Baidu ERNIE Outperforms GPT and Gemini in Multimodal AI Benchmarks

Baidu is making waves in the artificial intelligence landscape with its latest ERNIE (Enhanced Representation through Knowledge Integration) model, a sophisticated multimodal AI that’s showing strong performance against industry frontrunners like GPT and Gemini on key benchmarks. While much attention is focused on text-based AI, ERNIE is specifically designed to tackle the wealth of enterprise data often overlooked – the visual troves locked in schematics, video feeds, and complex dashboards.

This new iteration, ERNIE-4.5-VL-28B-A3B-Thinking, aims to bridge the gap between AI perception and practical application. For enterprise architects, the model’s efficiency is as compelling as its multimodal capability. Employing a “lightweight” architecture, it activates only a fraction of its total parameters (around three billion) during operation. This design choice directly addresses the often-prohibitive inference costs that can derail ambitious AI scaling initiatives. Baidu’s strategy hinges on efficiency, positioning ERNIE as a foundational model for “multimodal agents” capable of reasoning and acting, not just observing. This approach could significantly lower the barrier to entry for businesses looking to deploy AI solutions.

Complex Visual Data Analysis Capabilities Supported by AI Benchmarks

ERNIE excels at interpreting dense, non-textual data, a critical ability for many industries. For instance, it can analyze a “Peak Time Reminder” chart to determine optimal visiting hours – a valuable asset for resource scheduling in logistics and retail. This capability translates to improved efficiency and resource allocation.

The model also demonstrates aptitude in technical domains. It can analyze and solve bridge circuit diagrams using Ohm’s and Kirchhoff’s laws. For R&D and engineering departments, this means a potential AI assistant that can validate designs or efficiently explain complex schematics to new employees – significantly reducing training time and improving accuracy.

Baidu’s internal benchmarks support these claims, with ERNIE-4.5-VL-28B-A3B-Thinking outperforming competitors like GPT-5-High and Gemini 2.5 Pro on select 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 crucial to remember that AI benchmarks are not a definitive measure of performance; they serve as a guide. Organizations should conduct thorough internal testing based on their specific needs and datasets before deploying any AI model for mission-critical applications. The ideal solution will vary based on factors such as dataset size, specific performance requirements, and acceptable error rates.

Baidu Shifts from Perception to Automation with its Latest ERNIE AI Model

A major obstacle for enterprise AI adoption is the transition from simple perception (“what is this?”) to actionable automation (“what now?”). ERNIE 4.5 attempts to overcome this hurdle by integrating visual grounding with tool utilization. This means the model can not only identify objects but also understand their context and interact with other systems to achieve a specific goal.

For instance, the model can identify all people wearing suits in an image and return their coordinates in JSON format – a function easily adaptable to a production line for visual inspection or to a system auditing site images for safety compliance. The key is the model’s ability to generate structured data from visual input, enabling seamless integration with existing business systems.

ERNIE can also manage external tools, such as autonomously zooming in on a photograph to decipher small text. If it encounters an unknown object, it can initiate an image search to identify it. This marks a move toward a more proactive AI that could not only flag a data center error but also zoom in on the code, search the internal knowledge base, and propose a solution. This level of automation has the potential to significantly reduce downtime and improve operational efficiency.

Unlocking Business Intelligence with Multimodal AI

Baidu’s ERNIE also targets the untapped potential of corporate video archives, ranging from training sessions and meetings to security footage. The model can extract all on-screen subtitles and map them to their precise timestamps, creating a searchable index of the video content.

Furthermore, ERNIE demonstrates temporal awareness, allowing it to identify specific scenes (such as those “filmed on a bridge”) by analyzing visual cues. The ultimate goal is to transform vast video libraries into searchable resources, enabling employees to pinpoint the exact moment a specific topic was discussed in a long webinar – enhancing knowledge sharing and information retrieval within the organization.

Baidu provides deployment guidance for various platforms, including transformers, vLLM, and FastDeploy. However, the hardware requirements represent a significant barrier. A single-card deployment necessitates 80GB of GPU memory. Therefore, this is not a tool designed for casual experimentation but rather for organizations that have already invested in high-performance AI infrastructure. This suggests that ERNIE adoption will initially be driven by larger enterprises with established AI teams and budgets.

For organizations with the necessary hardware, Baidu’s ERNIEKit toolkit allows fine-tuning on proprietary data – a crucial step for realizing high-value use cases. Baidu is offering its latest ERNIE AI model with an Apache 2.0 license, permitting commercial use, a critical factor for widespread adoption and encouraging innovation on top of the platform.

The market is undeniably shifting toward multimodal AI capable of seeing, reading, and acting within specific business contexts. The benchmarks suggest that ERNIE is doing so with considerable capability. The immediate challenge is to identify high-impact visual reasoning tasks within your own organization and carefully evaluate them against the substantial hardware and governance costs. A proof-of-concept project focused on a specific use case could provide valuable insights before making a larger investment.