Baidu Releases Open Vision-Language MoE Model
The new ERNIE 4.5 VL model brings advanced multimodal reasoning to the open-source community with an efficient Mixture-of-Experts architecture.

Chinese tech giant Baidu has released ERNIE 4.5 VL, a powerful new vision-language model available under a permissive open-source license. The model is designed for complex reasoning tasks that require understanding both images and text, positioning it as a capable new entry in the open multimodal space.
Efficient by Design
At its core, ERNIE 4.5 VL is a sparse Mixture-of-Experts (MoE) model. While it contains a total of 28 billion parameters, it only activates a fraction—around 3 billion—for any given inference task. This design, hinted at by the 'A3B' (Active 3 Billion) in its name, aims to provide the power of a much larger model while maintaining greater computational efficiency during use.
The model's full name, ERNIE 4.5 VL 28B A3B Thinking, emphasizes its focus on multi-step reasoning. It's built to analyze visual information and perform logical thinking, a challenging frontier for AI development.
By releasing this model under the Apache 2.0 license, Baidu is making a notable contribution to the open-source ecosystem. This gives researchers and developers a sophisticated, efficient, and freely usable foundation for building the next generation of multimodal applications.
Sources
- Visit
baidu/ERNIE-4.5-VL-28B-A3B-Thinking
Hugging Face
0 comments
No comments yet. Be the first to weigh in.
More in Vision-Language
Moonshot AI Releases Kimi, a Multimodal Coding Model
The new Mixture-of-Experts model from the Chinese AI company can generate code while also understanding visual inputs, a rare combination in open models.
Google Releases Open-Source DiffusionGemma 26B Model
The new 26B parameter model from DeepMind uses a diffusion-based architecture, a technique more common in image generation, to produce text.

MiniMax Releases M3, a Multimodal MoE Model
The new open-weight model from MiniMax AI combines vision, coding, and reasoning using a Mixture-of-Experts architecture.