Vibe-Eval
Progress Over Time
Interactive timeline showing model performance evolution on Vibe-Eval
Vibe-Eval Leaderboard
| Context | Cost | License | ||||
|---|---|---|---|---|---|---|
| 1 | — | — | — | |||
| 2 |  Google | — | 1.0M | $1.25 / $10.00 | ||
| 3 | Google | — | — | — | ||
| 4 | Google | — | — | — | ||
| 5 | Google | — | — | — | ||
| 6 | Google | — | — | — | ||
| 7 | Google | — | — | — | ||
| 8 | Google | 8B | — | — |
What is Vibe-Eval?
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.
Vibe-Eval is a multimodal benchmark evaluating models on multimodal, general, and vision tasks. LLM Stats tracks 8 models on this benchmark, scored on a 0–1 scale. The current average is 0.6, with the leader at 0.7.
Compare leaders on the best AI for multimodal, best AI for general and best AI for vision leaderboards.
Current leaders
Gemini 2.5 Pro Preview 06-05 from Google currently leads the Vibe-Eval leaderboard with a score of 0.672 across 8 evaluated AI models.
Source paper
- Title
- Vibe-Eval: A hard evaluation suite for measuring progress of multimodal language models
- Authors
- Piotr Padlewski, Max Bain, Matthew Henderson, Zhongkai Zhu, and 18 others
- Published
- arXiv
- 2405.02287
Abstract
We introduce Vibe-Eval: a new open benchmark and framework for evaluating multimodal chat models. Vibe-Eval consists of 269 visual understanding prompts, including 100 of hard difficulty, complete with gold-standard responses authored by experts. Vibe-Eval is 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, our hard set contains >50% questions that all frontier models answer incorrectly. We explore the nuances of designing, evaluating, and ranking models on ultra challenging prompts. We also discuss trade-offs between human and automatic evaluation, and show that automatic model evaluation using Reka Core roughly correlates to human judgment. We offer free API access for the purpose of lightweight evaluation and plan to conduct formal human evaluations for public models that perform well on the Vibe-Eval's automatic scores. We release the evaluation code and data, see https://github.com/reka-ai/reka-vibe-eval
FAQ
Common questions about the Vibe-Eval benchmark and leaderboard.