Gemini 3.1 Pro vs GLM-5.2
Detailed pricing comparison and cost analysis.
Updated September 2026
Cost Simulator
| Feature | Gemini 3.1 Pro | GLM-5.2 |
|---|---|---|
| Provider | Zhipu AI | |
| Input Price (1M) | $2.00 | $1.40 |
| Output Price (1M) | $12.00 | $4.40 |
| Context Window | 1,000,000 | 1,000,000 |
Verdict
Gemini 3.1 Pro costs $2.00 per 1M input tokens and $12.00 per 1M output tokens. GLM-5.2 costs $1.40 per 1M input tokens and $4.40 per 1M output tokens. GLM-5.2 is 30% cheaper on input tokens than Gemini 3.1 Pro. For output tokens, GLM-5.2 is the more affordable option at $4.40/1M vs $12.00.
On context window, Gemini 3.1 Pro supports 1,000,000 tokens — meaning it can fit more conversation history, documents, or code in a single request. This matters for RAG pipelines, long document analysis, and agentic workflows where context builds up over many turns.
When to choose Gemini 3.1 Pro
- ✓ You are already integrated with Google
When to choose GLM-5.2
- ✓ You need the lowest input token cost ($ 1.40/1M)
- ✓ Your workload is output-heavy — GLM-5.2 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 Gemini 3.1 Pro cheaper than GLM-5.2? ▼
GLM-5.2 is cheaper on input tokens at $1.40/1M vs $2.00/1M for Gemini 3.1 Pro — a 30% saving.
What is the context window of Gemini 3.1 Pro vs GLM-5.2? ▼
Gemini 3.1 Pro has a 1,000,000-token context window. GLM-5.2 has a 1,000,000-token context window. Gemini 3.1 Pro supports the larger context, suitable for longer documents and agentic workflows.
Which model is better: Gemini 3.1 Pro or GLM-5.2? ▼
The best choice depends on your use case. For cost efficiency on input tokens, GLM-5.2 is the cheaper option. For maximum context length, Gemini 3.1 Pro supports 1,000,000 tokens. Use the comparison table above to find the right fit for your workload.