Kimi K2.7 Code vs GLM-4.7
Detailed pricing comparison and cost analysis.
Updated September 2026
Cost Simulator
| Feature | Kimi K2.7 Code | GLM-4.7 |
|---|---|---|
| Provider | Moonshot AI | Zhipu AI |
| Input Price (1M) | $0.95 | $0.60 |
| Output Price (1M) | $4.00 | $2.20 |
| Context Window | 262,144 | 204,800 |
Verdict
Kimi K2.7 Code costs $0.95 per 1M input tokens and $4.00 per 1M output tokens. GLM-4.7 costs $0.60 per 1M input tokens and $2.20 per 1M output tokens. GLM-4.7 is 37% cheaper on input tokens than Kimi K2.7 Code. For output tokens, GLM-4.7 is the more affordable option at $2.20/1M vs $4.00.
On context window, Kimi K2.7 Code supports 262,144 tokens — meaning it can fit more conversation history, documents, or code in a single request. This matters for RAG pipelines, long document analysis, and agentic workflows where context builds up over many turns.
When to choose Kimi K2.7 Code
- ✓ You need a larger context window (262,144 tokens)
- ✓ You are already integrated with Moonshot AI
When to choose GLM-4.7
- ✓ You need the lowest input token cost ($ 0.60/1M)
- ✓ Your workload is output-heavy — GLM-4.7 generates text cheaper
- ✓ You are already integrated with Zhipu AI
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 GLM-4.7? ▼
GLM-4.7 is cheaper on input tokens at $0.60/1M vs $0.95/1M for Kimi K2.7 Code — a 37% saving.
What is the context window of Kimi K2.7 Code vs GLM-4.7? ▼
Kimi K2.7 Code has a 262,144-token context window. GLM-4.7 has a 204,800-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 GLM-4.7? ▼
The best choice depends on your use case. For cost efficiency on input tokens, GLM-4.7 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.