GLM-4.7 vs GPT-5.4 nano
Detailed pricing comparison and cost analysis.
Updated September 2026
Cost Simulator
| Feature | GLM-4.7 | GPT-5.4 nano |
|---|---|---|
| Provider | Zhipu AI | OpenAI |
| Input Price (1M) | $0.60 | $0.20 |
| Output Price (1M) | $2.20 | $1.25 |
| Context Window | 204,800 | 400,000 |
Verdict
GLM-4.7 costs $0.60 per 1M input tokens and $2.20 per 1M output tokens. GPT-5.4 nano costs $0.20 per 1M input tokens and $1.25 per 1M output tokens. GPT-5.4 nano is 67% cheaper on input tokens than GLM-4.7. For output tokens, GPT-5.4 nano is the more affordable option at $1.25/1M vs $2.20.
On context window, GPT-5.4 nano supports 400,000 tokens — meaning it can fit more conversation history, documents, or code in a single request. This matters for RAG pipelines, long document analysis, and agentic workflows where context builds up over many turns.
When to choose GLM-4.7
- ✓ You are already integrated with Zhipu AI
When to choose GPT-5.4 nano
- ✓ You need the lowest input token cost ($ 0.20/1M)
- ✓ Your workload is output-heavy — GPT-5.4 nano generates text cheaper
- ✓ You need a larger context window (400,000 tokens)
- ✓ You are already integrated with OpenAI
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 GPT-5.4 nano? ▼
GPT-5.4 nano is cheaper on input tokens at $0.20/1M vs $0.60/1M for GLM-4.7 — a 67% saving.
What is the context window of GLM-4.7 vs GPT-5.4 nano? ▼
GLM-4.7 has a 204,800-token context window. GPT-5.4 nano has a 400,000-token context window. GPT-5.4 nano supports the larger context, suitable for longer documents and agentic workflows.
Which model is better: GLM-4.7 or GPT-5.4 nano? ▼
The best choice depends on your use case. For cost efficiency on input tokens, GPT-5.4 nano is the cheaper option. For maximum context length, GPT-5.4 nano supports 400,000 tokens. Use the comparison table above to find the right fit for your workload.