Kimi K2 Thinking
Kimi K2 Thinking is a reasoning-oriented chat model from Kimi (Moonshot AI), extending the Kimi K2 line with explicit step-by-step deliberation before answering.
API Pricing
Cheapest on Amazon AWS — 49% below avg| Provider | Input / 1M | Output / 1M | Cached / 1M |
|---|---|---|---|
| $0.300 | $1.25 | - | |
| $0.600 | $2.50 | - | |
| $0.600 | $2.50 | $0.300 | |
| $0.600 | $2.50 | $0.150 | |
| $0.600 | $2.50 | $0.150 | |
| $0.800 | $1.20 | - |
Prices updated daily. Last check: Sep 30, 2026
Kimi K2 Thinking pricing by provider
Input, output, and batch rates, plus alternatives, for one provider at a time.
Performance & Benchmarks
Source: Artificial Analysis →Reasoning & Knowledge
- MMLU-Pro84.8%
- GPQA Diamond83.8%
- Humanity's Last Exam23.8%
Coding
- LiveCodeBench85.3%
Math
- AIME 202594.7%
Agentic & Tool Use
- Terminal-Bench Hard31.1%
- τ²-bench93.0%
Instruction & Long Context
- IFBench68.1%
- Long-Context Reasoning72.0%
Benchmarks measured Sep 2026. Scores are independent evaluations, not vendor-reported.
Model Details
General
- Creator
- Kimi
- Modalities
- Text
Capabilities
- Tool Calling
- No
- Open Source
- No
Strengths & Limitations
Strengths
- Reasoning variant of the Kimi K2 family, designed to deliberate before answering rather than responding in a single pass
- Suited to multi-step problems where intermediate reasoning improves final answer quality
- Available through multiple third-party inference providers, so buyers can compare serving options rather than being tied to one endpoint
- Sits alongside non-thinking K2 models, making it straightforward to route easy prompts to a cheaper sibling and hard prompts here
- Chat-completions style interface, so it drops into existing LLM application code with minimal changes
- Extended reasoning output is useful for debugging agent behavior, since the deliberation trace shows how a conclusion was reached
Limitations
- Reasoning models generate extra tokens per request, which raises both latency and effective cost per answer
- We do not currently have verified throughput or time-to-first-token measurements for this model in our database
- Context window and supported input modalities are not tracked in our metadata — confirm with the serving provider
- Deliberation overhead is wasted on simple, short-form tasks that a direct-answer model handles equally well
- Performance and pricing differ between hosting providers, so results are not identical across endpoints
Key Features
About Kimi K2 Thinking
Common Use Cases
Kimi K2 Thinking fits workloads where the cost of a wrong answer exceeds the cost of extra generated tokens: mathematical and logical problem solving, debugging and refactoring code across multiple files, planning steps in an agentic pipeline, and analytical writing that has to reconcile several constraints at once. It is a poor fit for high-volume, latency-sensitive jobs such as classification, short summarization, autocomplete, or chat responses that must appear instantly — those are better served by a direct-answer model, including non-thinking members of the Kimi K2 line. A common deployment pattern is routing: send routine prompts to a cheaper, faster model and escalate only the hard ones to the thinking variant.
Frequently Asked Questions
How much does Kimi K2 Thinking cost to run?
Pricing depends on which inference provider serves the model and on the pricing type — separate input and output token rates, batch or cached-input discounts, and provider-specific terms all apply. Reasoning models also consume more output tokens per request than direct-answer models, which affects the effective cost per completed task. See the pricing table on this page for the providers we currently track.
What is Kimi K2 Thinking best used for?
Multi-step reasoning work: math and logic problems, code debugging and refactoring, agentic workflows involving sequential tool calls, and analysis that requires weighing several constraints. It is less appropriate for high-throughput, low-latency tasks like classification or short summarization.
How does Kimi K2 Thinking differ from other Kimi K2 models?
The "Thinking" designation identifies it as the reasoning variant of the K2 family. It performs an explicit deliberation phase before producing its final answer, whereas non-thinking K2 models respond directly. The trade-off is higher token usage and longer response times in exchange for more careful handling of hard, multi-step prompts.
What context window and input modalities does it support?
Our database does not currently hold verified context window or modality figures for Kimi K2 Thinking. Check the documentation of the specific provider you plan to use, since serving configurations and exposed limits can differ between endpoints.
How fast is Kimi K2 Thinking?
We do not have populated throughput or time-to-first-token measurements for this model. As a general property of reasoning models, perceived latency is higher than for direct-answer models because the deliberation phase is generated before the visible response, and speed also varies by hosting provider.