LlamaIndex Launches ExtractBench: A New Benchmark for Enterprise Document Extraction
llama_index · x · 2026-08-11
LlamaIndex has introduced ExtractBench, a comprehensive benchmark designed to evaluate information extraction from complex enterprise documents.
Key Findings
- Long Document Collapse: When processing documents past 50 pages, commercial VLMs see recall drop below 35%. While precision remains high, models silently drop most table rows.
- Shortcomings of Existing Benchmarks: Previous benchmarks lacked diversity in document domains (finance, energy, gov, etc.), elements (long records, scans), and schemas.
Evaluation Scale
- Tested 14 systems, including frontier VLMs, coding agents, and extraction APIs.
- Evaluated against 370 enterprise docs, totaling 4,869 pages across 67 doc types.
- Fully deterministic evaluation with zero LLM judges.
Related event: LlamaIndex Introduces ExtractBench for Enterprise Document Extraction(2 posts)→
More from Models
- Where Do Quantized Local LLMs Break? Reddit Users Share Experiences — d77chong · 2026-08-12
- Unreleased Anthropic Model Makes Surprising Progress on the Riemann Hypothesis — TechCrunch AI · 2026-08-12
- Frontier Models' Biggest Bottleneck is 'Lack of Self-Esteem' in Math Research — coherence · 2026-08-12
- Pokee-Isaac 28B Beats Meta and Qwen Rivals in Sub-30B Agent Benchmarks — Kyrannio · 2026-08-12
- Post-Trained NVIDIA Nemotron Beats Claude Opus in Legal Agent Tasks — ctnzr · 2026-08-12
- New LLM Jailbreak Trick: Just Tell It to "Be Smarter Than Grok" — npinto · 2026-08-12