USE CASE · VERIFIED 21 JUL 2026

The cheapest AI API for data extraction and structured output

Pulling structured fields out of messy, unstructured documents — invoices, contracts, resumes, scraped web pages — is one of the most heavily input-skewed workloads we track. You're feeding in a full document and getting back a compact JSON object with a handful of fields. That imbalance changes the cost math more than almost any other use case on this site.

Why input price dominates almost completely here

With a 20:1 input-to-output ratio in this example, the output price barely moves the total — the input rate is doing nearly all the work. This is the mirror image of our content generation guide, where output price dominated instead. For extraction specifically, this means the cheapest input-tier models are worth checking first, before anything else.

Structured output support matters as much as price

Beyond raw cost, extraction work benefits enormously from a provider's native structured output or function-calling support — the ability to define a JSON schema and have the model reliably fill it, rather than parsing free-form text and hoping the format holds. Every major provider we track supports this in some form; the practical difference is how strictly each one enforces the schema versus occasionally producing malformed output you have to catch and retry. This is worth testing directly against your specific document types before committing to a provider on price alone.

Where budget tier genuinely struggles

Extraction accuracy on clean, well-formatted documents (a standard invoice template, a structured form) is usually solid even at budget tier. Accuracy drops faster than expected on messy, inconsistent sources — scanned documents with OCR artifacts, documents in unfamiliar layouts, or fields that require inference rather than direct lookup (e.g., "what's the effective date" when it's implied but not explicitly labeled). If your source documents are genuinely messy, budget the time to validate accuracy on a real sample before assuming the cheapest tier will hold up at scale.

How we'd actually decide

• Clean, consistent document formats, high volume: GPT-4o mini or Gemini 2.5 Flash — tied for cheapest, input-dominated cost favors budget tier heavily here.
• Messy, inconsistent, or scanned source documents: test accuracy on a real sample before committing — the cheapest tier may need more retries than the price difference is worth.
• Either way: verify structured output / schema enforcement on your specific document types, not just the price per token.

Worked example uses standard (non-batch, non-cached) list pricing verified 21 July 2026. Use the calculator with your own volume for an exact estimate.