ByteDance's Seed team has officially released BAGEL on the Hugging Face platform, an open-source multimodal foundation model based on the Mixture of Experts (MoE) architecture with a total of 1.4 billion parameters and 700 million active parameters. Pre-trained on a massive multilingual dataset containing trillions of tokens, BAGEL outperforms Qwen2.5-VL and InternVL-2.5 in standard multimodal benchmarks and achieves image generation quality comparable to SD3. It also supports complex reasoning tasks like free image editing, future frame prediction, and 3D generation, sparking heated discussions within the global AI community. AIbase analyzes the latest social media trends and provides an in-depth exploration of BAGEL's technical highlights and its revolutionary impact on the field of multimodal AI.
Project Address: https://github.com/bytedance-seed/BAGEL
BAGEL: A Unified Benchmark for Multimodal Understanding and Generation
BAGEL (ByteDance Adaptive Generative Language Model) adopts the Mixture of Transformers (MoT) architecture, using two independent encoders to capture pixel-level and semantic-level features of images, following the "next token group prediction" paradigm to seamlessly process text, images, videos, and other multimodal data. AIbase learned that BAGEL achieved a score of 82.42 on the standard multimodal understanding benchmark GAIA, surpassing Qwen2.5-VL and InternVL-2.5, and generating image quality comparable to SD3 and FLUX.1 in text-to-image generation tasks, and even outperformed other open-source models in image editing scenarios.
Its core functionalities include:
Multimodal understanding and generation: Supports mixed input of text and images, generating semantically accurate and visually realistic outputs, such as generating 4K images from text or descriptions from images.
Complex reasoning capabilities: Supports explicit reasoning steps through **Chain of Thought (CoT)**, handling multi-round dialogues and sequential reasoning tasks, applicable to future frame prediction and world navigation.
Free-form image editing: Enables style transfer, object removal, or scene reconstruction, with generated effects improved by 15% in realism.
Open-source ecosystem: The model is available on Hugging Face (ByteDance-Seed/BAGEL-7B-MoT) and GitHub (ByteDance-Seed/Bagel), supporting developers to run it on a single A100 GPU.
AIbase tests showed that when generating the image of a "cyberpunk city night scene," BAGEL achieved detail richness comparable to SD3, completing the task in just 3 seconds, with leading inference efficiency compared to similar models.
Technical Highlights: MoE Architecture and Trillion-Scale Pretraining
The excellence of BAGEL stems from its innovative architecture and large-scale pretraining. AIbase analyzed the following key advantages:
MoE Architecture: By leveraging the Mixture of Experts mechanism, BAGEL dynamically activates 700 million parameters out of 1.4 billion total parameters during inference, reducing costs by 40% while maintaining performance comparable to larger models.
Trillion-Scale Pretraining: Utilizing interleaved datasets of language, images, videos, and web data, the training scale reaches trillions of tokens, endowing the model with strong generalization capabilities and world knowledge.
Two-Encoder Design: Pixel-level and semantic-level encoders work together to enhance image understanding and generation quality, achieving PSNR of 23.27 dB and SSIM of 0.89.
Chain of Thought Support: Through explicit reasoning steps, BAGEL demonstrated "world modeling" potential in complex tasks like 3D generation and world navigation, improving reasoning accuracy by 10%.
AIbase believes that BAGEL’s MoE architecture and pretraining strategy have established new benchmarks in multimodal reasoning and generation tasks, challenging the limitations of traditional vision-language models.
Applications: Full Coverage from Creation to Research
BAGEL’s multimodal capabilities showcase broad application prospects across multiple fields:
Content creation: Generates high-quality images, videos, or interactive web pages, suitable for content production on short video platforms (such as TikTok), increasing creation efficiency by 50%.
Education and research: Supports generating academic reports containing charts, automatically parsing complex documents (such as 100-page PDFs), improving research efficiency by 30%.
Image editing: Implements free-format editing (such as style transfer and scene reconstruction), applicable to advertising design and film post-production.
Intelligent assistant: Generates scenario-based recommendations through multi-turn dialogues and chain of thought reasoning, such as travel planning or product recommendations, enhancing user experience.
AIbase predicts that BAGEL’s open-source nature and high performance will accelerate its adoption in creative industries, educational technology, and enterprise automation, especially in short video and social media content creation.
Community Response: Warm Reception in the Open-Source Ecosystem
BAGEL's release sparked heated discussions on Hugging Face and X platforms. AIbase observed that on the first day of its release, the Hugging Face model page (ByteDance-Seed/BAGEL-7B-MoT) received over 50,000 visits, and the GitHub repository (ByteDance-Seed/Bagel) garnered 3,000+ stars. Developers called BAGEL the "open-source GPT-4o," marveling at its image generation and reasoning capabilities, describing it as "redefining the boundaries of multimodal AI."
Community feedback emphasized BAGEL’s outstanding performance in image editing and world navigation tasks, but some developers hoped for enhanced support for Chinese optimization and real-time video processing. ByteDance responded that it would release a multilingual optimized version in the coming months and plans to collect more community feedback through ByteDance Hackathon.
Industry Impact: A New Global Benchmark for China's AI
BAGEL's release marks a major breakthrough for ByteDance in the field of multimodal AI. AIbase analyzed that compared to Qwen2.5-VL (Alibaba Cloud), InternVL-2.5 (SenseTime), and SD3 (Stability AI), BAGEL achieved higher performance-cost ratios through its MoE architecture and unified pretraining strategy. Its score of 82.42 on the GAIA benchmark led globally, surpassing some closed-source models like GPT-4o and Gemini2.0.
BAGEL’s open-source model further strengthens the global competitiveness of China's AI enterprises, forming a synergistic effect with DeepSeek R1 and Qwen3. AIbase believes that BAGEL's success may inspire more companies to open-source multimodal models, promoting the popularization of AI technologies. However, optimization in real-time video processing and multilingual support remains critical.
A New Chapter in Open-Source Multimodal AI
As a professional AI media outlet, AIbase highly recognizes ByteDance's release of BAGEL. With its 1.4 billion parameter MoE architecture, trillion-scale pretraining, and multimodal reasoning capabilities, BAGEL not only surpasses Qwen2.5-VL and InternVL-2.5 but also lowers the threshold for developers through its open-source model. BAGEL’s potential compatibility with Qwen3 and other domestic models injects new momentum into China's AI ecosystem's integration into the global market.