Simulating realistic Data extraction & tagging parameters (2,000 in / 250 out with 20% cache reuse). MiniMax-01 (4M Context) delivers a 86% cost reduction over GLM 4.7 (Zhipu).
| Traffic Volume Tier | GLM 4.7 (Zhipu) Monthly | MiniMax-01 (4M Context) Monthly | Monthly Savings by picking MiniMax-01 (4M Context) |
|---|---|---|---|
| 1,000 reqs/mo (Dev/Testing) | $4.378 | $0.611 | Save $3.767 / mo |
| 10,000 reqs/mo (Small App) | $43.775 | $6.11 | Save $37.665 / mo |
| 100,000 reqs/mo (Growth Production) | $437.75 | $61.10 | Save $376.65 / mo |
| 1,000,000 reqs/mo (Scale SaaS) | $4,377.50 | $611.00 | Save $3,766.50 / mo |
MiniMax-01 (4M Context) is 86% cheaper for Data extraction & tagging workloads. At standard Data extraction & tagging parameter ratios (2,000 input tokens, 250 output tokens, 20% cache hit), MiniMax-01 (4M Context) costs $0.000611 per request compared to $0.004378 on GLM 4.7 (Zhipu).
GLM 4.7 (Zhipu) offers a context window of 128,000 tokens (max output: 16,384), while MiniMax-01 (4M Context) offers 4,000,000 tokens (max output: 64,000).
At 100,000 requests per month, using MiniMax-01 (4M Context) saves $376.65 every month (or $4,519.80 annually) compared to GLM 4.7 (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.