Simulating realistic Document summarization parameters (25,000 in / 600 out with 10% cache reuse). Fireworks AI — Llama 4 Scout delivers a 96% cost reduction over Cohere Command R+.
| Traffic Volume Tier | Cohere Command R+ Monthly | Fireworks AI — Llama 4 Scout Monthly | Monthly Savings by picking Fireworks AI — Llama 4 Scout |
|---|---|---|---|
| 1,000 reqs/mo (Dev/Testing) | $68.50 | $2.991 | Save $65.509 / mo |
| 10,000 reqs/mo (Small App) | $685.00 | $29.91 | Save $655.09 / mo |
| 100,000 reqs/mo (Growth Production) | $6,850.00 | $299.10 | Save $6,550.90 / mo |
| 1,000,000 reqs/mo (Scale SaaS) | $68,500.00 | $2,991.00 | Save $65,509.00 / mo |
Fireworks AI — Llama 4 Scout is 96% cheaper for Document summarization workloads. At standard Document summarization parameter ratios (25,000 input tokens, 600 output tokens, 10% cache hit), Fireworks AI — Llama 4 Scout costs $0.002991 per request compared to $0.0685 on Cohere Command R+.
Cohere Command R+ offers a context window of 128,000 tokens (max output: 4,096), 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 $6,550.90 every month (or $78,610.80 annually) compared to Cohere Command R+.
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.