Simulating realistic Data extraction & tagging parameters (2,000 in / 250 out with 20% cache reuse). Mistral Medium 3.5 delivers a 1% cost reduction over GLM 4.7 (Zhipu).
| Traffic Volume Tier | Mistral Medium 3.5 Monthly | GLM 4.7 (Zhipu) Monthly | Monthly Savings by picking Mistral Medium 3.5 |
|---|---|---|---|
| 1,000 reqs/mo (Dev/Testing) | $4.335 | $4.378 | Save $0.0425 / mo |
| 10,000 reqs/mo (Small App) | $43.35 | $43.775 | Save $0.425 / mo |
| 100,000 reqs/mo (Growth Production) | $433.50 | $437.75 | Save $4.25 / mo |
| 1,000,000 reqs/mo (Scale SaaS) | $4,335.00 | $4,377.50 | Save $42.50 / mo |
Mistral Medium 3.5 is 1% cheaper for Data extraction & tagging workloads. At standard Data extraction & tagging parameter ratios (2,000 input tokens, 250 output tokens, 20% cache hit), Mistral Medium 3.5 costs $0.004335 per request compared to $0.004378 on GLM 4.7 (Zhipu).
Mistral Medium 3.5 offers a context window of 256,000 tokens (max output: 32,768), while GLM 4.7 (Zhipu) offers 128,000 tokens (max output: 16,384).
At 100,000 requests per month, using Mistral Medium 3.5 saves $4.25 every month (or $51.00 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.