Simulating realistic AI coding assistant parameters (12,000 in / 2,000 out with 60% cache reuse). Llama 4 Scout (109B MoE) delivers a 90% cost reduction over Gemini 2.5 Pro.
| Traffic Volume Tier | Gemini 2.5 Pro Monthly | Llama 4 Scout (109B MoE) Monthly | Monthly Savings by picking Llama 4 Scout (109B MoE) |
|---|---|---|---|
| 1,000 reqs/mo (Dev/Testing) | $28.25 | $2.70 | Save $25.55 / mo |
| 10,000 reqs/mo (Small App) | $282.50 | $27.00 | Save $255.50 / mo |
| 100,000 reqs/mo (Growth Production) | $2,825.00 | $270.00 | Save $2,555.00 / mo |
| 1,000,000 reqs/mo (Scale SaaS) | $28,250.00 | $2,700.00 | Save $25,550.00 / mo |
Llama 4 Scout (109B MoE) is 90% cheaper for AI coding assistant workloads. At standard AI coding assistant parameter ratios (12,000 input tokens, 2,000 output tokens, 60% cache hit), Llama 4 Scout (109B MoE) costs $0.0027 per request compared to $0.0282 on Gemini 2.5 Pro.
Gemini 2.5 Pro offers a context window of 1,000,000 tokens (max output: 32,768), 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 $2,555.00 every month (or $30,660.00 annually) compared to Gemini 2.5 Pro.
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.