Bagel AI

ByteDance BAGEL: Redefining Multimodal AI with Open-Source Innovation

In May 2025, ByteDance unveiled BAGEL, a groundbreaking open-source multimodal AI model that pushes the boundaries of vision-language models (VLMs). With 7 billion active parameters (14 billion total), BAGEL excels in text understanding, image generation, video processing, and advanced reasoning, outperforming leading open-source competitors. This article dives into BAGEL’s architecture, capabilities, benchmarks, and its significance for the AI community.

What is BAGEL?

BAGEL, or Big Advanced Generalized Embodied Learner, is a unified, decoder-only multimodal foundation model developed by ByteDance’s Seed team. Trained on trillions of interleaved multimodal tokens, it natively supports text, images, and videos, making it a versatile tool for tasks like text-to-image generation, image editing, and visual reasoning. Released under the permissive Apache 2.0 license, BAGEL is freely available for researchers and developers to explore and build upon.

Key Features of BAGEL

• Multimodal Capabilities: Understands and generates text, images, and videos with state-of-the-art performance.
• Mixture-of-Transformer-Experts (MoT): Combines transformer architecture with dual encoders for efficient processing.
• 7B Active Parameters: Optimized for performance with a total of 14 billion parameters.
• Open-Source: Fully accessible weights and code under Apache 2.0.
• Advanced Reasoning: Excels in complex tasks like free-form visual manipulation and world modeling.

Technical Architecture

BAGEL’s architecture is a hybrid of a Mixture-of-Transformer-Experts (MoT) and dual encoders, enabling it to handle diverse modalities efficiently. Unlike traditional VLMs that rely on separate modules for text and vision, BAGEL uses a unified decoder-only approach. This design reduces latency and improves coherence across tasks, from generating high-quality images to editing videos based on text prompts.

Training Data

ByteDance trained BAGEL on a massive dataset of interleaved multimodal tokens, including text, images, and videos. This large-scale pretraining allows BAGEL to generalize across tasks, making it adept at understanding context and generating coherent outputs. While specific details about the dataset remain undisclosed, its scale is comparable to that of leading proprietary models.

Benchmark Performance

BAGEL sets a new standard for open-source VLMs, surpassing models like Qwen2.5-VL and InternVL-2.5 on multiple benchmarks. Below is a summary of its performance across key multimodal tasks:

Note: Higher scores indicate better performance, except for FID (Fréchet Inception Distance), where lower is better. Data sourced from ByteDance’s official benchmarks.

Standout Capabilities

• Image Generation: Produces high-fidelity images from text prompts, rivaling proprietary models.
• Image Editing: Supports precise, text-guided edits, such as free-form visual manipulation.
• Video Processing: Understands and generates video content, a rarity among open-source models.
• World Modeling: Demonstrates advanced reasoning for 3D environments and simulations.

Why BAGEL Matters

BAGEL’s release is a milestone for the open-source AI community. By providing a model that competes with proprietary systems, ByteDance is democratizing access to cutting-edge multimodal AI. Its Apache 2.0 license ensures that developers can use, modify, and distribute BAGEL without restrictions, fostering innovation in fields like creative arts, robotics, and scientific research.

Accessing BAGEL

ByteDance has made BAGEL widely available through official repositories and platforms. Below are the primary resources for accessing the model:

• GitHub Repository: Full code and model weights.
• Hugging Face: Pretrained model and documentation.

While BAGEL is not yet integrated into Ollama, community efforts are underway to add support, as seen in GitHub discussions.

Challenges and Future Directions

Despite its strengths, BAGEL faces challenges. Its code requires specific dependencies, which may complicate deployment for some users. Additionally, while it excels in multimodal tasks, its text-only performance lags behind dedicated language models. Future iterations could address these gaps by optimizing dependencies and enhancing text capabilities.

Potential Applications

• Creative Industries: Generating art, editing videos, and designing 3D models.
• Robotics: Enabling embodied AI with world modeling and visual reasoning.
• Research: Advancing studies in multimodal learning and generative AI.

Conclusion

ByteDance’s BAGEL is a game-changer for open-source AI, offering unmatched multimodal capabilities under a permissive license. Its superior performance, accessible resources, and community enthusiasm make it a must-watch model for 2025. Whether you’re a developer, researcher, or AI enthusiast, BAGEL is poised to inspire the next wave of innovation. Dive into its repositories on GitHub or Hugging Face to explore its potential today.