ByteDance recently officially released its latest open-source multimodal foundation model, BAGEL (Big Advanced Generalized Embodied Learner), which starts a new chapter in multimodal AI models with 7 billion effective parameters. BAGEL performs excellently in key tasks such as image understanding, generation, and editing, surpassing the current mainstream open-source vision-language models (VLMs) like Qwen2.5-VL and InternVL-2.5 on multiple standard evaluations.
The BAGEL model is trained on large-scale interleaved multimodal data, not only possessing strong text-to-image generation capabilities but also achieving results comparable to professional-level generators like Stable Diffusion3 (SD3). In complex tasks such as image editing, free-form operations, and multi-view synthesis, BAGEL's qualitative performance significantly outperforms existing models, demonstrating its potential in frontier directions such as "world modeling".
From a technical architecture perspective, BAGEL adopts a hybrid Transformer-Expert (MoT) structure and uses two independent encoders to capture pixel-level and semantic-level features of images. Its training paradigm follows the "next token prediction" strategy, enabling more efficient multimodal pre-training and supervised learning, thereby achieving stepwise enhancement in understanding and generation capabilities.
To facilitate developer use, ByteDance has not only made the pretrained model and evaluation scripts open source but also provided detailed user documentation and a Gradio WebUI for rapid deployment and testing. Users can access all resources via GitHub Pages.
The research team encourages community participation in model optimization and welcomes feedback on real-world performance issues through GitHub Issues or Discord channels. ByteDance stated that continuous openness and collaboration will be key to advancing BAGEL.
As an integrated multimodal model with understanding, generation, and editing capabilities, the release of BAGEL undoubtedly provides AI researchers and developers with a more powerful tool, marking a new stage in general artificial intelligence that is more practical and open.