This academic paper introduces BLIP3-o, a suite of cutting-edge multimodal models designed for both understanding and generating images. The research investigates various architectural choices and training techniques, finding that CLIP image features and flow matching are effective for image generation, while a sequential training strategy—starting with understanding before generation—yields the best overall performance. The authors also present BLIP3o-60k, a new dataset created with GPT-4o, to improve the models' ability to follow instructions and produce aesthetically pleasing images. The paper includes performance benchmarks and a human study demonstrating BLIP3-o's superior capabilities and offers its components as open-source resources to encourage further advancements in unified multimodal AI.