Understanding its Capabilities and Design

Introduction

Baidu, a leading Chinese technology has been a significant player in artificial intelligence (AI) since it began developing its Enhanced Representation through Knowledge Integration (ERNIE) models in 2019. On March 16, 2025, Baidu introduced ERNIE X1, a specialized deep-thinking reasoning model, alongside ERNIE 4.5, a multimodal foundation model. This blog focuses on exploring its purpose, technical foundations, capabilities, and position in the competitive AI landscape.

What is ERNIE X1?

ERNIE X1 is Baidu’s first multimodal deep-thinking reasoning model, designed to tackle complex tasks requiring logical reasoning, planning, and problem-solving. Unlike general-purpose language models that prioritize quick, pattern-based responses, ERNIE X1 emphasizes structured thought processes, making it suitable for advanced applications such as mathematical computations, complex coding, and analytical tasks. It is positioned as a competitor to models like DeepSeek’s R1 and OpenAI’s o1, with Baidu claiming it delivers comparable performance at a lower cost.

Key Features

Multimodal Capabilities: ERNIE X1 processes text and images, enabling it to handle diverse tasks such as image understanding, document-based question answering, and text analysis.
Reasoning and Transparency: The model explicitly articulates its reasoning process, providing step-by-step explanations for its conclusions. This transparency is valuable for applications requiring trust and clarity, such as business decision-making, scientific research, and legal analysis.
Tool Integration: ERNIE X1 supports a range of tools, including advanced search, code interpretation, academic and business information retrieval, and TreeMind mapping, enhancing its utility across professional domains.
Cost Efficiency: Baidu emphasizes ERNIE X1’s competitive pricing, with input costs at approximately $0.28 per million tokens and output costs at $1.10 per million tokens for enterprise users, reportedly half the cost of DeepSeek’s R1.

Technical Foundations

ERNIE X1’s capabilities are underpinned by several advanced technologies:

Progressive Reinforcement Learning Method: This approach refines the model’s decision-making by iteratively improving its responses based on feedback, enhancing accuracy and reliability.
End-to-End Training with Chains of Thought and Action: By integrating reasoning steps and actions into its training process, ERNIE X1 learns to break down complex problems systematically.
Unified Multi-Faceted Reward System: This system evaluates the model’s performance across multiple dimensions, ensuring balanced improvements in understanding, planning, and execution.

These technologies build on Baidu’s PaddlePaddle deep learning platform, which supports efficient training and inference. While specific architectural details, such as the number of parameters, are not publicly disclosed, ERNIE X1 likely leverages transformer-based architectures, similar to its predecessors, enhanced with knowledge integration techniques.

Applications and Use Cases

Given its design, ERNIE X1 is said to be versatile for various professional and academic applications:

Chinese Knowledge Q&A: Optimized for Chinese language processing, it is said that ERNIE X1 excels in answering queries requiring deep cultural or contextual understanding, making it valuable for education and customer service in Chinese-speaking regions.
Complex Calculations and Coding: The model’s reasoning capabilities are reported to support tasks like mathematical problem-solving and code generation, with logical explanations that aid developers and researchers.
Literary and Manuscript Writing: ERNIE X1 is said to assist in drafting coherent and contextually rich content, which is useful for creative and professional writing.
Business and Research Tools: Given its support for tools like academic search and business information retrieval, ERNIE X1 is considered a powerful assistant for data-driven decision-making and research.

Accessibility

ERNIE X1 is accessible to individual users for free through Baidu’s ERNIE Bot platform (https://yiyan.baidu.com). For enterprise users and developers, it will soon be available via APIs on Baidu AI Cloud’s Qianfan Model-as-a-Service platform. Baidu plans to integrate ERNIE X1 into its broader ecosystem, including Baidu Search and the Wenxiaoyan app, to enhance user experiences across its services.

However, access to ERNIE X1 is significantly limited for users outside China due to the requirement of a Chinese phone number (+86) for registration on the ERNIE Bot platform. This restriction effectively prevents most non-Chinese users from creating an account and using the model, as obtaining a valid Chinese phone number is challenging for those not residing in China. Additionally, the platform’s interface is predominantly in Chinese, with minimal English support, posing further challenges for non-Chinese speakers attempting to navigate and utilize ERNIE X1.

ERNIE models capabilities testing based of a Video

“Due to accessibility constraints, I wasn’t able to directly use the ERNIE model. However, the following demonstrations were highlighted in this video showcasing ERNIE’s capabilities https://www.youtube.com/watch?v=z46KBYbcpmo.

ERNIE 4.5 (The Multimodal Powerhouse):

Video-to-Text (Recipe Generation): Given a short video clip of someone making sugar art, it successfully identified the activity (traditional Chinese sugar painting of a dragon) and generated a detailed recipe for making something similar.
Document Analysis (PDF Summary): It processed an uploaded PDF financial report and accurately summarized the key information related to the cloud business segment within that document.
Image Analysis (Chart Explanation): It analyzed an uploaded image containing benchmark performance charts and explained what the chart represented, comparing the performance of different language models across various tests.
Audio Analysis (Context Identification): Given an MP3 audio clip, it identified the specific scene from the “Dream of the Red Chamber” novel that the clip depicted, describing the characters and context.
Coding (Web App Development): It generated the necessary HTML, CSS, and JavaScript code to build a functional responsive web application for tracking monthly income and expenses, including features like input, editing, deletion, categorization, and data visualization (charts). The video showed the resulting functional app.
Coding (Game Simulation): It generated Python code to create a simulation of Conway’s Game of Life, which was shown running successfully.
Coding (SVG Graphics): It generated SVG (Scalable Vector Graphics) code to represent a butterfly with symmetrical wings and simple styling. The video showed the resulting butterfly image rendered from the code.
Coding (Debugging): It identified logical errors and potential runtime issues within a provided Python function and then generated the corrected version of the code, explaining the fixes.

ERNIE X1 (The Reasoning Specialist):

Mathematical Word Problem (Trains): Solved a classic word problem involving two trains traveling towards each other at different speeds from different starting times (with one having a scheduled stop) to determine when and where they meet. It showed its step-by-step reasoning process.
Geometric Word Problem (Farmer’s Field): Solved a problem requiring finding the length of a line that divides a triangular field (with given side lengths) into two regions of equal area. This involves geometry and potentially calculus or specific theorems.
Combinatorics/Constraint Problem (Library Purchase): Determined the possible combinations of workbooks, app licenses, and science kits a library could purchase to total exactly $250, given individual item costs and the constraint that at least one of each item must be bought. It correctly concluded (after much reasoning) that there were no combinations that satisfy all conditions exactly.
Logical Deduction Puzzle (Truth-tellers & Liars): Solved a logic puzzle involving three people (A, B, C), where each is either a truth-teller or a liar, based on statements they make about each other. It deduced who was the liar and who were the truth-tellers by analyzing contradictions and consistent scenarios.

Challenges and Considerations

Baidu claims ERNIE X1 matches the performance of DeepSeek’s R1, a highly regarded reasoning model, at half the cost. In contrast, ERNIE 4.5, its multimodal counterpart, has demonstrated strong performance, reportedly surpassing OpenAI’s GPT-4.5 in multiple benchmarks while costing only 1% as much.

Despite these impressive claims of strong multimodal performance, advanced reasoning capabilities, and significantly lower costs, ERNIE X1 and ERNIE 4.5 have not yet achieved widespread international recognition or adoption. There are several reasons that might explain this:

Limited Accessibility: As discussed before, access to ERNIE models is heavily restricted outside China. Individual users need a Chinese number and the platform interface is primarily in Chinese with minimal English support. This barrier makes it difficult for non-Chinese researchers, developers, and companies to evaluate or integrate Baidu’s models into their workflows.
Localization Focus: Baidu has optimized ERNIE models, especially ERNIE X1, for Chinese language and context understanding. While this results in exceptional performance in Chinese applications, it means that English-language tasks, the dominant benchmark for global LLM comparisons may not showcase the full potential of ERNIE models to an international audience.
Geopolitical and Trust Factors:In the current global tech environment, geopolitical tensions can impact the adoption of Chinese-developed AI technologies. Concerns around data privacy, security, and regulatory differences might cause businesses and institutions outside China to hesitate before adopting Baidu’s solutions, even if they are technically competitive.
Late International Positioning:Baidu only recently started highlighting its models’ capabilities to a broader audience. By contrast, companies like OpenAI, Anthropic, and Google have actively courted the international AI community for years, building brand trust and familiarity. Baidu is still in the early stages of establishing an international presence for ERNIE X1 and ERNIE 4.5.
Model Evaluation Transparency: While Baidu has shared promising benchmark results, detailed independent evaluations, whitepapers, or open access model cards — common in the Western AI ecosystem — are limited. For international researchers and enterprises, transparent benchmarking and reproducibility are critical before adopting a model into production.

Conclusion

ERNIE X1 and ERNIE 4.5 showcase Baidu’s impressive strides in AI, offering strong reasoning, multimodal capabilities, and significant cost advantages. However, limited accessibility, language focus, and global trust barriers have so far kept them from gaining the international attention their technical achievements deserve. With the right moves, Baidu’s ERNIE models have the potential to become major players on the world stage.