VibeVideoBench: Evaluating Multimodal Agents on Code-Driven Video Generation
VibeVideoBench is a benchmark of 239 self-contained tasks in English and Chinese across 12 categories, where agents write code to render frames and synthesize audio to deliver an MP4 within one hour, without image or video generation models. Evaluation uses program checks, about 13k rubric checks, and five quality dimensions scored by three VLM judge panels. Across nine agent systems and 2,151 rollouts, GPT-6 Astra and Claude Opus 5.5 rank first and second, and the strongest system meets every core requirement on 76.3% of tasks. Many failures stem from late, slow, or missing renders.
Open original source huggingface.coOne primary source; other links are context, not independent corroboration.
- Primary sourceHugging Face Daily Papers
- Discovery linkHugging Face
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VibeVideoBench is a benchmark of 239 self-contained tasks in English and Chinese across 12 categories, where agents write code to render frames and synthesize audio to deliver an MP4 within one hour, without image or video generation models. Evaluation uses program checks, about 13k rubric checks, and five quality dimensions scored by three VLM judge panels. Across nine agent systems and 2,151 rollouts, GPT-6 Astra and Claude Opus 5.5 rank first and second, and the strongest system meets every core requirement on 76.3% of tasks. Many failures stem from late, slow, or missing renders.
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- 03Where are the sources?
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Source claims not independently checked; the judges are VLM panels and reliability is reported only at the system-ranking level.
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Key findings from the stored brief
- The strongest evaluated system satisfies every core requirement on 76.3% of tasks.
- Of 224 runs with no playable video, 169 were final renders that started too late, ran too slowly, or never started.
- Judges' system ranking closely matches blind human preferences (ρ = 0.98), but they agree on only 73% of individual pairs.
- Agents inspect their own frames in 95% of runs, yet visual inspection amount does not predict quality (ρ = −0.07).
Source claims not independently checked; the judges are VLM panels and reliability is reported only at the system-ranking level.
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Why this may matter
The benchmark targets a route to video generation that offers exact formulas, charts, and synchronized narration, which diffusion models struggle to render reliably. It also highlights that planning and time management, not only rendering, limit agent performance.
Limits and open questions
Source claims not independently checked; the judges are VLM panels and reliability is reported only at the system-ranking level.
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