Kimi K2.6 vs Qwen3.7-Max
Detailed pricing comparison and cost analysis.
Updated September 2026
Cost Simulator
| Feature | Kimi K2.6 | Qwen3.7-Max |
|---|---|---|
| Provider | Moonshot AI | Alibaba |
| Input Price (1M) | $0.95 | $1.25 |
| Output Price (1M) | $4.00 | $3.75 |
| Context Window | 262,144 | 1,000,000 |
Verdict
Kimi K2.6 costs $0.95 per 1M input tokens and $4.00 per 1M output tokens. Qwen3.7-Max costs $1.25 per 1M input tokens and $3.75 per 1M output tokens. Kimi K2.6 is 24% cheaper on input tokens than Qwen3.7-Max. For output tokens, Qwen3.7-Max is the more affordable option at $3.75/1M vs $4.00.
On context window, Qwen3.7-Max supports 1,000,000 tokens — meaning it can fit more conversation history, documents, or code in a single request. This matters for RAG pipelines, long document analysis, and agentic workflows where context builds up over many turns.
When to choose Kimi K2.6
- ✓ You need the lowest input token cost ($ 0.95/1M)
- ✓ You are already integrated with Moonshot AI
When to choose Qwen3.7-Max
- ✓ Your workload is output-heavy — Qwen3.7-Max generates text cheaper
- ✓ You need a larger context window (1,000,000 tokens)
- ✓ You are already integrated with Alibaba
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 K2.6 cheaper than Qwen3.7-Max? ▼
Kimi K2.6 is cheaper on input tokens at $0.95/1M vs $1.25/1M for Qwen3.7-Max — a 24% saving.
What is the context window of Kimi K2.6 vs Qwen3.7-Max? ▼
Kimi K2.6 has a 262,144-token context window. Qwen3.7-Max has a 1,000,000-token context window. Qwen3.7-Max supports the larger context, suitable for longer documents and agentic workflows.
Which model is better: Kimi K2.6 or Qwen3.7-Max? ▼
The best choice depends on your use case. For cost efficiency on input tokens, Kimi K2.6 is the cheaper option. For maximum context length, Qwen3.7-Max supports 1,000,000 tokens. Use the comparison table above to find the right fit for your workload.