Product Introduction

1. Bagel is an open-source unified multimodal model developed by ByteDance-Seed, designed for advanced image and text understanding, generation, editing, and navigation under the Apache 2.0 license. It integrates a natively multimodal architecture capable of processing interleaved image-text inputs and outputs, combining the reasoning capabilities of large language models with photorealistic visual generation.
2. The core value of Bagel lies in its ability to match proprietary systems like GPT-4o and Gemini 2.0 in functionality while remaining fully open-source, enabling developers to fine-tune, distill, and deploy the model for diverse applications without vendor lock-in. It democratizes access to state-of-the-art multimodal AI by offering precise image generation, intelligent editing, and navigation capabilities through a scalable, unified framework.

Main Features

1. Bagel unifies generation and understanding through a Mixture-of-Transformer-Experts (MoT) architecture, leveraging separate pixel-level (VAE) and semantic-level (ViT) encoders to process multimodal data, enabling tasks like image-text dialogue, style transfer, and 3D manipulation within a single model.
2. The model generates high-fidelity images and interleaved content by pretraining on large-scale video and web data, achieving photorealistic outputs through multimodal Chain-of-Thought reasoning that aligns visual and textual tokens during Next Group of Token Prediction.
3. Bagel’s thinking mode enhances output quality by refining prompts into detailed, context-aware instructions, ensuring logical consistency in tasks like compositional image generation (e.g., creating a car model from smaller cars) or dynamic scene editing while preserving visual identities.

Problems Solved

1. Bagel addresses the lack of open-source models capable of advanced multimodal tasks like photorealistic generation, context-aware editing, and environment navigation, which are typically restricted to proprietary systems with limited customization.
2. It serves AI researchers, developers, and enterprises needing scalable solutions for applications such as creative content generation, virtual environment simulation, and industrial automation requiring precise visual reasoning.
3. Typical use cases include generating marketing visuals from text prompts, editing product images while retaining brand-specific details, navigating 3D game environments, and predicting video frames for autonomous systems.

Unique Advantages

1. Unlike open models such as BLIP3-o or MetaQuery-XL, Bagel combines Apache 2.0 licensing with a unified architecture for both generation and understanding, eliminating the need for task-specific models and enabling end-to-end multimodal workflows.
2. Innovations like video-pretrained motion understanding and multimodal Chain-of-Thought allow Bagel to perform intelligent editing (e.g., modifying object interactions in scenes) and style transfer with minimal alignment data, surpassing basic inpainting tools.
3. Competitive benchmarks show Bagel outperforms comparable models in generation accuracy (88% overall score vs. 80% for Janus-Pro-7B) and understanding (67.2 MMVet score vs. 66.6 for BLIP3-o-8B), with emergent capabilities like 3D manipulation arising from scaled pretraining.

Frequently Asked Questions (FAQ)

1. What makes Bagel different from other open-source multimodal models? Bagel integrates generation and understanding in a single architecture, supports complex tasks like style transfer and navigation, and achieves benchmark scores comparable to proprietary models while being fully customizable under Apache 2.0.
2. Can Bagel handle video inputs or generate video content? While primarily optimized for images and text, Bagel’s video-pretrained framework enables frame-by-frame prediction, motion analysis, and sequential reasoning, making it adaptable for video-related tasks like future frame simulation.
3. How does Bagel ensure photorealistic image generation? The model combines VAE and ViT encoders to capture pixel-level details and semantic context, refined through multimodal Chain-of-Thought reasoning during token prediction, ensuring outputs align with both visual realism and textual intent.