[AI]Baidu ERNIE multimodal AI beats GPT and Gemini in benchmarks

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’s latest ERNIE model, a super-efficient multimodal AI, is beating GPT and

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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.

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’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.

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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

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’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

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’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

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. Always perform internal tests for your needs before deploying any AI model for mission-critical applications.

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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

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’s latest ERNIE AI model also targets corporate video archives from training sessions and meetings to security footage. It can extract all on-screen subtitles and map them to their precise timestamps.

It also demonstrates temporal awareness, finding specific scenes (like those “filmed on a bridge”) by analysing visual cues. The clear end-goal is making vast video libraries searchable, allowing an employee to find the exact moment a specific topic was discussed in a two-hour webinar they may have dozed off a couple of times during.

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provides deployment guidance for several paths, including transformers, vLLM, and FastDeploy. However, the hardware requirements are a major barrier. A single-card deployment needs 80GB of GPU memory. This is not a tool for casual experimentation, but for organisations with existing and high-performance AI infrastructure.

For those with the hardware,

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’s ERNIEKit toolkit allows fine-tuning on proprietary data; a necessity for most high-value use cases.

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is providing its latest ERNIE AI model with an Apache 2.0 licence that permits commercial use, which is essential for adoption.

The market is finally moving toward multimodal AI that can see, read, and act within a specific business context, and the benchmarks suggest it’s doing so with impressive capability. The immediate task is to identify high-value visual reasoning jobs within your own operation and weigh them against the substantial hardware and governance costs.

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