Simulating realistic Document summarization parameters (25,000 in / 600 out with 10% cache reuse). DeepSeek Coder V2.5 delivers a 81% 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) | $3.353 | $17.59 | Save $14.237 / mo |
| 10,000 reqs/mo (Small App) | $33.53 | $175.90 | Save $142.37 / mo |
| 100,000 reqs/mo (Growth Production) | $335.30 | $1,759.00 | Save $1,423.70 / mo |
| 1,000,000 reqs/mo (Scale SaaS) | $3,353.00 | $17,590.00 | Save $14,237.00 / mo |
DeepSeek Coder V2.5 is 81% cheaper for Document summarization workloads. At standard Document summarization parameter ratios (25,000 input tokens, 600 output tokens, 10% cache hit), DeepSeek Coder V2.5 costs $0.003353 per request compared to $0.0176 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 $1,423.70 every month (or $17,084.40 annually) compared to Kimi K2.7 Code (OpenRouter).
Long-context models pay off here — compare price per 1M tokens at your true document size. Summarize once, store the result; don't re-summarize unchanged documents. For batch backfills, nightly jobs can use cache-friendly request ordering.