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Vision-language models evaluated for document extraction, revealing trade-offs

By PulseAugur Editorial · [1 sources] ·

A new research paper explores the trade-offs involved in using vision-language models (VLMs) for extracting structured data from business documents. The study evaluated eleven systems, including commercial offerings like GPT-5 and open-source models such as Claude Sonnet 4.5, on a dataset of synthetic checks. Fine-tuning open-source VLMs significantly improved their performance, surpassing commercial systems in some cases, while GPT-5 led in overall accuracy and Claude Sonnet 4.5 struggled with date extraction. The research also introduces a framework to help practitioners select the most suitable approach based on factors like quality, latency, governance, and volume. AI

IMPACT Provides practical guidance for selecting VLMs in document extraction, highlighting performance and cost trade-offs.

RANK_REASON The cluster contains a research paper detailing an evaluation of vision-language models for a specific task. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Vision-language models evaluated for document extraction, revealing trade-offs

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The cluster contains a research paper detailing an evaluation of vision-language models for a specific task. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Kushal Patel, Pushkal Shrivastava, Mackenzie Lees, Qirui Lu, Bhargobjyoti Saikia, Liying Li, Junlin Jiang ·

    Beyond Accuracy: Robustness, Cost, and Governance Trade-offs for Vision-Language Models in Templated Document Extraction

    arXiv:2609.15706v1 Announce Type: new Abstract: Vision-language models (VLMs) are increasingly used to extract structured fields from business documents, yet most evaluations report accuracy on clean benchmarks and offer little guidance to practitioners choosing an approach for a…

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