Text model
moonshot.kimi-k2-thinking pricing
Amazon Bedrock charges $0.60 per million input tokens and $2.50 per million output tokens for moonshot.kimi-k2-thinking. Below: every rate that applies, what common workloads cost, and cheaper options.
- Input
- $0.60/ 1M
- Output
- $2.50/ 1M
- Context
- 256Ktokens
Retires 2027-03-30. npx @veqen/sdk scan finds every place your code uses it.
All rates
| Tier | Input | Output | Cache read | Cache write |
|---|---|---|---|---|
| Standard | $0.60 | $2.50 | — | — |
| Batch | $0.30 | $1.25 | — | — |
| Priority | $1.05 | $4.38 | — | — |
USD per million tokens. Long-context tiers apply when the whole prompt, cached tokens included, passes the threshold.
Work it out
What common workloads cost
| Workload | Per call | Cost per call | Calls a month | Per month |
|---|---|---|---|---|
| Chat reply | 1,500 input and 400 output tokens | $0.0019 | 100,000 | $190.00 |
| RAG answer, cached context | 2,000 fresh + 4,000 cached input, 500 output | $0.00485 | 100,000 | $485.00 |
| Agent step | 4,000 fresh + 8,000 cached input, 800 output | $0.0092 | 250,000 | $2,300.00 |
| Batch summary | 3,000 input and 300 output tokens via the Batch API | $0.00128 | 1,000,000 | $1,275.00 |
Planning a mix of models? Simulate a workload with moonshot.kimi-k2-thinking, alongside anything else you run.
Cheaper options
Current models that cost at least 15% less for a typical chat reply. Cheaper isn't always equivalent: test quality on your own prompts, then use veqen diff to see the impact at your volume.
| Model | Chat reply | Saving |
|---|---|---|
| bedrock/amazon.nova-2-lite-v1:0 | $0.0016 | 16% |
| bedrock/qwen.qwen3-coder-next | $0.00123 | 35% |
| deepinfra/Qwen/Qwen3-235B-A22B-Thinking-2507 | $0.00161 | 15% |
| deepinfra/deepseek-ai/DeepSeek-R1-0528 | $0.00161 | 15% |
Price history
No price changes since Veqen started tracking this model.
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Last verified with price book 2026-10-22. Sources: Amazon Bedrock pricing and LiteLLM's MIT-licensed price file. Track every call to this model, per customer or project: install the SDK.