Simulating realistic AI coding assistant parameters (12,000 in / 2,000 out with 60% cache reuse). Llama 4 Maverick (400B MoE) delivers a 67% cost reduction over Grok 4.5.
| Traffic Volume Tier | Llama 4 Maverick (400B MoE) Monthly | Grok 4.5 Monthly | Monthly Savings by picking Llama 4 Maverick (400B MoE) |
|---|---|---|---|
| 1,000 reqs/mo (Dev/Testing) | $7.90 | $23.76 | Save $15.86 / mo |
| 10,000 reqs/mo (Small App) | $79.00 | $237.60 | Save $158.60 / mo |
| 100,000 reqs/mo (Growth Production) | $790.00 | $2,376.00 | Save $1,586.00 / mo |
| 1,000,000 reqs/mo (Scale SaaS) | $7,900.00 | $23,760.00 | Save $15,860.00 / mo |
Llama 4 Maverick (400B MoE) is 67% 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 Maverick (400B MoE) costs $0.0079 per request compared to $0.0238 on Grok 4.5.
Llama 4 Maverick (400B MoE) offers a context window of 1,000,000 tokens (max output: 16,384), while Grok 4.5 offers 500,000 tokens (max output: 64,000).
At 100,000 requests per month, using Llama 4 Maverick (400B MoE) saves $1,586.00 every month (or $19,032.00 annually) compared to Grok 4.5.
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.