Baidu’s latest ERNIE model, a super-efficient multimodal AI, is beating GPT and Gemini on key benchmarks and targets enterprise data often ignored by text-focused models.
For many businesses, valuable insights are locked in engineering schematics, factory-floor video feeds, medical scans, and logistics dashboards. Baidu’s new model, ERNIE-4.5-VL-28B-A3B-Thinking, is designed to fill this gap.
What’s interesting to enterprise architects is not just its multimodal capability, but its architecture. It’s described as a “lightweight” model, activating only three billion parameters during operation. This approach targets the high inference costs that often stall AI-scaling projects. Baidu is betting on efficiency as a path to adoption, training the system as a foundation for “multimodal agents” that can reason and act, not just perceive.
Complex visual data analysis capabilities supported by AI benchmarks
Baidu’s multimodal ERNIE AI model excels at handling dense, non-text data. For example, it can interpret a “Peak Time Reminder” chart to find optimal visiting hours, a task that reflects the resource-scheduling challenges in logistics or retail.
ERNIE 4.5 also shows capability in technical domains, like solving a bridge circuit diagram by applying Ohm’s and Kirchhoff’s laws. For R&D and engineering arms, a future assistant could validate designs or explain complex schematics to new hires.
This capability is supported by Baidu’s benchmarks, which show ERNIE-4.5-VL-28B-A3B-Thinking outperforming competitors like GPT-5-High and Gemini 2.5 Pro on some key tests:
- MathVista: ERNIE (82.5) vs Gemini (82.3) and GPT (81.3)
- ChartQA: ERNIE (87.1) vs Gemini (76.3) and GPT (78.2)
- VLMs Are Blind: ERNIE (77.3) vs Gemini (76.5) and GPT (69.6)
It’s worth noting, of course, that AI benchmarks provide a guide but can be flawed. Always perform internal tests for your needs before deploying any AI model for mission-critical applications.
Baidu shifts from perception to automation with its latest ERNIE AI model
The primary hurdle for enterprise AI is moving from perception (“what is this?”) to automation (“what now?”). ERNIE 4.5 claims to address this by integrating visual grounding with tool use.
Asking the multimodal AI to find all people wearing suits in an image and return their coordinates in JSON format works. The model generates the structured data, a function easily transferable to a production line for visual inspection or to a system auditing site images for safety compliance.
The model also manages external tools and can autonomously zoom in on a photograph to read small text. If it faces an unknown object, it can trigger an image search to identify it. This represents a less passive form of AI that could power an agent to not only flag a data centre error, but also zoom in on the code, search the internal knowledge base, and suggest the fix.
Unlocking business intelligence with multimodal AI
Baidu’s latest ERNIE AI model also targets…
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