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?'})Playground
Try out Kimi K2 Thinking Turbo by Moonshot AI. Usage is billed to your team at API rates. Free users (those who haven't made a payment) get $5 of credits every 30 days.
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About Kimi K2 Thinking Turbo
Kimi K2 Thinking Turbo applies Kimi K2 Thinking's chain-of-thought reasoning at turbo-speed latency. The distinction from Kimi K2 Turbo (no thinking) and Kimi K2 Thinking (full thinking budget) is the combination: deliberation happens, but under time pressure. The model allocates a compressed reasoning budget per request and produces output faster than standard thinking mode.
This tradeoff serves interactive applications: coding assistants, tutoring interfaces, research tools, and reasoning-enabled chatbots. Users want the model to think through problems but will abandon the session if responses take too long. The turbo thinking configuration avoids the binary choice between "fast but non-reasoning" and "reasoning but slow." For problems that benefit from deliberation but don't require the full thinking budget, Kimi K2 Thinking Turbo sits in the middle.
The model keeps K2 Thinking's tool-use and multi-step reasoning pipeline support. Applications that chain reasoning steps together (verifying a conclusion, planning then executing, or synthesizing information from multiple tool calls) run at turbo latency without switching to a non-thinking endpoint.
Kimi K2 Thinking Turbo supports a context window of 262.1K tokens and is available through AI Gateway at $1.15 per million input tokens and $8 per million output tokens.
What To Consider When Choosing a Provider
- Configuration: 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.
- 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.
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.
Frequently Asked Questions
How does compressed thinking in Turbo differ from the full Thinking model?
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.
Does Kimi K2 Thinking Turbo support tool use alongside reasoning?
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.
When should I use Thinking Turbo vs standard K2 Turbo?
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.
What context window does Kimi K2 Thinking Turbo support?
262.1K tokens, consistent with the K2 family.
How does Kimi K2 Thinking Turbo handle very complex mathematical or logical problems?
It fits moderate-complexity problems. For frontier reasoning difficulty, Kimi K2 Thinking with the full deliberation budget is the better fit.
How do I use Kimi K2 Thinking Turbo on AI Gateway?
Use the identifier
moonshotai/kimi-k2-thinking-turbowith any supported interface. AI Gateway handles provider routing automatically.