Simulating realistic AI coding assistant parameters (12,000 in / 2,000 out with 60% cache reuse). DeepSeek Coder V2.5 delivers a 88% cost reduction over Kimi K2.7 Code (OpenRouter).
| Traffic Volume Tier | DeepSeek Coder V2.5 Monthly | Kimi K2.7 Code (OpenRouter) Monthly | Monthly Savings by picking DeepSeek Coder V2.5 |
|---|---|---|---|
| 1,000 reqs/mo (Dev/Testing) | $1.333 | $11.384 | Save $10.051 / mo |
| 10,000 reqs/mo (Small App) | $13.328 | $113.84 | Save $100.512 / mo |
| 100,000 reqs/mo (Growth Production) | $133.28 | $1,138.40 | Save $1,005.12 / mo |
| 1,000,000 reqs/mo (Scale SaaS) | $1,332.80 | $11,384.00 | Save $10,051.20 / mo |
DeepSeek Coder V2.5 is 88% cheaper for AI coding assistant workloads. At standard AI coding assistant parameter ratios (12,000 input tokens, 2,000 output tokens, 60% cache hit), DeepSeek Coder V2.5 costs $0.001333 per request compared to $0.0114 on Kimi K2.7 Code (OpenRouter).
DeepSeek Coder V2.5 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 DeepSeek Coder V2.5 saves $1,005.12 every month (or $12,061.44 annually) compared to Kimi K2.7 Code (OpenRouter).
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.