Simulating realistic Data extraction & tagging parameters (2,000 in / 250 out with 20% cache reuse). Amazon Nova Premier delivers a 66% cost reduction over GPT-5.6 Sol.
| Traffic Volume Tier | Amazon Nova Premier Monthly | GPT-5.6 Sol Monthly | Monthly Savings by picking Amazon Nova Premier |
|---|---|---|---|
| 1,000 reqs/mo (Dev/Testing) | $5.28 | $15.70 | Save $10.42 / mo |
| 10,000 reqs/mo (Small App) | $52.80 | $157.00 | Save $104.20 / mo |
| 100,000 reqs/mo (Growth Production) | $528.00 | $1,570.00 | Save $1,042.00 / mo |
| 1,000,000 reqs/mo (Scale SaaS) | $5,280.00 | $15,700.00 | Save $10,420.00 / mo |
Amazon Nova Premier is 66% cheaper for Data extraction & tagging workloads. At standard Data extraction & tagging parameter ratios (2,000 input tokens, 250 output tokens, 20% cache hit), Amazon Nova Premier costs $0.00528 per request compared to $0.0157 on GPT-5.6 Sol.
Amazon Nova Premier offers a context window of 1,000,000 tokens (max output: 32,768), while GPT-5.6 Sol offers 1,050,000 tokens (max output: 128,000).
At 100,000 requests per month, using Amazon Nova Premier saves $1,042.00 every month (or $12,504.00 annually) compared to GPT-5.6 Sol.
Volume makes nano-tier models attractive — extraction rarely needs frontier reasoning. Constrain output with JSON schema modes to avoid retry loops on malformed responses. Cache shared schema instructions and few-shot examples across calls.