Simulating realistic Data extraction & tagging parameters (2,000 in / 250 out with 20% cache reuse). Claude Sonnet 5 delivers a 9% cost reduction over GPT-5.3 Codex.
| Traffic Volume Tier | GPT-5.3 Codex Monthly | Claude Sonnet 5 Monthly | Monthly Savings by picking Claude Sonnet 5 |
|---|---|---|---|
| 1,000 reqs/mo (Dev/Testing) | $6.37 | $5.78 | Save $0.59 / mo |
| 10,000 reqs/mo (Small App) | $63.70 | $57.80 | Save $5.90 / mo |
| 100,000 reqs/mo (Growth Production) | $637.00 | $578.00 | Save $59.00 / mo |
| 1,000,000 reqs/mo (Scale SaaS) | $6,370.00 | $5,780.00 | Save $590.00 / mo |
Claude Sonnet 5 is 9% cheaper for Data extraction & tagging workloads. At standard Data extraction & tagging parameter ratios (2,000 input tokens, 250 output tokens, 20% cache hit), Claude Sonnet 5 costs $0.00578 per request compared to $0.00637 on GPT-5.3 Codex.
GPT-5.3 Codex offers a context window of 256,000 tokens (max output: 64,000), while Claude Sonnet 5 offers 1,000,000 tokens (max output: 128,000).
At 100,000 requests per month, using Claude Sonnet 5 saves $59.00 every month (or $708.00 annually) compared to GPT-5.3 Codex.
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.