Simulating realistic Data extraction & tagging parameters (2,000 in / 250 out with 20% cache reuse). Llama 4 Scout (109B MoE) delivers a 93% cost reduction over Gemini 3.1 Pro.
Quick answer
For Data extraction & tagging, Llama 4 Scout (109B MoE) is the lower-cost option at $0.000413 per request versus $0.00628 for Gemini 3.1 Pro, a modeled saving of 93%.
Method & trust
The comparison uses 2,000 input tokens, 250 output tokens, and 20% cache reuse for the selected workload. Pricing is applied per model, then scaled to monthly request volumes.
| 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) | $0.4125 | $6.28 | Save $5.868 / mo |
| 10,000 reqs/mo (Small App) | $4.125 | $62.80 | Save $58.675 / mo |
| 100,000 reqs/mo (Growth Production) | $41.25 | $628.00 | Save $586.75 / mo |
| 1,000,000 reqs/mo (Scale SaaS) | $412.50 | $6,280.00 | Save $5,867.50 / mo |
Llama 4 Scout (109B MoE) is 93% cheaper for Data extraction & tagging workloads. At standard Data extraction & tagging parameter ratios (2,000 input tokens, 250 output tokens, 20% cache hit), Llama 4 Scout (109B MoE) costs $0.000413 per request compared to $0.00628 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 $586.75 every month (or $7,041.00 annually) compared to Gemini 3.1 Pro.
Volume makes nano-tier models attractive — extraction rarely needs frontier reasoning. Constrain output with JSON schema modes to avoid retry loops on malformed responses. Cache shared schema instructions and few-shot examples across calls.