multimodal benchmark
VIBE-Eval is a hard evaluation suite for measuring progress of multimodal language models, consisting of 269 visual understanding prompts with gold-standard responses authored by experts. The benchmark has dual objectives: vibe checking multimodal chat models for day-to-day tasks and rigorously testing frontier models, with the hard set containing >50% questions that all frontier models answer incorrectly.
Updated Aug 11, 2026
Higher score ranks better on this benchmark.
| 01 | GO | 67.2% | 100.0% | 8 | C | |
| 02 | GO | 65.6% | 85.7% | 8 | C | |
| 03 | GO | 65.4% | 71.4% | 8 | C | |
| 04 | GO | 56.3% | 57.1% | 8 | C | |
| 05 | GO | 53.9% | 42.9% | 8 | C | |
| 06 | GO | 51.3% | 28.6% | 8 | C | |
| 07 | GO | 48.9% | 14.3% | 8 | C | |
| 08 | GO | 40.9% | 0.0% | 8 | C |
A closer view of the leading scores on this benchmark.
The leading models and scores on this benchmark.
What Vibe-Eval measures and how its scores work.
VIBE-Eval is a hard evaluation suite for measuring progress of multimodal language models, consisting of 269 visual understanding prompts with gold-standard responses authored by experts. The benchmark has dual objectives: vibe checking multimodal chat models for day-to-day tasks and rigorously testing frontier models, with the hard set containing >50% questions that all frontier models answer incorrectly.
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
Benchmark scores retain their original unit. Overall score eligibility is shown separately.
Common questions about Vibe-Eval.
Gemini 2.5 Pro Preview 06-05 is currently ranked first with 67.2%.
VIBE-Eval is a hard evaluation suite for measuring progress of multimodal language models, consisting of 269 visual understanding prompts with gold-standard responses authored by experts. The benchmark has dual objectives: vibe checking multimodal chat models for day-to-day tasks and rigorously testing frontier models, with the hard set containing >50% questions that all frontier models answer incorrectly.
Yes. Higher values rank better for this benchmark.
8 model results are currently shown.
Yes. This benchmark can contribute to the current LLMBoard capability score.