GPT-5.4 vs GLM-5.1
Detailed pricing comparison and cost analysis.
Updated September 2026
Cost Simulator
| Feature | GPT-5.4 | GLM-5.1 |
|---|---|---|
| Provider | OpenAI | Zhipu AI |
| Input Price (1M) | $2.50 | $1.40 |
| Output Price (1M) | $15.00 | $4.40 |
| Context Window | 1,050,000 | 1,000,000 |
Verdict
GPT-5.4 costs $2.50 per 1M input tokens and $15.00 per 1M output tokens. GLM-5.1 costs $1.40 per 1M input tokens and $4.40 per 1M output tokens. GLM-5.1 is 44% cheaper on input tokens than GPT-5.4. For output tokens, GLM-5.1 is the more affordable option at $4.40/1M vs $15.00.
On context window, GPT-5.4 supports 1,050,000 tokens — meaning it can fit more conversation history, documents, or code in a single request. This matters for RAG pipelines, long document analysis, and agentic workflows where context builds up over many turns.
When to choose GPT-5.4
- ✓ You need a larger context window (1,050,000 tokens)
- ✓ You are already integrated with OpenAI
When to choose GLM-5.1
- ✓ You need the lowest input token cost ($ 1.40/1M)
- ✓ Your workload is output-heavy — GLM-5.1 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 GPT-5.4 cheaper than GLM-5.1? ▼
GLM-5.1 is cheaper on input tokens at $1.40/1M vs $2.50/1M for GPT-5.4 — a 44% saving.
What is the context window of GPT-5.4 vs GLM-5.1? ▼
GPT-5.4 has a 1,050,000-token context window. GLM-5.1 has a 1,000,000-token context window. GPT-5.4 supports the larger context, suitable for longer documents and agentic workflows.
Which model is better: GPT-5.4 or GLM-5.1? ▼
The best choice depends on your use case. For cost efficiency on input tokens, GLM-5.1 is the cheaper option. For maximum context length, GPT-5.4 supports 1,050,000 tokens. Use the comparison table above to find the right fit for your workload.