Mistral OCR 4.1

The broad answer was “it depends sharply on the document.” People working with difficult source material like Fraktur, ligatures, critical editions, handwriting, and messy scans said no current model is consistently great. Several said OpenAI’s higher-end models or Claude still do better on raw transcription quality for their cases, especially handwriting, while Mistral can miss badly enough to invent sentences. Others reported the opposite on practical enterprise inputs like handwritten email addresses, old typeset pages, mixed typewriter and handwriting scans, and layout-heavy business documents. The recurring theme was not that Mistral won outright, but that OCR performance is weirdly task-specific and a single benchmark is close to useless. Price triggered almost as much reaction as accuracy. A lot of people called €3.5 per 1,000 pages expensive relative to Tesseract, Google Document AI, AWS Textract, Azure Document Intelligence, or local GPU pipelines. That pushback ran into a more pragmatic point from people who have actually built document systems: plain OCR text extraction is not the hard part. Preserving reading order across multi-column pages, detecting tables and figures, handling equations, returning bounding boxes for grounding, and surviving bad scans are where specialized models save real engineering time. For teams processing high volumes, the practical architecture looked less like “pick one best OCR” and more like tiered routing. Use a cheap fast model for the easy majority, detect low-confidence cases, then escalate to a stronger or different system and reconcile outputs when accuracy really matters. Trust was the other big issue. People were uneasy for two different reasons. General vision-language models can refuse, censor, or otherwise alter sensitive legal and clinical documents in ways that are hard to detect. OCR-only systems are less likely to refuse, but they can still hallucinate. That leaves teams building their own validation layers, cross-checking multiple providers, comparing against extracted plain text when available, and in some cases verifying quoted text back against the source image. The upshot was blunt: OCR is now good enough that throughput and convenience are no longer the whole story. The hard problem is knowing when not to trust the output.