GenAI-Bench
Image generation systems are compared by human side-by-side preference on text-to-image prompts.
Google DeepMind names GenAI-Bench in the Gemini 3.1 Flash Image model card and reports overall preference and visual quality Elo.
- Made by
- Google DeepMind
- Kind
- Image generation and editing
- Level
- Everyday
- Scored
- Ranked by votes
- Answers
- Open-ended
- Last in a launch post
- February 2026, dropped before it saturated
- Last model added
- February 2026
Only Gemini 3.1 Flash Image (nano-banana-2, Web Search) has a rating here, from its lab’s own post
Best score per model as labs report them in their own cards. Labs pick their own settings, so these compare less cleanly than independent runs. A rating ranks a model only against others rated in the same arena at the same time, so these don’t show who leads.
Bars start at 1,025.05.
Source: Labs’ model cards
| # | Model | Score | Setting | Date | Source |
|---|---|---|---|---|---|
| 1 | | 1,079 | Feb 2026 | Lab model card |
Scores over time
All the scores we have, one dot each, so a model tested at several settings appears more than once.
Source: Labs’ model cards
Labs headline it less often now, last in February 2026
Launch posts from every lab that has listed it, one tick per post. Tall colored ticks include it, and short gray ones leave it out.
| Date | Launch post | Score | As the post writes it |
|---|---|---|---|
| Feb 26, 2026 | 1079 | Overall Preference (GenAI-Bench), Thinking + Text Search + Image Search |
One system card reports it
Besides launch posts, we read the system cards, model cards and technical reports labs publish with a release. They report far more, including safety and frontier-risk tests.
| Date | Document | Score | As the document writes it | Section |
|---|---|---|---|---|
| Feb 26, 2026 | | 1079 | Overall Preference (GenAI-Bench), Thinking + Text Search + Image Search | Capabilities |
More in Vision
- MMMU In use 11.5K college-level multimodal questions across 30 subjects. Multiple-choice and open accuracy. GPT-5.1 85.4%
- MMMU-Pro In use Harder MMMU: college-level multimodal questions with more options and vision-only inputs to remove text shortcuts. GPT-5.6 Sol 84.6%
- CharXiv In use Chart understanding on real arXiv figures. Reasoning questions require synthesizing information from complex charts. Claude Fable 5 93.5%
- MATH-Vision (MathVision) In use 3,040 competition math problems with visual context. Answer accuracy.
Qwen 3.8 Max 97.7%
- MMStar In use Models answer 1,500 human-curated image questions that cannot be solved without the image, across 6 core capabilities. Scored by accuracy.
Qwen 3.8 Max 85.9%
- ChartQA In use Models answer human-written and generated questions about chart images that need visual and arithmetic reasoning. Scored by relaxed accuracy with a 5 percent numeric tolerance.
Command A Vision 90.9%