Simulating realistic AI coding assistant parameters (12,000 in / 2,000 out with 60% cache reuse). Groq LPU — Llama 4 Scout delivers a 93% cost reduction over Pixtral Large.
| Traffic Volume Tier | Pixtral Large Monthly | Groq LPU — Llama 4 Scout Monthly | Monthly Savings by picking Groq LPU — Llama 4 Scout |
|---|---|---|---|
| 1,000 reqs/mo (Dev/Testing) | $23.04 | $1.604 | Save $21.436 / mo |
| 10,000 reqs/mo (Small App) | $230.40 | $16.04 | Save $214.36 / mo |
| 100,000 reqs/mo (Growth Production) | $2,304.00 | $160.40 | Save $2,143.60 / mo |
| 1,000,000 reqs/mo (Scale SaaS) | $23,040.00 | $1,604.00 | Save $21,436.00 / mo |
Groq LPU — Llama 4 Scout is 93% cheaper for AI coding assistant workloads. At standard AI coding assistant parameter ratios (12,000 input tokens, 2,000 output tokens, 60% cache hit), Groq LPU — Llama 4 Scout costs $0.001604 per request compared to $0.023 on Pixtral Large.
Pixtral Large offers a context window of 128,000 tokens (max output: 8,192), while Groq LPU — Llama 4 Scout offers 512,000 tokens (max output: 16,384).
At 100,000 requests per month, using Groq LPU — Llama 4 Scout saves $2,143.60 every month (or $25,723.20 annually) compared to Pixtral Large.
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.