QwenWebBench
QwenWebBench is an internal front-end code generation benchmark by Qwen. It is bilingual (EN/CN) and spans 7 categories (Web Design, Web Apps, Games, SVG, Data Visualization, Animation, and 3D), using auto-render plus a multimodal judge for code and visual correctness. Scores are reported as BT/Elo ratings.
Qwen3.7 Max from Alibaba Cloud / Qwen Team currently leads the QwenWebBench leaderboard with a score of 1568.000 across 2 evaluated AI models.
What QwenWebBench measures
QwenWebBench is a multimodal benchmark that evaluates large language models on multimodal, agents, and coding tasks. LLM Stats tracks 2 models on this benchmark, with a maximum possible score of 2000. Current average across reported models is 1527.5, with the leader reaching 1568.0.
Compare leaders on the best AI for multimodal, best AI for agents and best AI for coding leaderboards.
Qwen3.7 Max leads with 1568.000, followed by Qwen3.6-27B at 1487.000.
Progress Over Time
Interactive timeline showing model performance evolution on QwenWebBench
QwenWebBench Leaderboard
| Context | Cost | License | ||||
|---|---|---|---|---|---|---|
| 1 |  Alibaba Cloud / Qwen Team | — | 1.0M | $1.25 / $3.75 | ||
| 2 | Alibaba Cloud / Qwen Team | 28B | 262K | $0.60 / $3.60 |
FAQ
Common questions about QwenWebBench.
More evaluations to explore
Related benchmarks in the same category
MMMU (Massive Multi-discipline Multimodal Understanding) is a benchmark designed to evaluate multimodal models on college-level subject knowledge and deliberate reasoning. Contains 11.5K meticulously collected multimodal questions from college exams, quizzes, and textbooks, covering six core disciplines: Art & Design, Business, Science, Health & Medicine, Humanities & Social Science, and Tech & Engineering across 30 subjects and 183 subfields.
A more robust multi-discipline multimodal understanding benchmark that enhances MMMU through a three-step process: filtering text-only answerable questions, augmenting candidate options, and introducing vision-only input settings. Achieves significantly lower model performance (16.8-26.9%) compared to original MMMU, providing more rigorous evaluation that closely mimics real-world scenarios.
BrowseComp is a benchmark comprising 1,266 questions that challenge AI agents to persistently navigate the internet in search of hard-to-find, entangled information. The benchmark measures agents' ability to exercise persistence in information gathering, demonstrate creativity in web navigation, and find concise, verifiable answers. Despite the difficulty of the questions, BrowseComp is simple and easy-to-use, as predicted answers are short and easily verifiable against reference answers.
Terminal-Bench 2.0 is an updated benchmark for testing AI agents' tool use ability to operate a computer via terminal. It evaluates how well models can handle real-world, end-to-end tasks autonomously, including compiling code, training models, setting up servers, system administration, security tasks, data science workflows, and cybersecurity vulnerabilities.
CharXiv-R is the reasoning component of the CharXiv benchmark, focusing on complex reasoning questions that require synthesizing information across visual chart elements. It evaluates multimodal large language models on their ability to understand and reason about scientific charts from arXiv papers through various reasoning tasks.
MathVista evaluates mathematical reasoning of foundation models in visual contexts. It consists of 6,141 examples derived from 28 existing multimodal datasets and 3 newly created datasets (IQTest, FunctionQA, and PaperQA), combining challenges from diverse mathematical and visual tasks to assess models' ability to understand complex figures and perform rigorous reasoning.