Simulating realistic Document summarization parameters (25,000 in / 600 out with 10% cache reuse). DeepSeek R1 (Reasoner) delivers a 98% cost reduction over GPT-5.5 Pro.
| Traffic Volume Tier | GPT-5.5 Pro Monthly | DeepSeek R1 (Reasoner) Monthly | Monthly Savings by picking DeepSeek R1 (Reasoner) |
|---|---|---|---|
| 1,000 reqs/mo (Dev/Testing) | $790.50 | $14.039 | Save $776.461 / mo |
| 10,000 reqs/mo (Small App) | $7,905.00 | $140.39 | Save $7,764.61 / mo |
| 100,000 reqs/mo (Growth Production) | $79,050.00 | $1,403.90 | Save $77,646.10 / mo |
| 1,000,000 reqs/mo (Scale SaaS) | $790,500.00 | $14,039.00 | Save $776,461.00 / mo |
DeepSeek R1 (Reasoner) is 98% cheaper for Document summarization workloads. At standard Document summarization parameter ratios (25,000 input tokens, 600 output tokens, 10% cache hit), DeepSeek R1 (Reasoner) costs $0.014 per request compared to $0.7905 on GPT-5.5 Pro.
GPT-5.5 Pro offers a context window of 512,000 tokens (max output: 64,000), while DeepSeek R1 (Reasoner) offers 64,000 tokens (max output: 8,000).
At 100,000 requests per month, using DeepSeek R1 (Reasoner) saves $77,646.10 every month (or $931,753.20 annually) compared to GPT-5.5 Pro.
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.