Simulating realistic Data extraction & tagging parameters (2,000 in / 250 out with 20% cache reuse). Pixtral Large delivers a 95% cost reduction over GPT-5.5 Pro.
| Traffic Volume Tier | GPT-5.5 Pro Monthly | Pixtral Large Monthly | Monthly Savings by picking Pixtral Large |
|---|---|---|---|
| 1,000 reqs/mo (Dev/Testing) | $94.20 | $4.78 | Save $89.42 / mo |
| 10,000 reqs/mo (Small App) | $942.00 | $47.80 | Save $894.20 / mo |
| 100,000 reqs/mo (Growth Production) | $9,420.00 | $478.00 | Save $8,942.00 / mo |
| 1,000,000 reqs/mo (Scale SaaS) | $94,200.00 | $4,780.00 | Save $89,420.00 / mo |
Pixtral Large is 95% cheaper for Data extraction & tagging workloads. At standard Data extraction & tagging parameter ratios (2,000 input tokens, 250 output tokens, 20% cache hit), Pixtral Large costs $0.00478 per request compared to $0.0942 on GPT-5.5 Pro.
GPT-5.5 Pro offers a context window of 512,000 tokens (max output: 64,000), while Pixtral Large offers 128,000 tokens (max output: 8,192).
At 100,000 requests per month, using Pixtral Large saves $8,942.00 every month (or $107,304.00 annually) compared to GPT-5.5 Pro.
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.