ByteDance Open-Source Multimodal AI Model That Challenges GPT-4o and Gemini 2.0

May 26 2025 17:41
A team of researchers from ByteDance, along with collaborators from multiple universities, has released BAGEL, an open-source multimodal AI model that they claim rivals the capabilities of OpenAI's GPT-4o and Google's Gemini 2.0. But unlike those proprietary systems, BAGEL comes with something rare in the AI world: complete transparency and free access to its code and model weights.

The announcement, made through a research paper published in May 2025, represents a significant step toward democratizing advanced AI capabilities that have largely remained locked behind corporate walls. For researchers, developers, and organizations who have been priced out of cutting-edge AI or restricted by API limitations, BAGEL offers a compelling alternative.



BAGEL stands for "Scalable Generative Cognitive Model," though the researchers seem to have embraced the breakfast pastry branding. The model represents what AI researchers call a "unified multimodal" system, meaning it can understand and generate both text and images within a single conversation, rather than requiring separate specialized models for each task.

What sets BAGEL apart from previous open-source attempts is its training approach. The model was trained on what the researchers describe as "trillions of tokens" from a carefully curated mix of text, images, videos, and web data. This interleaved training data allows the model to learn connections between different types of content in ways that more narrowly trained models cannot. Key capabilities of BAGEL include:

• Natural conversation with image understanding and generation
• Complex image editing and manipulation
• Video frame prediction and 3D object manipulation
• Visual reasoning with step-by-step thinking
• Style transfer and artistic transformation

Technically, BAGEL employs what researchers call a "Mixture-of-Transformer-Experts" architecture. Think of it as having two specialist brains working together: one focused on understanding visual content and another on generating it. Both experts share information through a common attention mechanism, allowing them to collaborate on complex tasks.

The model uses 7 billion active parameters out of a total 14 billion, making it relatively efficient compared to some of the largest models in use today. It builds on the Qwen2.5 language model as its foundation, then adds sophisticated visual processing capabilities through two separate encoders: one for understanding images and another for generating them.


Perhaps most intriguingly, BAGEL includes a "thinking" mode where the model can reason through complex prompts before generating visual content. When asked to create "a car made of small cars," for example, the model first works through the conceptual challenge in text, refining its understanding before producing the image. This approach often leads to more coherent and detailed outputs.




One of the most fascinating aspects of BAGEL's development is what researchers observed as they scaled up the training process. Different capabilities emerged at distinct stages, suggesting that advanced AI abilities may develop in predictable patterns as models grow more sophisticated.

Basic multimodal understanding and generation appeared first, followed by simple editing capabilities. Complex, intelligent editing only emerged later in the training process, suggesting that advanced reasoning builds on well-established foundational skills. This staged emergence provides insights into how future AI systems might develop and what capabilities to expect as models continue to grow.

The researchers tested BAGEL against leading open-source models across standard benchmarks and found it consistently outperformed competitors in both understanding and generation tasks. In image generation quality tests, BAGEL achieved an overall score of 0.88 compared to 0.80 for other leading open models.




BAGEL's capabilities extend beyond academic benchmarks into practical applications that could benefit various industries. The model can navigate virtual environments, predict future video frames, and perform sophisticated image editing that goes beyond simple filters or adjustments.

For content creators, BAGEL offers capabilities like style transfer, where it can transform photographs into different artistic styles or even shift them into entirely different visual worlds. For educators and researchers, the model's reasoning capabilities make it useful for explaining complex visual concepts or creating educational materials.

The navigation capabilities are particularly intriguing. By learning from video data, BAGEL has developed an understanding of spatial relationships and movement that allows it to predict what a scene might look like from different perspectives or after movement through space. This kind of spatial reasoning could have applications in robotics, virtual reality, and autonomous systems.

Unlike GPT-4o or Gemini 2.0, which are accessible only through APIs controlled by their respective companies, BAGEL can be downloaded, modified, and deployed anywhere. This opens up possibilities that simply aren't available with proprietary models.

Organizations can fine-tune BAGEL for specific use cases, integrate it into their own products without ongoing API costs, or modify its behavior to meet particular requirements. For researchers, having access to the complete model weights and training code enables deeper investigation into how these systems work and how they might be improved.



BAGEL represents more than just another AI model; it signals a potential shift in how advanced AI capabilities might be distributed. As open-source models approach the performance of proprietary systems, the competitive advantages of closed development may diminish.

This development could accelerate innovation by enabling more researchers and developers to experiment with cutting-edge capabilities. It also raises important questions about AI governance, safety, and the concentration of AI capabilities in the hands of a few large companies.

For users frustrated by the limitations, costs, or restrictions of commercial AI services, BAGEL offers a glimpse of a different future where advanced AI capabilities are freely available tools rather than controlled services. Whether this future materializes will depend on continued progress in open-source AI development and the broader ecosystem that supports it.

Github: https://github.com/bytedance-seed/BAGEL
Hugging Face: https://huggingface.co/ByteDance-Seed/BAGEL-7B-MoT
Research Paper: Emerging Properties in Unified Multimodal Pretraining
Demo: https://demo.bagel-ai.org