Simulating realistic AI coding assistant parameters (12,000 in / 2,000 out with 60% cache reuse). MiniMax M2.7 (OpenRouter) delivers a 89% cost reduction over AI21 Jamba 1.5 Large.
| Traffic Volume Tier | AI21 Jamba 1.5 Large Monthly | MiniMax M2.7 (OpenRouter) Monthly | Monthly Savings by picking MiniMax M2.7 (OpenRouter) |
|---|---|---|---|
| 1,000 reqs/mo (Dev/Testing) | $40.00 | $4.272 | Save $35.728 / mo |
| 10,000 reqs/mo (Small App) | $400.00 | $42.72 | Save $357.28 / mo |
| 100,000 reqs/mo (Growth Production) | $4,000.00 | $427.20 | Save $3,572.80 / mo |
| 1,000,000 reqs/mo (Scale SaaS) | $40,000.00 | $4,272.00 | Save $35,728.00 / mo |
MiniMax M2.7 (OpenRouter) is 89% 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 M2.7 (OpenRouter) costs $0.004272 per request compared to $0.04 on AI21 Jamba 1.5 Large.
AI21 Jamba 1.5 Large offers a context window of 256,000 tokens (max output: 4,096), while MiniMax M2.7 (OpenRouter) offers 204,800 tokens (max output: 131,072).
At 100,000 requests per month, using MiniMax M2.7 (OpenRouter) saves $3,572.80 every month (or $42,873.60 annually) compared to AI21 Jamba 1.5 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.