Better Than GPT-5? We Try ERNIE X1.1, Baidu’s Latest AI Model

Take: The structure looks neat. Headings stay disciplined. Table formatting holds.

Image generation: reasoning-guided layout and variant control

• Goal: Design a 1080×1350 event poster, then create a clean variant.
• Why this matters: I expect ERNIE X1.1 to plan before rendering. Reasoning should guide tool calls.

Prompt:

“Design a 1080×1350 poster for ‘Data + Dharma 2025’. Blend a skyline, a temple silhouette, and a circuit board pattern. Place the hero title top-center. Put the subtitle bottom-left. Place sponsor logos bottom-right. Keep contrast high and text readable on mobile.”

Follow-up prompt:

“Create a second variant. Change subtitle to ‘Ancient Ideas. Modern Intelligence.’ Keep layout identical. Increase logo padding by 12 pixels.”

Output:

Take: ERNIE X1.1 clearly failed here. It seems like the model could not manage the nitty-gritties of the prompt, and took a long time to come up with an absolutely rubbish output.

Code Generation: FastAPI service for hallucination risk

• Goal: Ship a tiny service that scores hallucination risk. Include unit tests.
• Why this matters: I want scaffolds that run on the first try. ERNIE X1.1 should deliver a clean structure.

Prompt:

“Write a Python FastAPI service /risk that accepts {text, sources[]} and returns hallucination_risk in [0,1]. Use lexical overlap and a simple contradiction check. Include pytest unit tests. No external SaaS calls. Keep it production-readable.”

Output:

from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from typing import List

app = FastAPI()

class RiskRequest(BaseModel):
    text: str
    sources: List[str]

class RiskResponse(BaseModel):
    hallucination_risk: float

def calculate_lexical_overlap(text: str, sources: List[str]) -> float:
    """Calculate Jaccard similarity between text and sources"""
    text_words = set(text.lower().split())
    total_similarity = 0.0
    valid_sources = [s for s in sources if s.strip()]
    
    if not valid_sources:
        return 0.0
    
    for source in valid_sources:
        source_words = set(source.lower().split())
        intersection = text_words & source_words
        union = text_words | source_words
        similarity = len(intersection) / len(union) if union else 0
        total_similarity += similarity
    
    return total_similarity / len(valid_sources)

def calculate_contradiction(text: str, sources: List[str]) -> float:
    """Simple NLI-inspired contradiction detection"""
    if not sources:
        return 0.0
    
    negation_words = {'not', 'no', 'never', 'none', 'without'}
    contradiction_count = 0
    
    for source in sources:
        source_tokens = source.lower().split()
        text_tokens = text.lower().split()
        
        # Check for direct contradictions
        for i, token in enumerate(text_tokens):
            if token in negation_words and i+1 < len(text_tokens):
                if text_tokens[i+1] in source_tokens:
                    contradiction_count += 1
                    break
    
    return min(contradiction_count / len(sources), 1.0)

@app.post("/risk", response_model=RiskResponse)
def risk_endpoint(request: RiskRequest):
    """Main endpoint for hallucination risk calculation"""
    if not request.sources:
        raise HTTPException(status_code=400, detail="Sources list cannot be empty")
    
    lexical_overlap = calculate_lexical_overlap(request.text, request.sources)
    contradiction = calculate_contradiction(request.text, request.sources)
    hallucination_risk = 0.7 * lexical_overlap + 0.3 * contradiction
    
    return RiskResponse(hallucination_risk=round(min(max(hallucination_risk, 0.0), 1.0), 4))

# Unit tests
import pytest

def test_lexical_overlap():
    assert round(calculate_lexical_overlap("the quick brown fox", 
                ["the quick fox", "a slow fox"]), 3) == 0.458
    assert calculate_lexical_overlap("test", []) == 0.0
    assert round(calculate_lexical_overlap("hello world", 
                ["hello", "world"]), 3) == 0.500

def test_contradiction_detection():
    assert calculate_contradiction("the sky is not blue", 
                ["the sky is blue"]) == 1.0
    assert calculate_contradiction("I like apples", 
                ["I like oranges"]) == 0.0

def test_endpoint_validation():
    try:
        RiskRequest(text="test", sources=[])
        assert False, "Expected validation error"
    except HTTPException as e:
        assert e.status_code == 400

# Run tests if executed directly
if __name__ == "__main__":
    pytest.main([__file__])

Early Impressions

Here is my honest take so far – ERNIE X1.1 thinks a lot. It second-guesses many steps. Simple tasks sometimes trigger long internal reasoning, slowing straightforward outputs that you expect to be quick.

On some prompts, ERNIE X1.1 feels overcautious. It insists on planning beyond the task. The extra thinking sometimes hurts coherence. Short answers become meandering and unsure, just like a human overthinking.

When ERNIE X1.1 hits the groove, it behaves well. It respects format and section order, and can keep tables tight and codes neat. The “think time,” though, often feels heavy.

In my future use of it, I will tune prompts to curb this by reducing instruction ambiguity and adding stricter constraints. For everyday drafts, the extra thinking needs restraint. ERNIE X1.1 shows promise, but it must pace itself.

Limitations and Open Questions

Access outside China still involves friction on mobile. ERNIE X1.1 works best through the web or API interface. Pricing details remain unclear at launch. I also want external benchmark checks, as the vendor claims at the time of launch sound too bold to be accurate.

The “thinking” depth needs user control. A visible knob would possibly help in this regard. If it were to me, I would add a fast mode to the model for all those quick drafts and emails. Then again, a deep mode for agents and tools would be helpful as well. ERNIE X1.1 can benefit from clear distinctions.

Conclusion

ERNIE X1.1 aims for reliability, not flash. The claim is fewer hallucinations and better compliance. My runs show sturdy structure and decent code. Yet the model often overthinks. That hurts speed and coherence on simple asks.

I will keep testing with tighter prompts. I will lean on API paths for agents. If Baidu exposes “think” control, adoption will rise. Until then, ERNIE X1.1 stays in my toolkit for strict drafts and clean scaffolds. It just needs to breathe between thoughts.