Simulating realistic AI coding assistant parameters (12,000 in / 2,000 out with 60% cache reuse). Kimi K2.7 Code (OpenRouter) delivers a 66% cost reduction over Cohere Command A+.
| Traffic Volume Tier | Cohere Command A+ Monthly | Kimi K2.7 Code (OpenRouter) Monthly | Monthly Savings by picking Kimi K2.7 Code (OpenRouter) |
|---|---|---|---|
| 1,000 reqs/mo (Dev/Testing) | $33.80 | $11.384 | Save $22.416 / mo |
| 10,000 reqs/mo (Small App) | $338.00 | $113.84 | Save $224.16 / mo |
| 100,000 reqs/mo (Growth Production) | $3,380.00 | $1,138.40 | Save $2,241.60 / mo |
| 1,000,000 reqs/mo (Scale SaaS) | $33,800.00 | $11,384.00 | Save $22,416.00 / mo |
Kimi K2.7 Code (OpenRouter) 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), Kimi K2.7 Code (OpenRouter) costs $0.0114 per request compared to $0.0338 on Cohere Command A+.
Cohere Command A+ offers a context window of 128,000 tokens (max output: 8,192), while Kimi K2.7 Code (OpenRouter) offers 262,144 tokens (max output: 235,929).
At 100,000 requests per month, using Kimi K2.7 Code (OpenRouter) saves $2,241.60 every month (or $26,899.20 annually) compared to Cohere Command A+.
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.