GLM-4.7 vs GLM-5.1
Detailed pricing comparison and cost analysis.
Updated August 2026
Cost Simulator
| Feature | GLM-4.7 | GLM-5.1 |
|---|---|---|
| Provider | Zhipu AI | Zhipu AI |
| Input Price (1M) | $0.60 | $1.40 |
| Output Price (1M) | $2.20 | $4.40 |
| Context Window | 204,800 | 1,000,000 |
Verdict
GLM-4.7 costs $0.60 per 1M input tokens and $2.20 per 1M output tokens. GLM-5.1 costs $1.40 per 1M input tokens and $4.40 per 1M output tokens. GLM-4.7 is 57% cheaper on input tokens than GLM-5.1. For output tokens, GLM-4.7 is the more affordable option at $2.20/1M vs $4.40.
On context window, GLM-5.1 supports 1,000,000 tokens — meaning it can fit more conversation history, documents, or code in a single request. This matters for RAG pipelines, long document analysis, and agentic workflows where context builds up over many turns.
When to choose GLM-4.7
- ✓ You need the lowest input token cost ($ 0.60/1M)
- ✓ Your workload is output-heavy — GLM-4.7 generates text cheaper
- ✓ You are already integrated with Zhipu AI
When to choose GLM-5.1
- ✓ You need a larger context window (1,000,000 tokens)
- ✓ 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 GLM-4.7 cheaper than GLM-5.1? ▼
GLM-4.7 is cheaper on input tokens at $0.60/1M vs $1.40/1M for GLM-5.1 — a 57% saving.
What is the context window of GLM-4.7 vs GLM-5.1? ▼
GLM-4.7 has a 204,800-token context window. GLM-5.1 has a 1,000,000-token context window. GLM-5.1 supports the larger context, suitable for longer documents and agentic workflows.
Which model is better: GLM-4.7 or GLM-5.1? ▼
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, GLM-5.1 supports 1,000,000 tokens. Use the comparison table above to find the right fit for your workload.