Simulating realistic AI coding assistant parameters (12,000 in / 2,000 out with 60% cache reuse). Kimi K2.7 Code (OpenRouter) delivers a 98% cost reduction over GPT-5.5 Pro.
| Traffic Volume Tier | GPT-5.5 Pro Monthly | Kimi K2.7 Code (OpenRouter) Monthly | Monthly Savings by picking Kimi K2.7 Code (OpenRouter) |
|---|---|---|---|
| 1,000 reqs/mo (Dev/Testing) | $525.60 | $11.384 | Save $514.216 / mo |
| 10,000 reqs/mo (Small App) | $5,256.00 | $113.84 | Save $5,142.16 / mo |
| 100,000 reqs/mo (Growth Production) | $52,560.00 | $1,138.40 | Save $51,421.60 / mo |
| 1,000,000 reqs/mo (Scale SaaS) | $525,600.00 | $11,384.00 | Save $514,216.00 / mo |
Kimi K2.7 Code (OpenRouter) is 98% 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 $0.5256 on GPT-5.5 Pro.
GPT-5.5 Pro offers a context window of 512,000 tokens (max output: 64,000), 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 $51,421.60 every month (or $617,059.20 annually) compared to GPT-5.5 Pro.
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.