VibeVideoBench:Evaluating Multimodal Agents on Code-Driven Video Generation

A new benchmark, VibeVideoBench, finds that even the strongest multimodal coding agents fail a core requirement on roughly a quarter of code-driven video generation tasks, according to a paper submitted to arXiv on 26 October 2026. The benchmark spans 239 self-contained tasks in English and Chinese across 12 categories, each requiring an agent in a sandbox to deliver a finished MP4 with sound within one hour, and was scored across nine frontier agent systems and 2,151 rollouts. GPT-6 Astra and Claude Opus 5.5 lead clearly, but three quarters of the videos that were never delivered stemmed from final renders started too late, run too slowly or never started.

Computer Science Computer Vision and Pattern Recognition Submitted on 26 Oct 2026 Title:VibeVideoBench:Evaluating Multimodal Agents on Code-Driven Video Generation View PDF http://arxiv.org/pdf/2610.35864v1 HTML experimental https://arxiv.org/html/2610.35864v1 Abstract:Diffusion video models produce striking clips, but they cannot reliably render an exact formula, a correct chart, a ruleconsistent game or narration synchronized to on-screen events. A different route is emerging: multimodal agents that write code to render every frame and synthesize every sound. We introduce VibeVideoBench, a benchmark for evaluating agents on code-driven video generation. Its 239 self-contained tasks in English and Chinese span 12 categories, from data stories and music-driven pieces to classroom lessons and paper explainers; each asks an agent, running in its native harness inside a sandbox, to deliver a finished MP4 with sound within one hour. Evaluation is hierarchical and evidence-based: program checks of the delivery spec, about 13k task-specific rubric checks traced to the requirements they enforce, and five quality dimensions anchored per task, judged by three VLM judge panels and validated with blind human preferences. Across nine frontier agent systems and 2,151 scored rollouts, the benchmark is far from solved: GPT-6 Astra and Claude Opus 5.5 lead clearly, yet even the best system fails a core requirement on about a quarter of tasks. Whether a video arrives at all matters as much as its quality, and three quarters of the lost videos are final renders started too late, run too slowly or never started. The judges agree with each other and with human preferences on system rankings, but only moderately on individual videos. Additional Features References & Citations Loading... Bibliographic and Citation Tools Bibliographic Explorer What is the Explorer? https://info.arxiv.org/labs/showcase.html arxiv-bibliographic-explorer Connected Papers What is Connected Papers? https://www.connectedpapers.com/about Litmaps What is Litmaps? https://www.litmaps.co/ scite Smart Citations What are Smart Citations? https://www.scite.ai/ Code, Data and Media Associated with this Article alphaXiv What is alphaXiv? https://alphaxiv.org/ CatalyzeX Code Finder for Papers What is CatalyzeX? https://www.catalyzex.com DagsHub What is DagsHub? https://dagshub.com/ Gotit.pub What is GotitPub? http://gotit.pub/faq Hugging Face What is Huggingface? https://huggingface.co/huggingface ScienceCast What is ScienceCast? https://sciencecast.org/welcome Demos Recommenders and Search Tools Influence Flower What are Influence Flowers? https://influencemap.cmlab.dev/ CORE Recommender What is CORE? https://core.ac.uk/services/recommender arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs https://info.arxiv.org/labs/index.html .