Recently, advancements in large multimodal models have led to significant strides in image comprehension capabilities. Despite these advancements, there is a lack of a robust benchmark specifically for assessing the image‐to‐web conversion proficiency of these large models. It is essential to ensure the integrity of the web elements generated, which comprise both visible and invisible categories. Previous evaluation methods (e.g., BLEU) are notably susceptible to significant alterations due to the presence of invisible elements. Furthermore, it is crucial to measure the layout information of web pages—i.e., the positional relationships between elements—which has been overlooked by prior work. To address these challenges, we have curated and aligned a benchmark of images and corresponding web codes (IW-bench). Specifically, we propose Element Accuracy, which tests the completeness of elements by parsing the Document Object Model (DOM) tree. We also introduce Layout Accuracy to analyze positional relationships by converting the DOM tree into a common subsequence. In addition, we design a five‐hop multimodal Chain‐of‐Thought prompting strategy for improved performance, consisting of: 1) SoM prompt injection, 2) inferring elements, 3) inferring layout, 4) inferring web code, and 5) reflection. Our benchmark comprises 1,200 image–code pairs with varying levels of difficulty. We have conducted extensive experiments on existing large multimodal models, providing insights into their performance and identifying areas for improvement in the image‐to‐web domain.

While large language models (LLMs) have shown considerable promise in code generation, real-world software development demands advanced repository-level reasoning. This includes understanding dependencies, project structures, and managing multi-file changes. However, the ability of LLMs to effectively comprehend and handle complex code repositories has yet to be fully explored. To address these challenges, we introduce a hierarchical benchmark designed to evaluate repository dependency understanding(DependEval) for LLMs. The benchmark is based on 2683 repositories collected from real-world websites. It evaluates models on three core tasks: Dependency Recognition, Repository Construction, and Multi-file Editing, across 8 programming languages from actual code repositories. Our evaluation of over 25 LLMs reveals substantial performance gaps and provides valuable insights into repository-level code understanding.