OCR Bench Blog
Notes on benchmarking LLMs for document extraction
Guides and deep-dives on benchmarking LLMs for OCR and field-value extraction — accuracy, cost, and latency, measured on your own documents.
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CER vs field-level accuracy: why character error rate misleads on structured extraction
Character error rate is the classic OCR metric — and the wrong one for structured field extraction. Play with a live invoice, watch CER and field-level accuracy disagree, and see why one maps to business impact and the other doesn't.
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LLM OCR pricing: a calculator for your real cost per document
Token-based pricing makes LLM document extraction hard to budget. This interactive calculator turns input/output tokens and per-million prices into a real cost per document, per 1,000 documents, and per year — with the exact formula shown.
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How to choose an OCR and document-extraction model in 2026
Traditional OCR, vision LLMs, or a specialized document-AI API? A practical, vendor-neutral framework for picking the model that reads your documents — scored on accuracy, cost, and latency, not on someone else's leaderboard.
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How OCR Bench scores field extraction: correct, wrong, missing, hallucinated
Diffing strings is a terrible way to grade an LLM. Here's the normalization-and-comparison engine OCR Bench uses to turn raw model output into a fair, per-field verdict — with dates, currency, and numbers handled properly.
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Introducing OCR Bench: know which model reads your documents best
Stop picking document-extraction models by vibes. OCR Bench runs every model that matters over your own documents and ranks them on accuracy, cost, and latency — one scorecard, real numbers.