Simulating realistic AI coding assistant parameters (12,000 in / 2,000 out with 60% cache reuse). MiniMax-01 (4M Context) delivers a 66% cost reduction over Grok Build 0.1.
| Traffic Volume Tier | Grok Build 0.1 Monthly | MiniMax-01 (4M Context) Monthly | Monthly Savings by picking MiniMax-01 (4M Context) |
|---|---|---|---|
| 1,000 reqs/mo (Dev/Testing) | $10.24 | $3.448 | Save $6.792 / mo |
| 10,000 reqs/mo (Small App) | $102.40 | $34.48 | Save $67.92 / mo |
| 100,000 reqs/mo (Growth Production) | $1,024.00 | $344.80 | Save $679.20 / mo |
| 1,000,000 reqs/mo (Scale SaaS) | $10,240.00 | $3,448.00 | Save $6,792.00 / mo |
MiniMax-01 (4M Context) is 66% cheaper for AI coding assistant workloads. At standard AI coding assistant parameter ratios (12,000 input tokens, 2,000 output tokens, 60% cache hit), MiniMax-01 (4M Context) costs $0.003448 per request compared to $0.0102 on Grok Build 0.1.
Grok Build 0.1 offers a context window of 256,000 tokens (max output: 32,768), while MiniMax-01 (4M Context) offers 4,000,000 tokens (max output: 64,000).
At 100,000 requests per month, using MiniMax-01 (4M Context) saves $679.20 every month (or $8,150.40 annually) compared to Grok Build 0.1.
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.