Exploring ERNIE 4.5: Baidu's Multimodal AI Breakthrough

ERNIE 4.5 is Baidu's advanced AI model, excelling in processing text, images, audio, and video.
It likely outperforms models like GPT-4.5 in multimodal tasks, though coding performance varies.
Research suggests it's cost-effective and open-sourced, enhancing accessibility for developers and researchers.

Introduction and Background

On June 30, 2025, Baidu, a leading Chinese tech company known for its search engine and AI innovations, open-sourced its latest multimodal large language model, ERNIE 4.5. This model is part of the ERNIE (Enhanced Representation through Knowledge Integration) series, aiming to advance AI's ability to process and generate content across multiple modalities, including text, images, audio, and video. This survey note provides a detailed examination of ERNIE 4.5, covering its technical specifications, performance, comparisons, applications, and accessibility.

Technical Specifications and Architecture

ERNIE 4.5 is a family of large-scale multimodal models comprising 10 distinct variants, including Mixture-of-Experts (MoE) models with 47B and 3B active parameters, the largest having 424B total parameters, and a 0.3B dense model. Its novel heterogeneous modality structure supports parameter sharing across modalities while allowing dedicated parameters for each, enhancing multimodal understanding without compromising text-related performance. Trained using the PaddlePaddle deep learning framework, it achieves 47% Model FLOPs Utilization (MFU), ensuring high-performance inference and streamlined deployment.

The model variants and their specifications are detailed below:

Model	Category	Input Modality	Output Modality	Context Window
ERNIE-4.5-300B-A47B-Base	LLMs	Text	Text	128K
ERNIE-4.5-300B-A47B	LLMs	Text	Text	128K
ERNIE-4.5-21B-A3B-Base	LLMs	Text	Text	128K
ERNIE-4.5-21B-A3B	LLMs	Text	Text	128K
ERNIE-4.5-VL-424B-A47B-Base	VLMs	Text/Image/Video	Text	-
ERNIE-4.5-VL-424B-A47B	VLMs	Text/Image/Video	Text	-
ERNIE-4.5-VL-28B-A3B-Base	VLMs	Text/Image/Video	Text	-
ERNIE-4.5-VL-28B-A3B	VLMs	Text/Image/Video	Text	-
ERNIE-4.5-0.3B-Base	Dense Models	Text	Text	-
ERNIE-4.5-0.3B	Dense Models	Text	Text	-

This architecture, detailed in the ERNIE 4.5 Technical Report, underscores its scalability and efficiency through multimodal heterogeneous MoE pre-training, using modality-isolated routing, router orthogonal loss, and multimodal token-balanced loss.

Key Features and Capabilities

ERNIE 4.5's multimodal processing capability allows it to handle diverse data types, making it suitable for complex tasks requiring integrated understanding. It exhibits advanced language understanding, including humor and cultural references, enhancing interaction naturalness. Its logical reasoning and coding abilities are significantly improved, supported by modality-specific post-training using Supervised Fine-tuning (SFT), Direct Preference Optimization (DPO), and Unified Preference Optimization (UPO). The model excels in instruction following, world knowledge, visual understanding, and multimodal reasoning, as noted in Baidu open-sources Ernie 4.5 multimodal AI model.

Performance and Benchmark Comparisons

ERNIE 4.5's performance has been benchmarked against leading models like OpenAI's GPT-4.5, GPT-4o, and DeepSeek-V3. According to X post by Baidu, it leads GPT-4o by 3.85 points in average multimodal scores (77.77 vs. 73.92), excelling in tasks like CCBench (~81 vs. ~79), OCRBench (~88 vs. ~81), and ChartQA (~82 vs. ~81). However, it lags in MMMU (~64 vs. ~70) and GPQA (~57 vs. ~61) compared to GPT-4.5.

Ernie-4.5 vs GPT-4o benchmark comparison Reference: Baidu

In text-only tasks, ERNIE 4.5 scores 79.6 against GPT-4.5's 79.14, leading by 0.46 points, and outperforms DeepSeek-V3 (~77) in average scores. It also shows strength in Chinese language benchmarks like C-Eval (~88 vs. ~80) and CMMLU (~88 vs. ~80). However, in coding tasks like LiveCodeBench, it scores ~35 compared to GPT-4.5's ~45, indicating weaker performance, as detailed in Baidu’s Ernie 4.5 Outperforms GPT 4.5 By A Mile. There is controversy, with claims of cherry-picking benchmarks, as noted in an Ycombinator forum discussion (GPT 4.5 level for 1% of the price on Hacker News), suggesting caution in interpreting results.

Use Cases and Applications

ERNIE 4.5 is poised for diverse applications:

Content Creation: Generating multimedia outputs for creative projects.
Customer Support: Enhancing chatbots with multimodal interactions.
Education: Providing multimedia explanations for accessible learning.
Research: Analyzing multimodal data, such as scientific papers with figures.
Entertainment: Creating interactive multimedia experiences.

These applications are supported by its versatility, as suggested in Baidu Releases ERNIE 4.5 & X1 Models Outperforming GPT-4.5 for 1% the Cost.

Accessibility and Usage

Baidu has made ERNIE 4.5 highly accessible, aligning with its open-source release on June 30, 2025, as reported in Baidu Makes a Major Open-Source Release of the ERNIE Bot 4.5. Users can interact via the free ERNIE Bot platform at ERNIE Bot official website. Developers can integrate it through APIs on Baidu AI Cloud's Qianfan platform, and the open-source version is available on GitHub (Baidu ERNIE GitHub Repository), supporting fine-tuning with ERNIEKit and deployment with FastDeploy, both based on PaddlePaddle. This accessibility is detailed in the official blog post at ERNIE 4.5 Blog Post.

Conclusion and Future Implications

ERNIE 4.5 represents a significant advancement in AI, particularly in multimodal processing, with its open-source release fostering global innovation. Its performance in benchmarks, while strong, should be interpreted cautiously due to potential biases in comparisons. As AI evolves, ERNIE 4.5's capabilities will likely influence various sectors, making it a model worth exploring for developers, researchers, and enthusiasts alike.