Vision2Web: A Hierarchical Benchmark for Visual Website Development with Agent Verification

Abstract

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.

Figures (12)

• Figure 1: Overview of Vision2Web, a hierarchical benchmark for visual website development. Tasks span three levels—static webpages, interactive frontends, and full-stack websites—requiring agents to integrate visual prototypes with textual specifications. Evaluation is performed via a workflow-based agent verification paradigm, measuring functional correctness and visual fidelity.
• Figure 2: Task distribution of Vision2Web across four major categories and 16 subcategories.
• Figure 3: Distribution of test cases across website-level tasks in Vision2Web.
• Figure 4: Distribution of Visual Scores (VS) across prototype heights for representative models under the OpenHands framework.
• Figure 5: Distribution of prototype image sizes across different device types.