Data extraction cost calculator

Estimate the AI data extraction cost of pulling structured fields — vendor, date, total, line items, and more — out of invoices, receipts, resumes, contracts, forms, and other business documents with a general-purpose model. Enter your document volume, schema, and field count to get a Low / Likely / High monthly cost estimate. Everything runs locally in your browser — no data is sent to any server.

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Configure your extraction workload

Estimated monthly cost

T9 estimates LLM inference/token cost only. Not included: OCR vendor fees, PDF/image parsing, document-storage fees, vector databases, dedicated parser/extraction-API services, or any other third-party fee — those are billed separately (often per page, not per token) by whatever service converts your source file into processable text.

Likely monthly cost$0.8190Estimated range: $0.4878$1.24
Cost per document
Low$0.001626Likely$0.002730High$0.004132
Input tokens / document
Low1.8KLikely3.0KHigh4.4K
Output tokens / document
Low68Likely115High185
Total monthly tokens
Low548KLikely920KHigh1.4M

This is an estimate, not a billing guarantee. Actual costs depend on your provider's exact tokeniser, your real document content and schema, and your billing tier.

Understand & trust these numbers Methodology What are AI tokens? Why bills surprise you Document summary calculator

How this estimate works

Input — you send
  • Extraction instructions / schema
  • The document's own text content
Model

Reads the document and returns the requested fields as structured data (JSON).

Output — you get

A small, structured result — the extracted field values, not more prose.

Data extraction is a lopsided workload: the document you send is usually much larger than what comes back. This calculator prices both sides of that call using each model's own input and output rates — which are commonly quite different from each other.

Why document size drives input cost

The full text of the document — every page, every line — is sent as input on every extraction call. A one-page receipt and a forty-page contract cost very differently on the input side, even with an identical extraction schema.

Why schema/instruction size matters

A short list of simple fields ("vendor, date, total") takes far fewer instruction tokens to specify than a large or nested schema with type rules, format examples, and edge-case notes — and that instruction text is resent on every single call, the same as any other system prompt.

Why line items/records affect output tokens

A resume has one record — one set of fields. An invoice usually has several — one set of fields per line item. Output size scales with fields × records, not with document length: a short invoice with 20 line items can produce more output tokens than a long contract with only a handful of top-level fields.

Why model choice changes cost

Extraction is an input-heavy, output-light workload, so a model's input price usually matters more here than its output price — the opposite emphasis from a chat or content-generation workload. This calculator applies each model's own current input and output rate to get the balance right.

Why long documents can trigger long-context pricing

Some models charge a higher rate once a single request's input tokens cross a documented threshold. A long contract or a bundle of scanned pages can cross that threshold on its own, even with a simple schema — this calculator applies each model's own verified threshold and higher rate automatically when a document does.

What's not included: getting to processable text

T9 estimates LLM inference/token cost only. Not included: OCR vendor fees, PDF/image parsing, document-storage fees, vector databases, dedicated parser/ extraction-API services, or any other third-party fee.

Those steps are billed separately — often per page, not per token — by whatever OCR or document-conversion service turns your source file (a scanned PDF, a photo of a receipt) into text a model can read. This estimate begins once that text already exists.

Low / Likely / High explained

Low

A short document, a minimal schema, few fields and records.

Likely

A typical document, standard schema, and typical field/record count. Plan against this number.

High

A long document, a detailed schema, many fields and records. Budget against this.

Cut your cost

1
Trim the document before extraction. Send only the pages or sections that actually contain the fields you need.
2
Keep the schema tight. Extract only the fields you'll actually use — every field adds a little output on every document, every month.
3
Match model to task. Routine, well-structured documents rarely need a frontier-tier model.

Example monthly data extraction costs

These worked examples use the same default assumptions as the calculator above — a medium-length document, standard schema, 10 fields, 1 record per document — with GPT-5.4 Mini. Only the number of documents per month changes between rows. Use the calculator above to model your own usage or a different model.

Usage levelDocuments / monthLowLikelyHigh
Light usage 100 $0.1626 $0.2730 $0.4133
Typical usage 300 $0.4878 $0.8190 $1.24
Heavy usage 2,000 $3.25 $5.46 $8.27

And here's the per-document cost for three realistic document types — a simple form, an invoice with several line items, and a long contract — with GPT-5.4 Mini, each with its own document size, schema complexity, and field/record count:

Document typeLowLikelyHigh
Simple form $0.000501 $0.000855 $0.001365
Invoice (with line items) $0.004344 $0.007343 $0.0116
Long contract $0.004851 $0.007417 $0.0112

Caveats

  • These estimates use the same reference document-length and instruction-prompt token ranges as the site's document summary calculator, accurate to ±10–15% for plain English text.
  • Per-field and per-record output-token figures are this project's own descriptive assumption, not a provider-published figure — no AI provider publishes a token-overhead spec for structured extraction, since it depends entirely on the schema and the field values themselves (a short code is cheaper than a long free-text note). Real output size varies by schema; use the calculator's field/record inputs to match your own schema as closely as possible.
  • Each provider uses a different tokeniser. Actual token counts may differ ±5–10% between providers for the same content.
  • Getting from a source file to processable text is not priced here. OCR vendor fees, PDF/image parsing, document-storage fees, vector databases, and dedicated parser/extraction-API services (often billed per page) are not included — this tool only estimates the language model's own input/output token cost once that text already exists.
  • Prices shown are manually verified against each provider's official pricing page as of 2026-09-08. AI provider pricing changes frequently — verify at your provider's official pricing page before making budget decisions.
  • The long-context pricing tier, where a selected model has one, is resolved against each document's own input size, never a monthly aggregate — a single long document can trip the higher rate even when most of your documents don't.
  • The context-window warning above appears when a document's combined tokens exceed 80% of the selected model's context window — a safety margin before truncation or failure risk, not a hard limit at 100%.

This is an estimate, not a billing guarantee. Always confirm against your provider's usage dashboard and official pricing before committing a budget.