mlsanityData to 14 Oct 2026

WebUIBench: A Comprehensive Benchmark for Evaluating Multimodal Large Language Models in WebUI-to-Code

arXiv 2506.07818 ↗First posted 9 Jun 2025cs.CLcited by 15

On the map: Multimodal LLMs / vision-language models → Chart understanding and anomaly detection

Abstract (arXiv)

With the rapid advancement of Generative AI technology, Multimodal Large Language Models(MLLMs) have the potential to act as AI software engineers capable of executing complex web application development. Considering that the model requires a confluence of multidimensional sub-capabilities to address the challenges of various development phases, constructing a multi-view evaluation framework is crucial for accurately guiding the enhancement of development efficiency. However, existing benchmarks usually fail to provide an assessment of sub-capabilities and focus solely on webpage generation outcomes. In this work, we draw inspiration from the principles of software engineering and further propose WebUIBench, a benchmark systematically designed to evaluate MLLMs in four key areas: WebUI Perception, HTML Programming,WebUI-HTML Understanding, and WebUI-to-Code. WebUIBench comprises 21K high-quality question-answer pairs derived from over 0.7K real-world websites. The extensive evaluation of 29 mainstream MLLMs uncovers the skill characteristics and various weakness that models encountered during the development process.

Key facts. Cited by 15 papers in the map (data through 14 Oct 2026). Placed in Chart understanding and anomaly detection, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 19 of its 30 references that are in the map. First posted on arXiv 9 Jun 2025.

Builds on

19 of 30 references.

Closest ancestors · 2

Same direction, newest first

  1. 2024IW-Bench: Evaluating Large Multimodal Models for Converting Image-to-Web14 Sep
  2. 2024Unlocking the conversion of Web Screenshots into HTML Code with the WebSight Dataset14 Mar

Other references · 17

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  2. 2024Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolutioncited by 4,234
  3. 2024Gemini 1.5: Unlocking multimodal understanding across millions of tokens of contextcited by 3,224
  4. 2023MMBench: Is Your Multi-modal Model an All-around Player?cited by 2,141
  5. 2023MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGIcited by 2,080
  6. 2024Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phonecited by 1,917
  7. 2024Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scalingcited by 1,599
  8. 2024ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Toolscited by 1,121
  9. 2024MiniCPM-V: A GPT-4V Level MLLM on Your Phonecited by 1,079
  10. 2023SEED-Bench: Benchmarking Multimodal LLMs with Generative Comprehensioncited by 940
  11. 2024Yi: Open Foundation Models by 01.AIcited by 719
  12. 2022Pix2Struct: Screenshot Parsing as Pretraining for Visual Language Understandingcited by 366
5 more
  1. 2024NVLM: Open Frontier-Class Multimodal LLMscited by 125
  2. 2017pix2code: Generating Code from a Graphical User Interface Screenshotcited by 114
  3. 2024A Survey on Evaluating Large Language Models in Code Generation Taskscited by 44
  4. 2024A Survey on Multimodal Benchmarks: In the Era of Large AI Modelscited by 27
  5. 2024INST-IT: Boosting Instance Understanding via Explicit Visual Prompt Instruction Tuningcited by 8

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 12.

15 papers.

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By area:

  1. 2026WebCoderBench: Benchmarking Web Application Generation with Comprehensive and Interpretable Evaluation Metricscited by 18
  2. 2025FronTalk: Benchmarking Front-End Development as Conversational Code Generation with Multi-Modal Feedbackcited by 14
  3. 2026WebGameBench: Requirement-to-Application Evaluation for Coding Agents via Browser-Native Gamescited by 12
  4. 2026WorldCoder-Bench: Benchmarking Physically Grounded 3D World Synthesiscited by 9
  5. 2026WebVR: Benchmarking Multimodal LLMs for WebPage Recreation from Videos via Human-Aligned Visual Rubricscited by 8
  6. 2026WebCompass: Towards Multimodal Web Coding Evaluation for Code Language Modelscited by 6
  7. 2026Beyond NL2Code: A Structured Survey of Multimodal Code Intelligencecited by 6
  8. 2026WebTestBench: Evaluating Computer-Use Agents towards End-to-End Automated Web Testingcited by 4
  9. 2026Asuka-Bench: Benchmarking Code Agents on Underspecified User Intent and Multi-Round Refinementcited by 4
  10. 2025WebRenderBench: Enhancing Web Interface Generation through Layout-Style Consistency and Reinforcement Learningcited by 3
  11. 2026Rubrics as Visual-Repair Context for Self-Evolving UI-to-Code Generationcited by 2
  12. 2026VideoVIBE: A Video-Grounded Diagnostic Benchmark for One-Shot Interactive Website Generationcited by 1
3 more
  1. 2025Real-Time Crowd Counting for Embedded Systems with Lightweight Architecturecited by 0
  2. 2026Pattern over Pixels: Measuring Pattern Completion Bias in Multimodal Code Generationcited by 0
  3. 2026AUV-Bench: Aesthetic Understanding and Generation Evaluation for User Interfacescited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025WebMMU: A Benchmark for Multimodal Multilingual Website Understanding and Code Generation
  2. 2026Benchmarking Multimodal LLMs on Code Generation for Complex Interactive Webpages
  3. 2024VisualWebBench: How Far Have Multimodal LLMs Evolved in Web Page Understanding and Grounding?
  4. 2024Design2Code: Benchmarking Multimodal Code Generation for Automated Front-End Engineering
  5. 2024Interaction2Code: Benchmarking MLLM-based Interactive Webpage Code Generation from Interactive Prototyping
  6. 2025Benchmarking MLLM-based Web Understanding: Reasoning, Robustness and Safety
  7. 2024Web2Code: A Large-scale Webpage-to-Code Dataset and Evaluation Framework for Multimodal LLMs
  8. 2026MV-Bench: Benchmarking Multimodal Large Language Models for Coordinated Multi-View Interface Construction