Frontier Models Are Strong But Document Parsing Is Harder

Recent advancements in frontier models have enabled them to perform tasks previously deemed impossible, achieving near human-expert levels on complex reasoning benchmarks and effectively handling extensive context windows. These models are capable of writing code, analyzing financial models, processing documents, and producing outputs that withstand professional scrutiny. However, when tested on a benchmark consisting of 224 real-world enterprise documents, including invoices, financial reports, and legal contracts, these models showed varying degrees of accuracy, particularly in areas like hallucination rate, table extraction, and document structure. Some models, like Opus 4.6, demonstrated minimal hallucination but struggled with content coverage, while others, such as GPT-5.2 and Gemini 2.5 Pro, achieved higher coverage but at the cost of increased hallucination. These challenges highlight the importance of optimized prompting, post-processing, and output structure enforcement in bridging the gap between raw model capabilities and production-ready document parsing performance. The findings emphasize that while these models are powerful, achieving high accuracy and reliability in real-world applications requires additional configuration and processing layers beyond simple prompts.

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