Cost to Extract Data from 1,000 Documents with LLM APIs
Calculate the real cost of using LLM APIs to extract structured data from documents like invoices, receipts, and forms. Compare all models with verified pricing.
⚡ Your Workload
📊 Cost Summary
Cost per documents across 153 models
Show all 153 models in a table
| Model | Provider | Input $/M | Output $/M | Cost for 1K documents |
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How this calculator works
Each document data extraction requires ~5,000 input tokens (the document content — invoice, receipt, form, or contract) and ~500 output tokens (the structured JSON/field data extracted). Input tokens dominate because the model must read the full document. Documents with images require multimodal models, which may have different pricing.
Formula: cost = (input_tokens × input_price_per_Mtok + output_tokens × output_price_per_Mtok) × quantity / 1,000,000
All prices are per million tokens, sourced directly from official provider pricing pages and verified by our automated scraper pipeline that runs 3× daily. No fabricated numbers — every price links to its source.
Frequently asked questions
How much does it cost to extract data from 1,000 documents with an LLM?
Extracting structured data from 1,000 documents costs $3-10 with budget models, $25-50 with mid-tier models, and $100-300+ with frontier models. The cost is dominated by input tokens since the model must read each full document.
Which LLM is best for document data extraction?
For text-based documents, DeepSeek V3 and Gemini Flash offer the lowest cost. For scanned documents or images, you need a multimodal model (GPT-4.1, Gemini Pro, Claude with vision). Models with prompt caching can significantly reduce costs if you process many similar documents.
How are data extraction token costs calculated?
Each document uses ~5,000 input tokens (document text) and ~500 output tokens (extracted data). Total cost = (input_tokens × input_price + output_tokens × output_price) × number_of_documents. Prices are per million tokens, verified from official sources.