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We live in an era where AI is expected to understand the world just as we do — through language, visuals, and sound. Yet most AI models specialize in one modality: some are great with text (like GPT), some shine in image generation (like Stable Diffusion), and others handle video. The holy grail is a unified model that understands and generates across all these domains effortlessly.
https://medium.com/media/fb0241dee1bc382913c31401c2e9a47f/href
Enter BAGEL by ByteDance: an open-source, unified multimodal model designed to tackle this challenge head-on.
Data Science in Your Pocket – No Rocket Science
BAGEL isn’t just another foundation model — it’s a multimodal powerhouse that reads, sees, generates, and reasons. Built with 7 billion active parameters (14B total across two transformer experts), BAGEL sets a new benchmark for what open-source multimodal AI can do.
What is BAGEL?
BAGEL is ByteDance’s open-source entry into the world of large-scale multimodal models. Developed by the ByteDance-Seed research team, it aims to provide a single architecture capable of performing a wide range of tasks, including:
Text and image understanding
Text-to-image generation
Image editing (including intelligent, multi-step edits)
Unlike proprietary models like GPT-4o or Gemini, BAGEL is fully open-source under the Apache 2.0 license, allowing researchers and developers to use, inspect, and fine-tune it without restrictions.
Architecture: Mixture of Experts Meets Unified Vision
At the heart of BAGEL is a clever architectural design built for flexibility and capacity. Here’s what makes it special:
- Mixture-of-Transformer-Experts (MoT): BAGEL uses two Transformer decoder experts — one optimized for understanding and the other for generation. Both process the same input sequence and share a common attention context. This shared attention ensures coherent understanding while allowing each expert to specialize.
- Dual Visual Encoders: Visual inputs are handled by two pretrained encoders:
- A Vision Transformer (SigLIP-L) to extract high-level semantics
- A VAE-based encoder (from FLUX.1) for pixel-level image reconstruction
- Shared Attention Layers: Both experts use a shared self-attention mechanism, allowing for efficient computation and seamless token flow across text, images, and videos.
- Causal Mechanism: Text tokens follow a causal, left-to-right pattern; vision tokens use a bidirectional attention strategy, making it ideal for both autoregressive and bidirectional tasks.
This unified architecture lets BAGEL perform complex tasks like answering questions about images, editing pictures based on descriptions, or generating photorealistic scenes — all with a single model.
Training: Trillions of Tokens, Seamlessly Mixed
Training a model like BAGEL requires not just a smart design but also a massive and diverse dataset. BAGEL was trained on trillions of tokens, pulled from a blend of text, images, videos, and web data.
Key aspects of the training strategy:
- Interleaved Multimodal Data: Instead of training on separate image or text data, BAGEL was fed interleaved streams. For example, a training sequence might include a paragraph of text, followed by image tokens, then more text, and maybe even video frame tokens. This mixed diet helps the model build a unified understanding of context.
- Next Group of Token Prediction: Unlike traditional next-token models, BAGEL predicts entire groups of tokens (e.g., a whole image or sentence) at once. This improves efficiency and encourages richer representations.
- Staged Training Pipeline:
Pretraining on raw, large-scale multimodal data
Continued training on curated instruction-following datasets
Supervised finetuning for specific tasks (e.g., editing, reasoning)
The result is a model that learns not just patterns, but relations between modalities — understanding how a caption relates to an image, or how a video evolves frame by frame.
Capabilities: What BAGEL Can Do
BAGEL isn’t just a research demo; it’s a practical, general-purpose AI system. Here’s a breakdown of its key capabilities:
1. Vision-Language Understanding
BAGEL excels at tasks like:
- Visual Question Answering (“How many people in this image are playing soccer?”)
- Captioning (“Describe this scene in one sentence.”)
- Scene analysis and object recognition
It ranks at the top on benchmarks like MMBench, MMVet, and MMMU, outperforming even the latest open-source competitors like Qwen-VL and InternVL.
2. Text-to-Image Generation
Feed BAGEL a prompt like “A robotic owl perched on a futuristic tree branch,” and it generates a vivid, realistic image. On GenEval, BAGEL scores 0.88, ahead of models like SD3-Medium (0.74) and Janus-Pro-7B (0.80).
The quality is not just in pixel realism but also in adherence to prompts and style consistency.
3. Image Editing
This is where BAGEL truly shines. You can upload an image and ask:
- “Change the sky to a stormy night.”
- “Make the dog wear a wizard hat.”
- “Swap the cat and dog, and turn the dog blue.”
BAGEL handles all these with impressive fidelity and coherence. On GEdit-Bench, it scores 7.36, beating all competitors in open-source.
Even more, BAGEL supports multi-step editing via chain-of-thought prompting. Tell it to plan the edit in steps, and it does just that.
4. World Modeling & Future Prediction
One of BAGEL’s coolest tricks is its ability to predict future video frames or generate a scene from another angle. For instance, given a street photo, it can infer what the back alley might look like.
It also supports navigation tasks like “If I walk forward in this image, what will I see next?” These tasks hint at its internal understanding of 3D space and movement.
Benchmarks: Where BAGEL Stands Out
BAGEL is among the top-performing open models across the board. And with its Apache 2.0 license, anyone can build on it.
Emergent Behaviors: What Surprised the Researchers What sets BAGEL apart is not just what it was trained to do, but what it learned to do:
- Multiview Synthesis: Given one view of a room, it can generate plausible back or side views.
- 3D Spatial Reasoning: Understands where objects are located and can plan navigation.
- Intelligent Editing: Breaks down complex tasks into reasoning steps before executing them.
- Chain-of-Thought Multimodal Reasoning: Handles multi-turn prompts involving both text and visuals.
These weren’t explicitly programmed — they emerged from scale and diversity of training.
Why BAGEL Matters
BAGEL is more than a model — it’s a platform. It sets a precedent for how open-source AI can match proprietary models in sophistication, flexibility, and performance.
- For developers, it means faster prototyping of multimodal apps.
- For researchers, it offers a transparent system to study emergent reasoning.
- For startups, it’s a launchpad to build AI tools without API limitations.
Unlike closed models that gate capabilities behind paywalls or black boxes, BAGEL gives the community the whole package.
How to use BAGEL for free?
- GitHub: https://github.com/Bytedance/bagel
- Demo: https://bagel-ai.org
- Hugging Face Spaces: available for quick test runs
Conclusion: The Future Is Unified
BAGEL represents the next logical step in AI: unified models that understand and generate across all data types. With its smart architecture, massive training, and emergent capabilities, it brings us closer to AI that sees, thinks, and creates like humans do.
It’s open. It’s powerful. It’s ready.
So go ahead — take a bite of BAGEL.
BAGEL by ByteDance: The all-in-one LLM was originally published in Data Science in Your Pocket on Medium, where people are continuing the conversation by highlighting and responding to this story.
