GLM-4.7 vs Kimi K2.5
Detailed pricing comparison and cost analysis.
Updated September 2026
Cost Simulator
| Feature | GLM-4.7 | Kimi K2.5 |
|---|---|---|
| Provider | Zhipu AI | Moonshot AI |
| Input Price (1M) | $0.60 | $0.60 |
| Output Price (1M) | $2.20 | $3.00 |
| Context Window | 204,800 | 262,144 |
Verdict
GLM-4.7 costs $0.60 per 1M input tokens and $2.20 per 1M output tokens. Kimi K2.5 costs $0.60 per 1M input tokens and $3.00 per 1M output tokens. GLM-4.7 and Kimi K2.5 have identical input token pricing. For output tokens, GLM-4.7 is the more affordable option at $2.20/1M vs $3.00.
On context window, Kimi K2.5 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 GLM-4.7
- ✓ Your workload is output-heavy — GLM-4.7 generates text cheaper
- ✓ You are already integrated with Zhipu AI
When to choose Kimi K2.5
- ✓ You need a larger context window (262,144 tokens)
- ✓ You are already integrated with Moonshot 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 GLM-4.7 cheaper than Kimi K2.5? ▼
GLM-4.7 and Kimi K2.5 have identical input token pricing at $0.60/1M tokens.
What is the context window of GLM-4.7 vs Kimi K2.5? ▼
GLM-4.7 has a 204,800-token context window. Kimi K2.5 has a 262,144-token context window. Kimi K2.5 supports the larger context, suitable for longer documents and agentic workflows.
Which model is better: GLM-4.7 or Kimi K2.5? ▼
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 K2.5 supports 262,144 tokens. Use the comparison table above to find the right fit for your workload.