VisExpress: Beyond Rendering—Benchmarking How LLM-Generated HTML Answers Convey Information

As the code-generation capabilities of Large Language Models (LLMs) advance, applications powered by LLMs increasingly present answers as HTML pages integrating text, charts, media, and interactive components. When appropriately designed, such responses can make information easier to understand and navigate. However, existing benchmarks primarily focus on code correctness, component quality, or design fidelity, paying limited attention to how effectively a complete HTML answer communicates information. To address this issue, we introduce VisExpress, a benchmark for evaluating the visual expression quality of complete LLM-generated HTML answers. VisExpress comprises 786 queries across 16 domains, combining real-user queries with curated queries that broaden the coverage of presentation tasks. It includes component-necessity annotations and covers 12 component forms organized into four categories: structured text, charts, media, and interactive components. We further develop a multimodal evaluation framework consisting of 13 metrics organized into two complementary dimensions. Design Normativity assesses the basic visual quality of page elements and layouts, while Presentation Effectiveness assesses whether component selection and information organization help users find and understand information. Using multimodal evidence—including screenshots, individual images, and recordings of animations and interactions—the framework evaluates both missing necessary representations and redundant components. Experiments across 12 models show that the model with the highest Design Normativity score trails the best model in Presentation Effectiveness by 9.6 points. While most models produce orderly layouts, component selection and information organization remain key challenges. The framework shows positive alignment with human judgments and matches the aggregate model ranking observed in our human study.