GLM-5.1 vs GPT-5 mini
Detailed pricing comparison and cost analysis.
Updated September 2026
Cost Simulator
| Feature | GLM-5.1 | GPT-5 mini |
|---|---|---|
| Provider | Zhipu AI | OpenAI |
| Input Price (1M) | $1.40 | $0.25 |
| Output Price (1M) | $4.40 | $2.00 |
| Context Window | 1,000,000 | 128,000 |
Verdict
GLM-5.1 costs $1.40 per 1M input tokens and $4.40 per 1M output tokens. GPT-5 mini costs $0.25 per 1M input tokens and $2.00 per 1M output tokens. GPT-5 mini is 82% cheaper on input tokens than GLM-5.1. For output tokens, GPT-5 mini is the more affordable option at $2.00/1M vs $4.40.
On context window, GLM-5.1 supports 1,000,000 tokens — meaning it can fit more conversation history, documents, or code in a single request. This matters for RAG pipelines, long document analysis, and agentic workflows where context builds up over many turns.
When to choose GLM-5.1
- ✓ You need a larger context window (1,000,000 tokens)
- ✓ You are already integrated with Zhipu AI
When to choose GPT-5 mini
- ✓ You need the lowest input token cost ($ 0.25/1M)
- ✓ Your workload is output-heavy — GPT-5 mini generates text cheaper
- ✓ 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-5.1 cheaper than GPT-5 mini? ▼
GPT-5 mini is cheaper on input tokens at $0.25/1M vs $1.40/1M for GLM-5.1 — a 82% saving.
What is the context window of GLM-5.1 vs GPT-5 mini? ▼
GLM-5.1 has a 1,000,000-token context window. GPT-5 mini has a 128,000-token context window. GLM-5.1 supports the larger context, suitable for longer documents and agentic workflows.
Which model is better: GLM-5.1 or GPT-5 mini? ▼
The best choice depends on your use case. For cost efficiency on input tokens, GPT-5 mini 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.