BloomBench - English - Understand: leaderboard

BloomBench (Almieyar-Oryx series, QCRI, 2026) is a bilingual English and Arabic multiple-choice benchmark for vision-language models organized by the six levels of the revised Bloom taxonomy, scored as zero-shot accuracy in percent from the answer letter parsed from generated text (chance 25). This board is accuracy on the 1,592 English items of the Understand level (compositional scene understanding, emotions and semantic knowledge); higher is better.

Metric: Accuracy (%) on the 1592 English BloomBench items at the Bloom level Understand (compositional and cognitive understanding: relations, emotions, semantics), four-option image-question multiple choice, zero-shot, temperature 0, the chosen letter parsed from the generated answer (Regex-based Answer Extraction; unparseable answers count as wrong); chance 25; higher is better. Source: arxiv.org. Saturation forecast: Estimated already saturated. 8 models tracked.

Top models

#ModelScoreOverall rank
1Gemma 3 27B94#1631
2Qwen 2.5 VL 7B94#1787
3Gemma 3 12B94#2000
4Qwen 2 VL 7B93#2261
5Gemma 4 31B93#394
6Gemma 4 26B A4B91#615
7GPT-4o Mini90#1623
8Gemma 3 4B89#5064

No result here: #1 Claude Fable 5.2, #2 Gemini 4 Carbon, #5 Claude Opus 5.5.

Interactive version: theaggregate.ai/benchmark?slug=bloombench-english-understand · How It Works · Data refreshed daily, snapshot 2026-11-13.