Recent advances in large language models have improved the capabilities of coding agents, yet systematic evaluation of complex, end-to-end website development remains limited. To address this gap, we introduce Vision2Web, a hierarchical benchmark for visual website development, spanning from static UI-to-code generation, interactive multi-page frontend reproduction, to long-horizon full-stack website development. The benchmark is constructed from real-world websites and comprises a total of 193 tasks across 16 categories, with 918 prototype images and 1,255 test cases. To support flexible, thorough and reliable evaluation, we propose workflow-based agent verification paradigm based on two complementary components: a GUI agent verifier and a VLM-based judge. We evaluate multiple visual language models instantiated under different coding-agent frameworks, revealing substantial performance gaps at all task levels, with state-of-the-art models still struggling on full-stack development.

翻译：近年来大语言模型的进步提升了编码智能体的能力，然而针对复杂端到端网站开发的系统性评估仍十分有限。为填补这一空白，我们提出Vision2Web——一个面向视觉网站开发的层级化基准测试，涵盖从静态UI到代码生成、交互式多页面前端复现，以及长时序全栈网站开发等任务。该基准基于真实网站构建，包含16个类别的193项任务，共918张原型图像与1,255个测试用例。为实现灵活、全面且可靠的评估，我们提出基于工作流的智能体验证范式，该范式由两个互补组件构成：GUI智能体验证器与基于VLM的评估器。我们评估了在不同编码智能体框架下实现的多种视觉语言模型，揭示出所有任务层级均存在显著性能差距，当前最先进模型在全栈开发任务中仍面临挑战。