Simulating realistic Data extraction & tagging parameters (2,000 in / 250 out with 20% cache reuse). GLM 5.2 (Zhipu) delivers a 8% cost reduction over Gemini 3.1 Pro.
| Traffic Volume Tier | Gemini 3.1 Pro Monthly | GLM 5.2 (Zhipu) Monthly | Monthly Savings by picking GLM 5.2 (Zhipu) |
|---|---|---|---|
| 1,000 reqs/mo (Dev/Testing) | $6.28 | $5.78 | Save $0.50 / mo |
| 10,000 reqs/mo (Small App) | $62.80 | $57.80 | Save $5.00 / mo |
| 100,000 reqs/mo (Growth Production) | $628.00 | $578.00 | Save $50.00 / mo |
| 1,000,000 reqs/mo (Scale SaaS) | $6,280.00 | $5,780.00 | Save $500.00 / mo |
GLM 5.2 (Zhipu) is 8% cheaper for Data extraction & tagging workloads. At standard Data extraction & tagging parameter ratios (2,000 input tokens, 250 output tokens, 20% cache hit), GLM 5.2 (Zhipu) costs $0.00578 per request compared to $0.00628 on Gemini 3.1 Pro.
Gemini 3.1 Pro offers a context window of 2,000,000 tokens (max output: 128,000), while GLM 5.2 (Zhipu) offers 1,000,000 tokens (max output: 64,000).
At 100,000 requests per month, using GLM 5.2 (Zhipu) saves $50.00 every month (or $600.00 annually) compared to Gemini 3.1 Pro.
Volume makes nano-tier models attractive — extraction rarely needs frontier reasoning. Constrain output with JSON schema modes to avoid retry loops on malformed responses. Cache shared schema instructions and few-shot examples across calls.