Simulating realistic Data extraction & tagging parameters (2,000 in / 250 out with 20% cache reuse). GLM-5 (Z.ai) delivers a 43% cost reduction over Gemini 3.6 Flash.
| Traffic Volume Tier | Gemini 3.6 Flash Monthly | GLM-5 (Z.ai) Monthly | Monthly Savings by picking GLM-5 (Z.ai) |
|---|---|---|---|
| 1,000 reqs/mo (Dev/Testing) | $4.335 | $2.48 | Save $1.855 / mo |
| 10,000 reqs/mo (Small App) | $43.35 | $24.80 | Save $18.55 / mo |
| 100,000 reqs/mo (Growth Production) | $433.50 | $248.00 | Save $185.50 / mo |
| 1,000,000 reqs/mo (Scale SaaS) | $4,335.00 | $2,480.00 | Save $1,855.00 / mo |
GLM-5 (Z.ai) is 43% 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 (Z.ai) costs $0.00248 per request compared to $0.004335 on Gemini 3.6 Flash.
Gemini 3.6 Flash offers a context window of 1,000,000 tokens (max output: 64,000), while GLM-5 (Z.ai) offers 200,000 tokens (max output: 131,072).
At 100,000 requests per month, using GLM-5 (Z.ai) saves $185.50 every month (or $2,226.00 annually) compared to Gemini 3.6 Flash.
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.