Simulating realistic Document summarization parameters (25,000 in / 600 out with 10% cache reuse). Mistral Large 3 delivers a 98% cost reduction over GPT-5.5 Pro.
| Traffic Volume Tier | GPT-5.5 Pro Monthly | Mistral Large 3 Monthly | Monthly Savings by picking Mistral Large 3 |
|---|---|---|---|
| 1,000 reqs/mo (Dev/Testing) | $790.50 | $12.275 | Save $778.225 / mo |
| 10,000 reqs/mo (Small App) | $7,905.00 | $122.75 | Save $7,782.25 / mo |
| 100,000 reqs/mo (Growth Production) | $79,050.00 | $1,227.50 | Save $77,822.50 / mo |
| 1,000,000 reqs/mo (Scale SaaS) | $790,500.00 | $12,275.00 | Save $778,225.00 / mo |
Mistral Large 3 is 98% cheaper for Document summarization workloads. At standard Document summarization parameter ratios (25,000 input tokens, 600 output tokens, 10% cache hit), Mistral Large 3 costs $0.0123 per request compared to $0.7905 on GPT-5.5 Pro.
GPT-5.5 Pro offers a context window of 512,000 tokens (max output: 64,000), while Mistral Large 3 offers 1,000,000 tokens (max output: 64,000).
At 100,000 requests per month, using Mistral Large 3 saves $77,822.50 every month (or $933,870.00 annually) compared to GPT-5.5 Pro.
Long-context models pay off here — compare price per 1M tokens at your true document size. Summarize once, store the result; don't re-summarize unchanged documents. For batch backfills, nightly jobs can use cache-friendly request ordering.