Invoice extraction
Which AI model delivers the best cost-accuracy balance? A practical guide to benchmarking for high-volume invoice processing.
Read article
Document Benchmarking
Compare vision models on
document extraction
Upload documents, define extraction schemas, and benchmark 50+ vision-capable LLMs. Get accuracy scores, cost breakdowns, and visual comparisons to find the best model for your use case.
## What is document benchmarking?
Systematic comparison of AI models on structured data extraction
Document benchmarking lets you objectively compare how different AI models extract structured data from your documents. Upload real documents, define what data you want to extract using a JSON schema, and let the system run extractions across multiple models simultaneously.
Ground truth accuracy
Compare extracted data against your ground truth to get precise accuracy scores. Know exactly which model extracts most accurately.
50+ models compared
Test GPT-4V, Claude, Gemini, and many others side-by-side. See which models work best for your specific document types and extraction needs.
Visual diff viewer
See exactly where models differ with side-by-side comparison. Highlighted differences help you understand model behavior and choose the best one.
Cost & performance analysis
Track per-document costs, token usage, and latency. Find the optimal balance between accuracy and cost for your production needs.
## How it works
A simple workflow for document extraction benchmarking
1
Upload documents
Upload PDFs, images, or scanned documents. The system supports any format that vision models can process. You can upload multiple documents to test model consistency.
2
Define your schema
Create a JSON schema that describes exactly what data you want to extract. Define field types, required fields, nested structures, and validation rules. The schema guides extraction and enables accuracy scoring.
3
Add ground truth (optional)
For accurate accuracy scoring, provide ground truth data—the expected extracted values for each document. This enables precise comparison and helps identify which models extract most accurately.
4
Select models & run benchmark
Choose which AI models to benchmark. You can select from 50+ vision-capable models including GPT-4V, Claude, Gemini, and many others. Run extractions in parallel and compare results.
5
Analyze results
View accuracy scores, cost breakdowns, and visual diffs. The leaderboard ranks models by accuracy, cost, and speed. Use the diff viewer to see exactly where models differ.
## Key features
Everything you need for comprehensive model comparison
Accuracy scoring
Ground truth comparison with weighted field scoring. Get precise accuracy metrics per model and per field.
Custom schemas
Define any JSON schema structure. Support for nested objects, arrays, and complex data types.
Visual diffs
Side-by-side comparison with highlighted differences. See exactly where models extract differently.
Cost tracking
Per-document cost breakdowns. Track tokens, latency, and USD costs to optimize for value.
Leaderboards
Rank models by accuracy, cost, speed, or combined metrics. Find the best model for your needs.
Project management
Organize benchmarks by use case. Track history, compare runs, and manage multiple projects.
Schema validation
Automatic schema compliance checking. Validate extracted data structure and completeness.
Parallel processing
Run extractions across multiple models simultaneously. Get results faster with efficient parallel execution.
Export results
Export benchmark results, accuracy scores, and extracted data in JSON or CSV format for further analysis.
## Use cases
Extract structured data from any document type
Invoices & receipts
Extract line items, totals, tax breakdowns, vendor details, and payment terms from invoices and receipts.
Contracts
Extract parties, terms, dates, obligations, and key clauses from legal contracts and agreements.
Medical records
Extract patient data, diagnoses, treatments, lab results, and medication information from medical documents.
Real estate docs
Extract property details, valuations, certificates, permits, and ownership information from real estate documents.
Insurance claims
Extract policy numbers, damages, assessments, payouts, and claim details from insurance documents.
Legal filings
Extract case numbers, parties, rulings, citations, and legal references from court documents and filings.
## Real-world examples
See how teams use document benchmarking in production
Medical records extraction
Achieving 99%+ accuracy with AI. A practical guide where extraction errors have real consequences.
Read articleContract analysis
Finding the right AI model for legal documents, with a focus on consistency across document complexity.
Read articleResume screening
Comparing AI models for fair and accurate CV parsing, with a focus on accuracy, speed, and demographic fairness.
Read articleClaims processing
Speeding up insurance workflows with AI. Balancing speed, accuracy, and fraud detection.
Read articleProperty documents
Automating due diligence with AI extraction from energy certificates, appraisals, and property documents.
Read articleReady to benchmark your documents?
Upload documents, define schemas, and compare 50+ vision models to find the best one for your extraction needs.