OCR pipelines beat vision LLMs on long document QA in new benchmark

A rigorous independent benchmark compared vision-capable LLMs against OCR-based pipelines on the MMLongBench-Doc dataset, which consists of 30 image-heavy PDFs with 171 long‑form questions. The best OCR pipeline, built on LlamaCloud’s premium parser, achieved 59.6% accuracy at $0.1885 per query. In contrast, the leading vision LLM (an experimental model referred to as Claude Sonnet 4.5) lagged at 52.0% accuracy and cost $0.2552 per query. Worse, the vision LLM failed on 7% of queries due to file‑size limits, while the OCR pipeline completed every query without error. This finding challenges the prevailing narrative that end‑to‑end vision models are the natural replacement for traditional document processing.

The landscape of document AI is increasingly polarized between lightweight OCR‑plus‑LLM workflows and heavy multimodal models. LlamaCloud, from the company behind LlamaIndex, leads the OCR side with managed parsing that combines layout detection, table extraction, and text recognition. Open‑source alternatives like Marker (by VikParuchuri) convert PDFs to structured Markdown with similar accuracy on tables and code, offering a zero‑cost entry point. On the enterprise side, Microsoft’s Azure AI Document Intelligence provides cloud‑based OCR with prebuilt models for invoices, receipts, and contracts. The benchmark underscores that none of these OCR tools are obsolete: they consistently beat generic vision LLMs on structured document understanding, a fact that the $12.3 billion document AI market (projected by 2026) cannot ignore.

The implications extend far beyond a single benchmark. The cost advantage—$0.19 vs. $0.26 per query—compounds dramatically at scale. Enterprises processing millions of PDFs monthly can save tens of thousands of dollars while achieving higher accuracy. This economic reality may redirect investment away from expensive vision model API calls toward specialized OCR pipelines paired with smaller, cheaper LLMs for downstream reasoning. The result also validates a hybrid architecture: separate the parsing (OCR) from the comprehension (LLM). Until multimodal models can natively handle raw PDF input with long context windows and zero truncation failures, this modular approach remains the production‑grade standard. Moreover, the failure rate due to file‑size limits is a critical flaw for enterprise deployment, where documents routinely exceed token constraints.

Yet the benchmark has important caveats. The sample size of only 30 PDFs (171 questions) limits statistical power despite clear trends. The vision model tested, Claude Sonnet 4.5, does not exist as a public release, raising questions about its representativeness. Newer models with million‑token context windows (e.g., Gemini 1.5 Pro) could narrow the gap, and lighter vision models like Gemini Flash may reduce the cost difference. Latency, a key factor for real‑time applications, was not measured, and OCR pipelines may be slower. Nevertheless, the core insight holds: for long, structured documents, traditional OCR combined with an LLM is currently more reliable and cost‑effective than end‑to‑end vision LLMs. The future likely belongs to hybrid systems that leverage the best of both worlds, not a wholesale replacement of one by the other.