Simulating realistic Data extraction & tagging parameters (2,000 in / 250 out with 20% cache reuse). GLM-4.5 (Z.ai) delivers a 75% cost reduction over Gemini 3.1 Pro.
| Traffic Volume Tier | Gemini 3.1 Pro Monthly | GLM-4.5 (Z.ai) Monthly | Monthly Savings by picking GLM-4.5 (Z.ai) |
|---|---|---|---|
| 1,000 reqs/mo (Dev/Testing) | $6.28 | $1.554 | Save $4.726 / mo |
| 10,000 reqs/mo (Small App) | $62.80 | $15.54 | Save $47.26 / mo |
| 100,000 reqs/mo (Growth Production) | $628.00 | $155.40 | Save $472.60 / mo |
| 1,000,000 reqs/mo (Scale SaaS) | $6,280.00 | $1,554.00 | Save $4,726.00 / mo |
GLM-4.5 (Z.ai) is 75% cheaper for Data extraction & tagging workloads. At standard Data extraction & tagging parameter ratios (2,000 input tokens, 250 output tokens, 20% cache hit), GLM-4.5 (Z.ai) costs $0.001554 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-4.5 (Z.ai) offers 128,000 tokens (max output: 98,304).
At 100,000 requests per month, using GLM-4.5 (Z.ai) saves $472.60 every month (or $5,671.20 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.