Simulating realistic AI coding assistant parameters (12,000 in / 2,000 out with 60% cache reuse). Llama 4 Scout (109B MoE) delivers a 92% cost reduction over Gemini 3.1 Pro.
| Traffic Volume Tier | Llama 4 Scout (109B MoE) Monthly | Gemini 3.1 Pro Monthly | Monthly Savings by picking Llama 4 Scout (109B MoE) |
|---|---|---|---|
| 1,000 reqs/mo (Dev/Testing) | $2.70 | $35.04 | Save $32.34 / mo |
| 10,000 reqs/mo (Small App) | $27.00 | $350.40 | Save $323.40 / mo |
| 100,000 reqs/mo (Growth Production) | $270.00 | $3,504.00 | Save $3,234.00 / mo |
| 1,000,000 reqs/mo (Scale SaaS) | $2,700.00 | $35,040.00 | Save $32,340.00 / mo |
Llama 4 Scout (109B MoE) 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), Llama 4 Scout (109B MoE) costs $0.0027 per request compared to $0.035 on Gemini 3.1 Pro.
Llama 4 Scout (109B MoE) offers a context window of 10,000,000 tokens (max output: 16,384), while Gemini 3.1 Pro offers 2,000,000 tokens (max output: 128,000).
At 100,000 requests per month, using Llama 4 Scout (109B MoE) saves $3,234.00 every month (or $38,808.00 annually) compared to Gemini 3.1 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.