Simulating realistic Data extraction & tagging parameters (2,000 in / 250 out with 20% cache reuse). Codestral 2501 delivers a 70% cost reduction over Cerebras — Gemma 4 31B.
| Traffic Volume Tier | Codestral 2501 Monthly | Cerebras — Gemma 4 31B Monthly | Monthly Savings by picking Codestral 2501 |
|---|---|---|---|
| 1,000 reqs/mo (Dev/Testing) | $0.717 | $2.353 | Save $1.635 / mo |
| 10,000 reqs/mo (Small App) | $7.17 | $23.525 | Save $16.355 / mo |
| 100,000 reqs/mo (Growth Production) | $71.70 | $235.25 | Save $163.55 / mo |
| 1,000,000 reqs/mo (Scale SaaS) | $717.00 | $2,352.50 | Save $1,635.50 / mo |
Codestral 2501 is 70% 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 $0.002353 on Cerebras — Gemma 4 31B.
Codestral 2501 offers a context window of 256,000 tokens (max output: 8,192), while Cerebras — Gemma 4 31B offers 128,000 tokens (max output: 8,192).
At 100,000 requests per month, using Codestral 2501 saves $163.55 every month (or $1,962.60 annually) compared to Cerebras — Gemma 4 31B.
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.