Cost Calculator
Compare 14 AI models for data extraction. Prices shown at 10K calls/month with default token counts.
Data extraction tasks parse unstructured text into structured output like JSON formats. Input includes the raw document that the model processes for structured data. Output is compact and well-structured, keeping output token costs relatively low overall.
Extraction is input-heavy and output-light (the raw document goes in, structured fields come out), which makes it one of the cheapest tasks per unit of value on this list. Output is small enough that output pricing is nearly irrelevant. Cost tracks document size, so the lever is sending only the pages that contain the fields you want.
Recommendations
Best quality
GPT 5.4
89/100 confidence
Best value
GPT 5.6 Luna
89/100 confidence · $16/mo
Monthly estimates assume 4,000 input / 1,000 output tokens per call. Use the detailed page for custom calculations.
Confidence scores are a third-party benchmark snapshot from week 29 of 2026. This snapshot is 8 weeks old, so treat the scores as directional and validate against your own evals.
Extraction is input-heavy and output-light (the raw document goes in, structured fields come out), which makes it one of the cheapest tasks per unit of value on this list. Output is small enough that output pricing is nearly irrelevant. Cost tracks document size, so the lever is sending only the pages that contain the fields you want. Across the 14 models we track, a typical call uses about 4,000 input and 1,000 output tokens.
GPT 5.6 Luna from GPT is the lowest-cost model we track for data extraction, at roughly $16.00 for 10,000 calls per month. It scores 89 out of 100 on this task type.
GPT 5.4 from GPT ranks highest for data extraction, scoring 89 out of 100. Confidence scores are a third-party benchmark snapshot from week 29 of 2026. This snapshot is 8 weeks old, so treat the scores as directional and validate against your own evals.
These are estimates based on published rates. Track your real spend with the free dashboard.