Simulating realistic Data extraction & tagging parameters (2,000 in / 250 out with 20% cache reuse). Codestral 2501 delivers a 71% cost reduction over GLM-5 (Z.ai).
| Traffic Volume Tier | Codestral 2501 Monthly | GLM-5 (Z.ai) Monthly | Monthly Savings by picking Codestral 2501 |
|---|---|---|---|
| 1,000 reqs/mo (Dev/Testing) | $0.717 | $2.48 | Save $1.763 / mo |
| 10,000 reqs/mo (Small App) | $7.17 | $24.80 | Save $17.63 / mo |
| 100,000 reqs/mo (Growth Production) | $71.70 | $248.00 | Save $176.30 / mo |
| 1,000,000 reqs/mo (Scale SaaS) | $717.00 | $2,480.00 | Save $1,763.00 / mo |
Codestral 2501 is 71% cheaper for Data extraction & tagging workloads. At standard Data extraction & tagging parameter ratios (2,000 input tokens, 250 output tokens, 20% cache hit), Codestral 2501 costs $0.000717 per request compared to $0.00248 on GLM-5 (Z.ai).
Codestral 2501 offers a context window of 256,000 tokens (max output: 8,192), while GLM-5 (Z.ai) offers 200,000 tokens (max output: 131,072).
At 100,000 requests per month, using Codestral 2501 saves $176.30 every month (or $2,115.60 annually) compared to GLM-5 (Z.ai).
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.