Baidu Unleashes Open-Source Multimodal AI That Claims to Beat GPT-5
When the global AI race feels dominated by Western giants, China’s Baidu has just thrown down a serious challenge. The company has released a new open-source multimodal model — ERNIE-4.5-VL-28B-A3B-Thinking — which it claims can outperform GPT-5 and Gemini 2.5 Pro in a range of vision-language tasks. Even more striking: it’s free to use commercially under an Apache 2.0 license.
🚀 A Closer Look at ERNIE-4.5-VL-28B-A3B-Thinking
This new model is part of Baidu’s ERNIE 4.5 family but introduces a clever architectural shift. Despite having 28 billion parameters, it only activates about 3 billion per inference through a Mixture-of-Experts (MoE) design — a major boost in efficiency.
Here’s what makes it stand out:
- “Thinking with Images” – Instead of processing visuals at a fixed resolution, the model can dynamically zoom in and out to focus on fine-grained image details — much like a human would.
- Advanced multimodal reasoning – It can handle documents, charts, visual grounding, and even temporal video understanding, making it useful across a wide range of enterprise tasks.
- Efficiency by design – Baidu says the model can run inference on a single 80 GB GPU, thanks to the MoE structure — a big deal for cost-conscious developers.
- Open and commercial-friendly – The Apache 2.0 license allows unrestricted use, modification, and deployment, breaking free from the constraints of closed ecosystems.
- Developer-ready – It’s fully compatible with Hugging Face, vLLM, and FastDeploy, enabling easier fine-tuning and integration.
(Sources: VentureBeat, Hugging Face Model Page)
✅ Why This Release Matters
For enterprises and AI teams, Baidu’s move could be transformative:
- Top-tier performance at lower cost – If the company’s claims hold true, organizations can deploy powerful multimodal capabilities without massive compute budgets.
- Freedom to innovate – The open-source license removes vendor lock-in and encourages experimentation.
- Real-world relevance – The model’s strengths in visual reasoning make it ideal for document automation, manufacturing inspection, and data visualization analysis.
- Geopolitical significance – It signals Baidu’s ambition to compete head-on with OpenAI and Google on the global AI stage.
⚠️ A Few Caveats
No breakthrough is without fine print:
- Independent benchmarks pending – The “beats GPT-5” claim hasn’t yet been validated by external researchers.
- Hardware accessibility – One 80 GB GPU might be “modest” by hyperscaler standards but still costly for small labs.
- Task specialization – ERNIE-4.5-VL shines in structured vision-language domains but may not excel at open-ended creative generation.
- Deployment complexity – MoE routing adds engineering overhead, and smaller teams may face challenges fine-tuning or serving the model efficiently.
- Limited transparency on safety and bias – The documentation offers little detail on robustness or fairness testing.
🔍 Broader Implications
Baidu’s release underscores several big shifts in the AI landscape:
- Multimodal is the new frontier – The next generation of AI models won’t just read and write — they’ll see, reason, and interpret across formats.
- Open-source momentum is accelerating – As more enterprise-grade models adopt permissive licensing, innovation will spread faster and become more democratized.
- Smarter beats bigger – Architectural ingenuity, not just parameter count, is becoming the new performance edge.
- Practical deployment matters – Expect more demand for engineers skilled in MLOps, MoE optimization, and efficient multimodal serving.
For machine learning researchers and developers, the takeaway is clear: this is a model worth exploring — not just for its capabilities, but for what it represents in the shifting dynamics of global AI development.
🔑 Glossary
Mixture-of-Experts (MoE) – A neural network design that routes each input to a subset of specialized subnetworks (“experts”), activating only the most relevant parts of the model to boost efficiency.
Visual Grounding – The process of linking textual references to corresponding elements in an image or video.
Multimodal AI – Systems that can understand and generate across multiple data types, such as text, images, and videos.
Apache 2.0 License – A permissive open-source license allowing free commercial use, modification, and distribution.
Dynamic Image Zoom (“Thinking with Images”) – Baidu’s technique allowing the model to adapt its focus within images, zooming in on details rather than processing at a fixed scale.
🎯 Final Word
Baidu’s ERNIE-4.5-VL-28B-A3B-Thinking marks a bold leap in multimodal AI — open-source, efficient, and enterprise-ready. Whether it truly surpasses GPT-5 remains to be seen, but it’s already setting new expectations for what open AI systems can deliver.
If it performs as advertised, this could mark a turning point — from “bigger is better” to “smarter, leaner, and more open.”
