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  "text": "We’re open sourcing the first document OCR benchmark for the agentic era, ParseBench.\n\nDocument parsing is the foundation of every AI agent that works with real-world files. ParseBench is a benchmark that measures parsing quality specifically for agent knowledge work:\n✅ It optimizes for semantic correctness (instead of exact similarity)\n✅ It has the most comprehensive distribution of real-world enterprise documents\n\nIt contains ~2,000 human-verified enterprise document pages with 167,000+ test rules across five dimensions that matter most: tables, charts, content faithfulness, semantic formatting, and visual grounding.\n\nWe benchmarked 14 known document parsers on ParseBench, from frontier/OSS VLMs to specialized parsers to LlamaParse. Here are some of our findings:\n\n💡 Increasing compute budget yields diminishing returns - Gemini/gpt-5-mini/haiku gain 3-5 points from minimal to high thinking, at 4x the cost.\n\n💡 Charts are the most polarizing dimension for evaluation. Most specialized parsers score below 6%, while some VLM-based parsers do a bit better.\n\n💡 VLMs are great at visual understanding but terrible at layout extraction. GPT-5-mini/haiku score below 10% on our visual grounding task, all specialized parsers do much better.\n\n💡 No method crushes all 5 dimensions at once, but LlamaParse achieves the highest overall score at 84.9%, and is the leader in 4 out of the 5 dimensions.\n\nThis is by far the deepest technical work that we’ve published as a company. I would encourage you to start with our blog and explore our links to Hugging Face to GitHub. All the details are in our full 35-page (!!) ArXiv whitepaper.\n\n🌐: Blog: https://t.co/57OHkx0pQW\n\n📄 Paper: https://t.co/Ho2oH2xEAM\n\n💻 Code: https://t.co/6P7UxqOZYA\n\n📊 Dataset: https://t.co/YguIXWm41j\n\n🎥 YouTube: https://t.co/6Fh1Nsk9ei",
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