Best OCR Models

Purpose-built OCR systems, from classic engines to OCR-tuned models like GLM-OCR, are optimized for one job: reading text accurately and cheaply at volume. They tend to win on cost per page, throughput, and consistency for well-understood document types, and specialist models increasingly handle tables, formulas, and layout structure that used to require separate systems.

Choose this family when you process documents at scale (invoices, receipts, archives), the format is broadly predictable, and cost per page matters more than open-ended flexibility.

Frontier VLMs read text as one part of understanding the whole page: they can transcribe, then also summarize, answer questions, or return the content already structured as JSON or markdown from a single prompt. That flexibility comes at a higher price per page and more latency, and on straightforward transcription a dedicated engine is usually cheaper for the same accuracy.

Choose a VLM when the extraction is complicated (mixed layouts, judgment calls, follow-up questions), when you want structured output without building a parsing pipeline, or when volumes are small enough that flexibility beats unit cost.

Teams processing documents at scale often route pages: a fast, cheap OCR model handles the bulk, and hard pages (poor scans, handwriting, unusual layouts) escalate to a stronger VLM. Confidence scores or simple heuristics decide the split, keeping average cost low without giving up accuracy on the tail.