Simulating realistic AI coding assistant parameters (12,000 in / 2,000 out with 60% cache reuse). DeepInfra — Llama 3.3 70B delivers a 92% cost reduction over Pixtral Large.
| Traffic Volume Tier | Pixtral Large Monthly | DeepInfra — Llama 3.3 70B Monthly | Monthly Savings by picking DeepInfra — Llama 3.3 70B |
|---|---|---|---|
| 1,000 reqs/mo (Dev/Testing) | $23.04 | $1.80 | Save $21.24 / mo |
| 10,000 reqs/mo (Small App) | $230.40 | $18.00 | Save $212.40 / mo |
| 100,000 reqs/mo (Growth Production) | $2,304.00 | $180.00 | Save $2,124.00 / mo |
| 1,000,000 reqs/mo (Scale SaaS) | $23,040.00 | $1,800.00 | Save $21,240.00 / mo |
DeepInfra — Llama 3.3 70B is 92% cheaper for AI coding assistant workloads. At standard AI coding assistant parameter ratios (12,000 input tokens, 2,000 output tokens, 60% cache hit), DeepInfra — Llama 3.3 70B costs $0.0018 per request compared to $0.023 on Pixtral Large.
Pixtral Large offers a context window of 128,000 tokens (max output: 8,192), while DeepInfra — Llama 3.3 70B offers 128,000 tokens (max output: 8,192).
At 100,000 requests per month, using DeepInfra — Llama 3.3 70B saves $2,124.00 every month (or $25,488.00 annually) compared to Pixtral Large.
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.