Simulating realistic Document summarization parameters (25,000 in / 600 out with 10% cache reuse). Kimi K2.7 Code (OpenRouter) delivers a 97% cost reduction over o3-pro (Frontier Reasoning).
| Traffic Volume Tier | o3-pro (Frontier Reasoning) Monthly | Kimi K2.7 Code (OpenRouter) Monthly | Monthly Savings by picking Kimi K2.7 Code (OpenRouter) |
|---|---|---|---|
| 1,000 reqs/mo (Dev/Testing) | $503.00 | $17.59 | Save $485.41 / mo |
| 10,000 reqs/mo (Small App) | $5,030.00 | $175.90 | Save $4,854.10 / mo |
| 100,000 reqs/mo (Growth Production) | $50,300.00 | $1,759.00 | Save $48,541.00 / mo |
| 1,000,000 reqs/mo (Scale SaaS) | $503,000.00 | $17,590.00 | Save $485,410.00 / mo |
Kimi K2.7 Code (OpenRouter) is 97% cheaper for Document summarization workloads. At standard Document summarization parameter ratios (25,000 input tokens, 600 output tokens, 10% cache hit), Kimi K2.7 Code (OpenRouter) costs $0.0176 per request compared to $0.503 on o3-pro (Frontier Reasoning).
o3-pro (Frontier Reasoning) offers a context window of 1,000,000 tokens (max output: 128,000), 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 $48,541.00 every month (or $582,492.00 annually) compared to o3-pro (Frontier Reasoning).
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.