Simulating realistic Document summarization parameters (25,000 in / 600 out with 10% cache reuse). Gemini 2.0 Flash delivers a 35% cost reduction over GPT-4o mini.
| Traffic Volume Tier | Gemini 2.0 Flash Monthly | GPT-4o mini Monthly | Monthly Savings by picking Gemini 2.0 Flash |
|---|---|---|---|
| 1,000 reqs/mo (Dev/Testing) | $2.553 | $3.923 | Save $1.37 / mo |
| 10,000 reqs/mo (Small App) | $25.525 | $39.225 | Save $13.70 / mo |
| 100,000 reqs/mo (Growth Production) | $255.25 | $392.25 | Save $137.00 / mo |
| 1,000,000 reqs/mo (Scale SaaS) | $2,552.50 | $3,922.50 | Save $1,370.00 / mo |
Gemini 2.0 Flash is 35% cheaper for Document summarization workloads. At standard Document summarization parameter ratios (25,000 input tokens, 600 output tokens, 10% cache hit), Gemini 2.0 Flash costs $0.002553 per request compared to $0.003923 on GPT-4o mini.
Gemini 2.0 Flash offers a context window of 1,048,576 tokens (max output: 8,192), while GPT-4o mini offers 128,000 tokens (max output: 16,384).
At 100,000 requests per month, using Gemini 2.0 Flash saves $137.00 every month (or $1,644.00 annually) compared to GPT-4o mini.
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.