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AI Document Processing Cost Calculator

Estimate text-token API cost for summarising, extracting, classifying, or reviewing document collections.

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Inputs

PricingChoose a model or enter custom rates
DocumentsEstimate input size
OutputEstimate generated response size
Results update automatically

Results

Result Enter values to calculate.
Your result explanation will appear here.

Useful next checks

  • Check the inputs before relying on the result.
  • Try a second scenario to compare outcomes.
  • Read the guide below for context.

# AI Document Processing Cost Calculator

Use this calculator to estimate AI text-token cost for document workflows such as summarisation, extraction, classification, review, due diligence, and knowledge-base preparation.

How It Works

You can estimate document input size from pages, words, or manual token counts:

  • Page mode = average pages x words per page x about 1.33 tokens per word
  • Word mode = average words x about 1.33 tokens per word
  • Manual mode = your known token count per document

The calculator adds prompt overhead, estimates output tokens, then multiplies cost per document by document count.

Prompt Overhead

Prompt overhead should include instructions, formatting rules, extraction schema, examples, guardrails, and repeated context that is sent with every document.

When To Use Actual Token Counts

Use manual token counts when your pipeline already measures token usage or when documents have unusual structure, such as tables, code, legal numbering, OCR artifacts, or repeated boilerplate.

Limitations

This calculator estimates text-token API cost only. It does not include OCR, file parsing, storage, retrieval, embedding, tools, cache discounts, batch discounts, taxes, or account-specific discounts.

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Frequently asked questions

Does this include OCR or file parsing?

No. This calculator estimates text-token API costs after document text is available. OCR, parsing, storage, and file conversion may add separate costs.

How are pages converted to tokens?

Pages are multiplied by your words-per-page estimate, then converted using the rough rule of about 1.33 tokens per word.

When should I use manual tokens?

Use manual tokens when your pipeline or provider already reports exact token counts for representative documents.