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Vision-Language Models · benchmark dataset · ENGLISH

Vibe-Eval: A hard evaluation suite for measuring progress of multimodal language models.

Vibe-Eval is an open benchmark for evaluating multimodal chat models. It consists of 269 visual understanding prompts, including 100 of hard difficulty, complete with gold-standard responses authored by experts. The benchmark is designed to be open-ended and challenging with dual objectives: (i) vibe checking multimodal chat models for day-to-day tasks and (ii) rigorously testing and probing the capabilities of present frontier models. Notably, the hard set contains >50% questions that all frontier models answer incorrectly.

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  • 01A public checkpoint or API endpoint
  • 02A reproduction script with frozen commit + seed
  • 03Declared evaluation environment (Python, deps)
  • 04One row per metric declared by this dataset
  • 05A contact so we can follow up on discrepancies