Simulating realistic Agentic workflow parameters (40,000 in / 1,500 out with 65% cache reuse). Llama 4 Scout (109B MoE) delivers a 97% cost reduction over o3 (Reasoning Frontier).
| Traffic Volume Tier | o3 (Reasoning Frontier) Monthly | Llama 4 Scout (109B MoE) Monthly | Monthly Savings by picking Llama 4 Scout (109B MoE) |
|---|---|---|---|
| 1,000 reqs/mo (Dev/Testing) | $226.00 | $6.675 | Save $219.325 / mo |
| 10,000 reqs/mo (Small App) | $2,260.00 | $66.75 | Save $2,193.25 / mo |
| 100,000 reqs/mo (Growth Production) | $22,600.00 | $667.50 | Save $21,932.50 / mo |
| 1,000,000 reqs/mo (Scale SaaS) | $226,000.00 | $6,675.00 | Save $219,325.00 / mo |
Llama 4 Scout (109B MoE) is 97% cheaper for Agentic workflow workloads. At standard Agentic workflow parameter ratios (40,000 input tokens, 1,500 output tokens, 65% cache hit), Llama 4 Scout (109B MoE) costs $0.006675 per request compared to $0.226 on o3 (Reasoning Frontier).
o3 (Reasoning Frontier) offers a context window of 1,050,000 tokens (max output: 128,000), while Llama 4 Scout (109B MoE) offers 10,000,000 tokens (max output: 16,384).
At 100,000 requests per month, using Llama 4 Scout (109B MoE) saves $21,932.50 every month (or $263,190.00 annually) compared to o3 (Reasoning Frontier).
Prompt caching is the single biggest lever — each step re-reads prior context. Summarize or prune tool outputs before appending them to the transcript. Cap the step budget; runaway loops are the #1 surprise on agent invoices.