Simulating realistic AI coding assistant parameters (12,000 in / 2,000 out with 60% cache reuse). Qwen 2.5 Max delivers a 7% cost reduction over Mistral Medium 3.5.
| Traffic Volume Tier | Mistral Medium 3.5 Monthly | Qwen 2.5 Max Monthly | Monthly Savings by picking Qwen 2.5 Max |
|---|---|---|---|
| 1,000 reqs/mo (Dev/Testing) | $23.28 | $21.632 | Save $1.648 / mo |
| 10,000 reqs/mo (Small App) | $232.80 | $216.32 | Save $16.48 / mo |
| 100,000 reqs/mo (Growth Production) | $2,328.00 | $2,163.20 | Save $164.80 / mo |
| 1,000,000 reqs/mo (Scale SaaS) | $23,280.00 | $21,632.00 | Save $1,648.00 / mo |
Qwen 2.5 Max is 7% cheaper for AI coding assistant workloads. At standard AI coding assistant parameter ratios (12,000 input tokens, 2,000 output tokens, 60% cache hit), Qwen 2.5 Max costs $0.0216 per request compared to $0.0233 on Mistral Medium 3.5.
Mistral Medium 3.5 offers a context window of 256,000 tokens (max output: 32,768), while Qwen 2.5 Max offers 32,768 tokens (max output: 8,192).
At 100,000 requests per month, using Qwen 2.5 Max saves $164.80 every month (or $1,977.60 annually) compared to Mistral Medium 3.5.
Output is the expensive side — prefer models with cheap output for autocomplete-style calls. Cache repository context between keystrokes; diffs change far less than the full file. Measure acceptance rate: paying for output users delete is pure waste.