Kimi K2 Thinking
Kimi K2 Thinking is worth evaluating for rag, long context, and classification when its provider route and context window match the workload.
Use it for
- Teams evaluating rag, long context, and classification
- Workloads that can use a 256k context window
- Buyers comparing 4 tracked provider routes
Do not use it for
- Vision or document-understanding workloads
Cheapest of 7 routes · AWS Bedrock
About
Extended thinking variant of Kimi K2 with native reasoning capabilities. 256K context.
Kimi K2 Thinking is a reasoning-specialized model developed by Moonshot AI, produced by post-training the Kimi K2 foundation model for extended chain-of-thought reasoning. Kimi K2 is a trillion-parameter mixture-of-experts model that activates 32 billion parameters per forward pass, organized with 384 total experts from which 8 are routed per token plus 1 shared expert per token. The model supports a 256,000-token context window. Kimi K2 was trained on 15.5 trillion tokens using Moonshot's proprietary MuonClip optimizer, which enabled training without instabilities at that scale.
The K2 Thinking post-training adapts the base model to interleave extended reasoning steps with tool calls, enabling it to sustain coherent multi-step task execution across hundreds of sequential actions without drift. This makes it appropriate for autonomous coding, research workflows, and planning tasks that require long chains of intermediate reasoning. K2 Thinking ships in INT4 precision via quantization-native post-training, reducing serving cost compared to FP16 inference.
The model is available via Fireworks AI, OpenRouter, NVIDIA NIM, AWS Bedrock, Google Vertex AI, Novita AI, and the Vercel AI Gateway. It is the reasoning-optimized variant of the Kimi K2 family; the base Kimi K2 Instruct variant is the non-thinking counterpart for standard instruction-following tasks.
Kimi K2 Thinking has a 256k-token context window.
Kimi K2 Thinking input tokens at $0.6/1M, output at $2.5/1M.
Top use-case fit
RAG
Included by capability and metadata signals in the decision map.
Long context
Included by capability and metadata signals in the decision map.
Classification
Included by capability and metadata signals in the decision map.
Provider price ladder
Compare all 7Compare API pricing across 4 providers for input and output tokens, batch, and cached reads when available.
| Provider | Input / 1M | Output / 1M | Route |
|---|---|---|---|
| AWS Bedrock | $0.600 | $2.50 | Serverless |
| Fireworks AI | $0.600 | $2.50 | Serverless |
| GCP Vertex AI | $0.600 | $2.50 | Serverless |
| Novita AI | $0.600 | $2.50 | Serverless |
Capabilities
Benchmark peer barsfor RAG
No task-mapped benchmark peers are available for this model yet.
Migration checks
No linked migration route is available for this model yet.
Rankings & picks(10)
Comparison and alternatives
Browse all comparisons →Cheapest of 7 routes · AWS Bedrock