Simulating realistic Data extraction & tagging parameters (2,000 in / 250 out with 20% cache reuse). Gemma 4 31B Instruct delivers a 88% cost reduction over GLM 5.2 (Zhipu).
| Traffic Volume Tier | Gemma 4 31B Instruct Monthly | GLM 5.2 (Zhipu) Monthly | Monthly Savings by picking Gemma 4 31B Instruct |
|---|---|---|---|
| 1,000 reqs/mo (Dev/Testing) | $0.6875 | $5.78 | Save $5.093 / mo |
| 10,000 reqs/mo (Small App) | $6.875 | $57.80 | Save $50.925 / mo |
| 100,000 reqs/mo (Growth Production) | $68.75 | $578.00 | Save $509.25 / mo |
| 1,000,000 reqs/mo (Scale SaaS) | $687.50 | $5,780.00 | Save $5,092.50 / mo |
Gemma 4 31B Instruct is 88% cheaper for Data extraction & tagging workloads. At standard Data extraction & tagging parameter ratios (2,000 input tokens, 250 output tokens, 20% cache hit), Gemma 4 31B Instruct costs $0.000688 per request compared to $0.00578 on GLM 5.2 (Zhipu).
Gemma 4 31B Instruct offers a context window of 128,000 tokens (max output: 8,192), while GLM 5.2 (Zhipu) offers 1,000,000 tokens (max output: 64,000).
At 100,000 requests per month, using Gemma 4 31B Instruct saves $509.25 every month (or $6,111.00 annually) compared to GLM 5.2 (Zhipu).
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.