A developer spent May testing 14 OCR engines across 93 diverse documents to determine whether expensive APIs justify their cost. The experiment included handwritten notes, financial documents, scanned invoices, and legacy reports to simulate real-world business document processing scenarios where accuracy matters most.

For clean documents, Tesseract remained the top choice as a free and fast solution. Gemini Flash emerged as the best all-rounder for mixed production documents, while Mistral OCR offered better value for structured table extraction. Specialist models performed well within their domain but struggled with unfamiliar documents.

The evaluation revealed no single best OCR engine exists - the solution requires routing based on document type. Companies should avoid paying premium prices for structured OCR when basic extraction suffices. Classify documents, test engines on your specific data, and route based on cost, accuracy, and failure tolerance requirements.