Vision Language Models (VLMs) like Qwen3-VL represent a major leap forward in AI capabilities by combining visual and textual understanding. Unlike traditional approaches that rely on OCR followed by an LLM, VLMs process images and text together, enabling more accurate and context-aware analysis of documents and visual content. One of the key advantages of VLMs is their ability to understand spatial relationships. For example, when dealing with forms that include checkboxes, the position of a checkmark relative to a text label is crucial. OCR alone cannot capture this relationship, as it only extracts raw text without context. A VLM, however, can see that a checkmark is next to a specific line of text and infer relevance accordingly. In testing, Qwen3-VL correctly identified which documents were checked by analyzing both the image and text layout. Another powerful use case is OCR. Qwen3-VL performs highly accurate text extraction from images. When tested on a municipal planning document from Oslo, it successfully captured all visible text, including dates, addresses, scales, and metadata, with no omissions. This demonstrates that VLMs can serve as robust OCR tools, often outperforming traditional OCR engines like Tesseract, especially on complex or low-quality images. Beyond OCR, VLMs excel at structured information extraction. By prompting Qwen3-VL with a JSON schema, it can extract specific fields such as date, address, scale, and Gnr (plot number) from the image. In one test, it correctly returned the date as 2014-01-23, the address as Camilla Colletts vei 15, and the scale as 1:500. When asked for Bnr (building number), which was missing from the document, it returned None—showing it can distinguish between available and missing data. However, VLMs are not without limitations. One issue is occasional text omission during OCR, where parts of the document are missed entirely. This can be critical in applications like legal or medical document processing. The cause is often related to how the model handles image resolution, aspect ratios, or dense text regions. Another major challenge is computational demand. Even the smaller 4B and 8B versions of Qwen3-VL require significant GPU memory. Processing high-resolution images (e.g., 2048×2048) can quickly exhaust local resources, making large-scale document processing difficult without powerful hardware or cloud infrastructure. Despite these challenges, VLMs are transforming how we interact with visual data. They enable tasks that were previously impossible with text-only models, such as understanding diagrams, forms, and video content. As models like Qwen3-VL become more efficient and accessible, they will play a central role in automating document processing, data entry, and visual reasoning across industries. In summary, VLMs like Qwen3-VL offer a more holistic and accurate way to process visual information compared to the outdated OCR + LLM pipeline. While they come with resource and reliability challenges, their ability to understand both content and context makes them essential tools for the future of AI-driven document and image analysis.