Kimi K2.7 Code vs DeepSeek R1
Detailed pricing comparison and cost analysis.
Updated September 2026
Cost Simulator
| Feature | Kimi K2.7 Code | DeepSeek R1 |
|---|---|---|
| Provider | Moonshot AI | DeepSeek |
| Input Price (1M) | $0.95 | $0.28 |
| Output Price (1M) | $4.00 | $0.42 |
| Context Window | 262,144 | 64,000 |
Verdict
Kimi K2.7 Code costs $0.95 per 1M input tokens and $4.00 per 1M output tokens. DeepSeek R1 costs $0.28 per 1M input tokens and $0.42 per 1M output tokens. DeepSeek R1 is 71% cheaper on input tokens than Kimi K2.7 Code. For output tokens, DeepSeek R1 is the more affordable option at $0.42/1M vs $4.00.
On context window, Kimi K2.7 Code supports 262,144 tokens — meaning it can fit more conversation history, documents, or code in a single request. This matters for RAG pipelines, long document analysis, and agentic workflows where context builds up over many turns.
When to choose Kimi K2.7 Code
- ✓ You need a larger context window (262,144 tokens)
- ✓ You are already integrated with Moonshot AI
When to choose DeepSeek R1
- ✓ You need the lowest input token cost ($ 0.28/1M)
- ✓ Your workload is output-heavy — DeepSeek R1 generates text cheaper
- ✓ You are already integrated with DeepSeek
Use the calculator above to simulate your specific workload and find the exact break-even point. For most applications, the cheapest model is the one that minimises your total monthly bill given your input-to-output token ratio.
Frequently Asked Questions
Is Kimi K2.7 Code cheaper than DeepSeek R1? ▼
DeepSeek R1 is cheaper on input tokens at $0.28/1M vs $0.95/1M for Kimi K2.7 Code — a 71% saving.
What is the context window of Kimi K2.7 Code vs DeepSeek R1? ▼
Kimi K2.7 Code has a 262,144-token context window. DeepSeek R1 has a 64,000-token context window. Kimi K2.7 Code supports the larger context, suitable for longer documents and agentic workflows.
Which model is better: Kimi K2.7 Code or DeepSeek R1? ▼
The best choice depends on your use case. For cost efficiency on input tokens, DeepSeek R1 is the cheaper option. For maximum context length, Kimi K2.7 Code supports 262,144 tokens. Use the comparison table above to find the right fit for your workload.