Kimi K2 Thinking Turbo
Kimi K2 Thinking Turbo is Moonshot AI's user-facing reasoning model. It delivers chain-of-thought thinking at turbo-speed latency for interactive products where deliberation quality and response time both shape the experience.
import { streamText } from 'ai'
const result = streamText({ model: 'moonshotai/kimi-k2-thinking-turbo', 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.
For interactive products using reasoning features, validate that turbo-mode thinking depth meets your quality bar. The latency reduction trades deliberation budget against standard thinking mode.
When to Use Kimi K2 Thinking Turbo
Best For
Interactive reasoning products:
Tutoring platforms, research assistants, and reasoning-enabled copilots where users wait for a response and slow thinking times cause session abandonment
Moderate-complexity problems at speed:
Tasks that benefit from chain-of-thought but don't require exhaustive deliberation (code explanation, step-by-step problem solving, logic puzzle resolution) fit the compressed thinking budget well
Multi-step tool-use with reasoning:
Pipelines where reasoning steps interleave with tool calls benefit from turbo latency across every step
A/B testing thinking vs. non-thinking quality:
Compare Kimi K2 Thinking Turbo against Kimi K2 Turbo (no thinking) to measure thinking-enabled vs. direct-answer quality at similar latency profiles
Consider Alternatives When
Maximum reasoning depth is required:
Kimi K2 Thinking with the full thinking budget fits the most complex mathematical proofs, intricate multi-step planning, or tasks where compressed deliberation falls short
Reasoning isn't needed:
Kimi K2 Turbo without thinking is more efficient when chain-of-thought adds latency without improving output quality
Real-time streaming responsiveness matters most:
When first-token latency is the priority and you can skip thinking entirely, Kimi K2 Turbo has the shortest response time in the K2 family for non-thinking generation
Conclusion
Kimi K2 Thinking Turbo sits between the K2 family's full reasoning depth and its fastest throughput configuration. For interactive products where reasoning quality matters and slow thinking modes are impractical, it's a practical default.
FAQ
Thinking Turbo runs the same reasoning mechanism under a tighter budget. Standard Kimi K2 Thinking allocates a larger deliberation budget per request and produces longer traces. For hard tasks, use full Thinking; for many interactive cases, Thinking Turbo is enough.
Yes. Tool calls and reasoning steps can interleave. The model can reason about a result, call a tool, reason about the response, and continue. This pipeline runs at turbo latency rather than full thinking-mode latency.
Use Thinking Turbo when the task benefits from deliberation and you have user-facing latency constraints. Use standard K2 Turbo when reasoning adds overhead without improving output quality for the specific task.
262.1K tokens, consistent with the K2 family.
It fits moderate-complexity problems. For frontier reasoning difficulty, Kimi K2 Thinking with the full deliberation budget is the better fit.
Use the identifier moonshotai/kimi-k2-thinking-turbo with any supported interface. AI Gateway handles provider routing automatically.
