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

May 25, 2026

1:10

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

Here's something that might surprise you — vision-capable large language models aren’t quite ready to replace OCR for complex, chart-heavy documents. Uiqueblhats, writing in AI, benchmarked these models against traditional OCR on 30 long PDFs. Turns out, the native PDF approach, which uses vision-LLMs, scored only around 52% accuracy — pretty underwhelming, especially considering it's the most expensive at over 25 cents per query. But here’s where it gets interesting: vision models struggle with pages packed with charts and tables, where layout extraction from OCR actually performs better. Plus, native PDF methods faced a 7% intrinsic failure rate due to file size issues — failures that retries couldn’t fix. OCR methods, on the other hand, had zero intrinsic failures after retries. As Uiqueblhats points out, while some differences are statistically small, the overall trend favors OCR for complex, image-heavy documents. So the takeaway? For now, OCR still holds the edge — especially in tough, chart-laden terrains — and that’s unlikely to change anytime soon.

I benchmarked vision-capable LLMs (the "just attach the PDF and let the model read it" pattern) against OCR-based pipelines on 30 long, image-heavy PDFs from MMLongBench-Doc (https://github.com/mayubo2333/MMLongBench-Doc). There were 171 questions in total, using Claude Sonnet 4.5 as the LLM.

Post-retry results:

Approach	Accuracy	$/query
LlamaCloud premium + full-context	59.6%	$0.1885
Azure premium + full-context	58.5%	$0.2051
Azure basic + full-context	54.4%	$0.1062
Agentic RAG	53.2%	$0.0827
Native PDF (vision LLM)	52.0%	$0.2552
LlamaCloud basic + full-context	50.9%	$0.1049

Native PDF came 5th of 6 on accuracy and was the most expensive arm at $0.2552 per query.

Two findings:

Vision underperformed on chart-heavy and table-heavy pages, the territory that the "vision LLMs make OCR obsolete" claim most often points to. Premium OCR with layout extraction held up better there.

The native-PDF arm had a 7% intrinsic failure rate (related to PDF file size) that survived retries. There were 27 first-pass failures, with 5 attempts of exponential backoff per failed query. Fifteen recovered, and 12 stayed permanently broken. These were concentrated in two specific PDFs that fail for predictable transport-layer reasons (the blog identifies them). OCR-based arms had a 0% intrinsic failure rate after retries.

Caveats: 30 docs is a small sample. I ran McNemar's pairwise test to determine which gaps are real and which are within noise. Only 3 of 15 head-to-head gaps are statistically distinguishable at α = 0.05, so the order in the table is partly noise. The vision-versus-OCR finding survives the test.

Full writeup: https://www.surfsense.com/blog/agentic-rag-vs-long-context-llms-benchmark

submitted by /u/Uiqueblhats
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