Simulating realistic Data extraction & tagging parameters (2,000 in / 250 out with 20% cache reuse). GPT-5.3 Codex delivers a 59% cost reduction over GPT-5.6 Sol.
| Traffic Volume Tier | GPT-5.6 Sol Monthly | GPT-5.3 Codex Monthly | Monthly Savings by picking GPT-5.3 Codex |
|---|---|---|---|
| 1,000 reqs/mo (Dev/Testing) | $15.70 | $6.37 | Save $9.33 / mo |
| 10,000 reqs/mo (Small App) | $157.00 | $63.70 | Save $93.30 / mo |
| 100,000 reqs/mo (Growth Production) | $1,570.00 | $637.00 | Save $933.00 / mo |
| 1,000,000 reqs/mo (Scale SaaS) | $15,700.00 | $6,370.00 | Save $9,330.00 / mo |
GPT-5.3 Codex is 59% cheaper for Data extraction & tagging workloads. At standard Data extraction & tagging parameter ratios (2,000 input tokens, 250 output tokens, 20% cache hit), GPT-5.3 Codex costs $0.00637 per request compared to $0.0157 on GPT-5.6 Sol.
GPT-5.6 Sol offers a context window of 1,050,000 tokens (max output: 128,000), while GPT-5.3 Codex offers 256,000 tokens (max output: 64,000).
At 100,000 requests per month, using GPT-5.3 Codex saves $933.00 every month (or $11,196.00 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.