benchmark
UniG2U-Bench reveals unified multimodal models underperform VLMs in most tasks
A new comprehensive benchmark called UniG2U-Bench evaluates whether generation capabilities improve multimodal understanding across 30+ models. The findings show unified multimodal models generally underperform specialized Vision-Language Models, with generation-then-answer inference degrading performance in most cases—though spatial reasoning and multi-round tasks show consistent improvements.