OCR benchmarks matter, so in this blog @jerryjliu0 analyzes OlmOCR-Bench, one of the most influential document OCR benchmarks. TLDR: it’s an important step in the right direction, but doesn’t quite cover real-world document parsing needs. 📊 OlmOCR-Bench covers 1400+ PDFs with binary pass-fail tests, but focuses heavily on academic papers (56%) while missing invoices, forms, and financial statements 🔍 The benchmark's unit tests are too coarse for complex tables and reading order, missing merged cells, chart understanding, and global document structure ⚡ Exact string matching in tests creates brittleness where small formatting differences cause failures, even when the extraction is semantically correct 🏗️ Model bias exists since the benchmark uses Sonnet and Gemini to generate test cases, giving advantages to models trained on similar outputs Our preliminary tests show that LlamaParse shines at deep visual reasoning over figures, diagrams, and complex business documents. Read our Jerry's analysis of OCR benchmarking challenges and what next-generation document parsing evaluation should look like: