Simulating realistic Customer support chatbot parameters (3,500 in / 350 out with 70% cache reuse). Codestral 2501 delivers a 85% cost reduction over Mistral Medium 3.5.
| Traffic Volume Tier | Mistral Medium 3.5 Monthly | Codestral 2501 Monthly | Monthly Savings by picking Codestral 2501 |
|---|---|---|---|
| 1,000 reqs/mo (Dev/Testing) | $4.568 | $0.7035 | Save $3.864 / mo |
| 10,000 reqs/mo (Small App) | $45.675 | $7.035 | Save $38.64 / mo |
| 100,000 reqs/mo (Growth Production) | $456.75 | $70.35 | Save $386.40 / mo |
| 1,000,000 reqs/mo (Scale SaaS) | $4,567.50 | $703.50 | Save $3,864.00 / mo |
Codestral 2501 is 85% cheaper for Customer support chatbot workloads. At standard Customer support chatbot parameter ratios (3,500 input tokens, 350 output tokens, 70% cache hit), Codestral 2501 costs $0.000703 per request compared to $0.004568 on Mistral Medium 3.5.
Mistral Medium 3.5 offers a context window of 256,000 tokens (max output: 32,768), while Codestral 2501 offers 256,000 tokens (max output: 8,192).
At 100,000 requests per month, using Codestral 2501 saves $386.40 every month (or $4,636.80 annually) compared to Mistral Medium 3.5.
Cache the system prompt and static documentation chunks — cached input is often 4–10× cheaper. Route simple FAQ turns to a nano-tier model and escalate only complex tickets. Cap max_output per reply; support answers rarely need more than a few hundred tokens.