Baidu’s ERNIE X1.1 New Reasoning Model

Baidu has just revealed its brand-new reasoning model, ERNIE X1.1, and it is already attracting a lot of attention. According to the company, this model represents a leap forward in reasoning, factual grounding, and agent-style intelligence. In other words, it is designed not only to answer questions but to think through problems, step by step.

The name “BU BU” has a symbolic meaning. It comes from a poem about searching endlessly before finding answers. The term “BU” signifies a hundred times or countless times, which perfectly fits the model’s mission of tirelessly navigating complex problems until it reaches the correct solution.

In this article, I’ll guide you step by step through ERNIE X1.1, its features, how it works, and my experience testing it in real time. This will help you understand the model better and see how it can be applied to various tasks.

What Makes ERNIE X1.1 Unique

ERNIE X1.1 isn’t just another language model. It was created with a strong focus on reasoning and factual accuracy.

ERNIE X1.1 Reasoning Model

The three key pillars that define this model are:

Factuality
- Built using reinforcement learning for knowledge consistency, which ensures that the model sticks to facts and verified information.
Instruction Following
- It uses a checklist-driven verification process, ensuring the instructions given to the model are properly followed.
Agentic Capabilities
- It is designed for multi-round reinforcement learning (RL), allowing it to plan actions and reasoning steps across complex tasks.

Under the hood, ERNIE X1.1 builds on ERNIE 4.5 but has been upgraded with end-to-end reinforcement learning, mid-training refinement, and post-training alignment. These upgrades allow it to handle complex, multi-layered reasoning while staying consistent and fact-based.

Getting Started with ERNIE X1.1

To begin testing the model, you can access it through the official platform

Once you’re on the site, you can start by entering prompts and exploring the model’s reasoning capabilities.

Step-by-Step Testing of ERNIE X1.1

I ran several real-time tests to see how ERNIE X1.1 performs across different scenarios, including reasoning, coding, and multilingual translation.

Step 1: Testing Multi-Layered Reasoning

For the first test, I gave ERNIE X1.1 a complex philosophical and cultural prompt.

The Prompt

I asked the model to:

Explain the deeper meaning of a poem line:

“Having searched hundreds of times in the crowd, suddenly turning back, she is there in the dim candlelight.”
Draw a parallel between the poem's imagery and the mission of an AI reasoning model like ERNIE X1.1.
Propose a futuristic scenario, set ten years from now, where an AI model based on this philosophy helps humanity.

The Model’s Response Process

ERNIE X1.1 broke the task into steps:
1. Understand the context of the poem.
2. Interpret the meaning.
3. Relate it to AI reasoning and problem-solving.
4. Create a futuristic scenario.
It demonstrated step-by-step chain-of-thought reasoning, which was concise and grounded in facts.

The response showed that ERNIE X1.1:

Fully understood the historical and philosophical context (it even referenced the Song Dynasty accurately).
Made modern parallels between the poem’s seeker and the AI model’s mission.
Considered the current year (2025) while describing a futuristic 10-year scenario.

This test proved that the model is creative, coherent, and factually reliable.

Step 2: Coding Task with ERNIE X1.1

Next, I tested its ability to generate code by asking it to create a self-contained HTML file using p5.js.

The Task:

Generate code for a rocket that:
- Moves smoothly across the screen.
- Explodes into multiple colored particles.
- Leaves a trail behind as it moves.

How the Model Worked:

ERNIE X1.1 planned the solution step by step:
1. Identify necessary components for the rocket animation.
2. Include particle physics to handle movement and explosion effects.
3. Ensure smooth rendering without errors.

The final output was accurate:

The rocket moved as intended.
Particles were generated during explosions.
The background matched the request.
It followed instructions carefully, demonstrating strong coding skills.

Step 3: Multilingual Translation Test

To test its multilingual capabilities, I gave the model a complex translation challenge.

The Prompt:

Translate the sentence:

“Clinging to the past is like holding on to smoke.”
Translate it into a wide range of languages:
- Popular languages like Spanish, French, Arabic, Russian, and Hindi.
- Obscure regional languages.
- Fictional languages like Elder Futhark (runic script) and a completely made-up language.
Provide cultural notes for three chosen languages.

How the Model Handled It:

The model analyzed the prompt carefully and planned its workflow:
1. Identify which languages require real translations.
2. Handle fictional languages creatively.
3. Add cultural notes for three languages.
It automatically decided when to use web search tools and when to rely on its internal knowledge.
For obscure or fictional languages, it generated creative responses without external help.

Results:

Accurate translations for well-known languages.
Creative construction for fictional languages.
Cultural notes added depth and context:
- Hindi: Focused on the concept of impermanence.
- French: Related to literature and sayings.
- Spanish: Included relevant proverbs.

This test highlighted the model’s multilingual strength and its ability to adapt to unusual requests.

How ERNIE X1.1 Works Step-by-Step

Below is a simplified workflow showing how ERNIE X1.1 processes complex tasks.

Step	What ERNIE X1.1 Does
1. Understand Prompt	Breaks down the user query into smaller tasks.
2. Plan Actions	Creates a clear action plan before responding.
3. Execute Reasoning	Solves each step logically and sequentially.
4. Verify Facts	Uses reinforcement learning to ensure factual accuracy.
5. Produce Final Output	Combines reasoning and factual data into a coherent response.

This process is especially evident in multi-round reasoning tasks, where the model acts like an intelligent agent capable of planning and correcting itself.

Key Features of ERNIE X1.1

Here are the standout features based on my testing:

Step-by-step problem-solving: It clearly shows its chain of thought, making the reasoning process transparent.
Factually grounded responses: It focuses on accuracy through reinforcement learning.
Multi-round conversations: Can handle extended dialogues where each response builds on the previous one.
Strong coding skills: Generates well-structured, functional code for complex tasks.
Multilingual and cultural understanding: Supports a wide range of languages, including fictional ones.
Agent-style intelligence: Makes decisions about when to search online, when to rely on internal knowledge, and how to manage complex workflows.

Challenges During Testing

While testing ERNIE X1.1, I noticed technical glitches caused by heavy server load.

At times, responses were delayed or interrupted.
This is likely due to high demand and limited GPU availability, as the model was recently launched.

These glitches don’t reflect the model’s capabilities but rather the current infrastructure limits.

Step-by-Step Guide to Using ERNIE X1.1

Here’s how you can get started with ERNIE X1.1:

Step 1: Visit the Platform

Go to ernie.baidu.com.

Step 2: Create an Account

Step 3: Choose a Task

Reasoning task
Coding challenge
Translation project
Philosophical exploration

Step 4: Enter Your Prompt

Be clear and specific. Example: “Explain the cultural meaning of a poem and link it to AI reasoning.”

Step 5: Review the Model’s Chain of Thought

Watch as ERNIE X1.1:

Breaks down the task.
Plans each step.
Produces a coherent final output.

Step 6: Verify Results

Check the factual accuracy and test any generated code or translations.

Future Potential of ERNIE X1.1

Based on its current capabilities, ERNIE X1.1 has the potential to:

Assist researchers with complex problem-solving.
Support developers with automated coding.
Help language learners with accurate translations and cultural insights.
Drive innovation in areas like AI planning and autonomous systems.

Baidu has hinted at future open-source releases, which could make ERNIE X1.1 even more accessible to the global developer community.

Final Thoughts

ERNIE X1.1 stands out as one of the most powerful reasoning models I’ve tested. From philosophical reasoning to coding and multilingual translation, it excels in a wide variety of tasks.

Key Takeaways:

Built for thinking, not just answering.
Strong focus on factual accuracy and instruction-following.
Effective at multi-step problem solving and planning.
Capable of handling real-world and creative challenges.

While technical glitches currently limit its online performance, the model’s underlying capabilities are impressive. As Baidu continues to refine ERNIE X1.1, it has the potential to become a vital tool for developers, researchers, and anyone working with complex reasoning tasks.