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STORY · MULTI-SOURCE·2 sources·SIG 72

Vision-capable LLMs vs. OCR for long-document (including charts, images, tables, etc.) QA [D]

Benchmark sur 30 PDFs longs (171 questions) comparant vision-LLMs natifs vs pipelines OCR pour QA documentaire. Claude Sonnet 4.5 utilisé. LlamaCloud premium atteint 59.6% accuracy ($0.1885/query), vision native 52% ($0.2552/query, plus cher). Vision échoue sur graphiques/tableaux; OCR premium plus robuste. Vision-LLM a 7% taux d'échec intrinsèque vs 0% pour OCR après retries.

VisionBenchmarksRAGClaude

Timeline

  1. 24 May 03:05
    Reddit r/LocalLLaMAVision-capable LLMs vs. OCR for long-document (including charts, images, tables, etc.) QA

    Benchmark on 30 long PDFs (171 questions) comparing vision LLMs vs OCR for document QA. Claude Sonnet 4.5 native PDF: 52% accuracy, $0.2552/query (5th/6). LlamaCloud premium + OCR: 59.6%, $0.1885/query. Vision underperforms on charts/tables; premium OCR more robust. Vision LLM has 7% intrinsic failure rate vs 0% for OCR after retry.

    SIG 72
  2. 24 May 03:11
    Reddit r/MachineLearningVision-capable LLMs vs. OCR for long-document (including charts, images, tables, etc.) QA [D]

    Benchmark on 30 long PDFs (171 questions) comparing native vision-LLMs vs OCR pipelines for document QA. Claude Sonnet 4.5 used. LlamaCloud premium achieves 59.6% accuracy ($0.1885/query), native vision 52% ($0.2552/query, most expensive). Vision underperforms on charts/tables; premium OCR more robust. Vision-LLM has 7% intrinsic failure rate vs 0% for OCR after retries.

    SIG 72

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Vision-capable LLMs vs. OCR for long-document (including charts, images, tables, etc.) QA [D] · Signal IA