Baidu ERNIE Multimodal AI Surpasses GPT and Gemini in Latest Benchmark Tests
Baidu has unveiled its latest ERNIE model, a groundbreaking multimodal AI that outshines GPT and Gemini in several critical benchmarks. The new model, known as ERNIE-4.5-VL-28B-A3B-Thinking, excels in processing enterprise data often overlooked by models focused solely on text.
Many companies struggle with valuable insights trapped in complex data forms like engineering schematics, factory video feeds, and medical scans. Baidu’s ERNIE is specifically designed to bridge this gap.
What sets this model apart for enterprise use is its "lightweight" architecture, which activates only three billion parameters during operation. This design addresses the significant costs associated with AI inference, positioning Baidu as a leader in efficiency and scalability. Training the system aims to pave the way for "multimodal agents" that can not only perceive but also reason and act.
Advanced visuals and capabilities
Baidu’s ERNIE model has shown outstanding performance in analyzing dense, non-text data. For instance, it can interpret a "Peak Time Reminder" chart to optimize visiting times, as seen in logistics and retail scenarios. Additionally, it can tackle technical challenges like solving bridge circuit diagrams using Ohm’s and Kirchhoff’s laws. These capabilities could revolutionize R&D and engineering departments by assisting in validating designs or elucidating complex schematics for new recruits.
Supporting Baidu’s claims are several benchmarks that show ERNIE-4.5 outperforming peers like GPT-5-High and Gemini 2.5 Pro:
- 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)
While benchmarks serve as useful guidelines, it’s crucial to conduct internal evaluations before committing to any AI model, especially for critical applications.
Transitioning from perception to automation
One of the primary challenges for enterprise AI is moving from mere perception ("what is this?") to actionable insights ("what now?"). ERNIE 4.5 addresses this by linking visual groundings with tool utilization. An example use case could involve the AI identifying and listing the coordinates of all individuals in suits from an image, with the potential application in production settings for visual inspections.
Moreover, the model can interact with external tools and autonomously zoom into photographs to extract small textual information. In instances of encountering unknown objects, it can initiate an image search. This represents a significant leap toward active AI capabilities capable of not just identifying issues but also facilitating solutions.
Unlocking business intelligence
ERNIE’s design also allows it to process corporate archives that include training videos, meetings, and security footage. The model can retrieve on-screen subtitles with precise timestamps and can even analyze specific scenes based on visual cues.
Baidu does provide various paths for deployment, from transformers to FastDeploy. However, a significant barrier remains: a single deployment requires 80GB of GPU memory, indicating that this technology is not intended for casual use but rather for organizations equipped with high-performance AI infrastructure.
For those with the necessary hardware, Baidu’s ERNIEKit enables fine-tuning on proprietary data, essential for maximizing high-value applications. Furthermore, ERNIE is offered under an Apache 2.0 license, allowing for commercial use, which is pivotal for broad adoption.
The landscape is shifting toward multimodal AI capable of visual understanding, reading, and acting within specific business contexts, and benchmarks suggest impressive potential. The immediate challenge for enterprises is to identify tasks where visual reasoning can add value while balancing the costs of hardware and governance.
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