Simulating realistic Document summarization parameters (25,000 in / 600 out with 10% cache reuse). Llama 4 Scout (109B MoE) delivers a 92% cost reduction over GPT-5.3 Codex.
| Traffic Volume Tier | GPT-5.3 Codex Monthly | Llama 4 Scout (109B MoE) Monthly | Monthly Savings by picking Llama 4 Scout (109B MoE) |
|---|---|---|---|
| 1,000 reqs/mo (Dev/Testing) | $48.213 | $4.02 | Save $44.192 / mo |
| 10,000 reqs/mo (Small App) | $482.125 | $40.20 | Save $441.925 / mo |
| 100,000 reqs/mo (Growth Production) | $4,821.25 | $402.00 | Save $4,419.25 / mo |
| 1,000,000 reqs/mo (Scale SaaS) | $48,212.50 | $4,020.00 | Save $44,192.50 / mo |
Llama 4 Scout (109B MoE) is 92% cheaper for Document summarization workloads. At standard Document summarization parameter ratios (25,000 input tokens, 600 output tokens, 10% cache hit), Llama 4 Scout (109B MoE) costs $0.00402 per request compared to $0.0482 on GPT-5.3 Codex.
GPT-5.3 Codex offers a context window of 256,000 tokens (max output: 64,000), 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 $4,419.25 every month (or $53,031.00 annually) compared to GPT-5.3 Codex.
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.