Recent advances in image generation models have expanded their applications beyond aesthetic imagery toward practical visual content creation. However, existing benchmarks mainly focus on natural image synthesis and fail to systematically evaluate models under the structured and multi-constraint requirements of real-world commercial design tasks. In this work, we introduce BizGenEval, a systematic benchmark for commercial visual content generation. The benchmark spans five representative document types: slides, charts, webpages, posters, and scientific figures, and evaluates four key capability dimensions: text rendering, layout control, attribute binding, and knowledge-based reasoning, forming 20 diverse evaluation tasks. BizGenEval contains 400 carefully curated prompts and 8000 human-verified checklist questions to rigorously assess whether generated images satisfy complex visual and semantic constraints. We conduct large-scale benchmarking on 26 popular image generation systems, including state-of-the-art commercial APIs and leading open-source models. The results reveal substantial capability gaps between current generative models and the requirements of professional visual content creation. We hope BizGenEval serves as a standardized benchmark for real-world commercial visual content generation.

翻译：近期图像生成模型的进展已将其应用从美学影像扩展到实用视觉内容创作。然而，现有基准测试主要聚焦于自然图像合成，未能系统评估模型在真实商业设计任务中结构化且多约束条件下的表现。本文提出BizGenEval，一个面向商业视觉内容生成的系统性基准测试。该基准涵盖五种代表性文档类型：幻灯片、图表、网页、海报及科学图像，并评估文本渲染、布局控制、属性绑定及基于知识的推理四个关键能力维度，形成20项多样化评估任务。BizGenEval包含400条精心设计的提示词及8000个人工验证的核查清单问题，以严格检验生成图像是否符合复杂视觉与语义约束。我们对26个主流图像生成系统（包括最新商业API及领先开源模型）进行了大规模基准测试。结果表明，当前生成模型与专业视觉内容创作需求之间存在显著能力差距。我们希望BizGenEval能成为真实商业视觉内容生成的标准化基准测试。