Simulating realistic AI coding assistant parameters (12,000 in / 2,000 out with 60% cache reuse). Fireworks AI — Llama 4 Scout delivers a 92% cost reduction over o3-mini.
| Traffic Volume Tier | o3-mini Monthly | Fireworks AI — Llama 4 Scout Monthly | Monthly Savings by picking Fireworks AI — Llama 4 Scout |
|---|---|---|---|
| 1,000 reqs/mo (Dev/Testing) | $18.04 | $1.512 | Save $16.528 / mo |
| 10,000 reqs/mo (Small App) | $180.40 | $15.12 | Save $165.28 / mo |
| 100,000 reqs/mo (Growth Production) | $1,804.00 | $151.20 | Save $1,652.80 / mo |
| 1,000,000 reqs/mo (Scale SaaS) | $18,040.00 | $1,512.00 | Save $16,528.00 / mo |
Fireworks AI — Llama 4 Scout 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), Fireworks AI — Llama 4 Scout costs $0.001512 per request compared to $0.018 on o3-mini.
o3-mini offers a context window of 200,000 tokens (max output: 100,000), while Fireworks AI — Llama 4 Scout offers 10,000,000 tokens (max output: 16,384).
At 100,000 requests per month, using Fireworks AI — Llama 4 Scout saves $1,652.80 every month (or $19,833.60 annually) compared to o3-mini.
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.