for this model via direct gateway requests (BYOK is not included). To configure this, check the documentation.Authentication
AI Gateway authenticates requests using an API key or OIDC token. You do not need to manage provider credentials directly.
Reasoning traces increase output length, so budget planning should account for higher output token use relative to non-thinking K2 variants. Completions support up to 262.1K tokens per request.
When to Use Kimi K2 Thinking
Best For
Visible model reasoning:
Problems where seeing the model's work matters — debugging complex logic, validating mathematical derivations, auditing decisions
Algorithmic exploration:
Multi-step design where the model must explore and eliminate approaches before settling on a solution
Long tool-call chains:
Agentic sessions requiring sequential tool calls with coherence across the full chain
Evaluation and red-teaming:
Workflows where reasoning traces surface failure modes and edge cases
Consider Alternatives When
Straightforward tasks:
Standard Kimi K2 is faster and cheaper for direct-answer tasks that don't benefit from deliberation
Hard latency constraints:
Reasoning traces add significant generation time
Output cost sensitivity:
Thinking traces can multiply output length by 3 to 10x
Speed-optimized reasoning:
Kimi K2 Thinking Turbo trades some reasoning depth for lower latency
Conclusion
Kimi K2 Thinking restructures model output around explicit reasoning. For problems that reward deliberation, the visible chain-of-thought adds an auditable record of the model's logic. Long chains of sequential tool calls extend this into agentic workflows. Reserve it for tasks where the thinking trace earns its token cost. Use non-thinking variants for everything else.
FAQ
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.
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.
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.
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.
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.
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.
