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ERNIE 4.0 vs o3 Deep Research

How do these models stack up? Below is an expert side-by-side comparison of specifications, context window capacity, live pricing per million tokens, and standardized benchmark scores for ERNIE 4.0 and o3 Deep Research.

Baidu

ERNIE 4.0

ERNIE 4.0 (文心一言 4.0) is Baidu's most advanced large language model, representing the culmination of Baidu's decades of search and NLP expertise. Built on Baidu's proprietary knowledge-enhanced pre-training architecture, ERNIE 4.0 integrates deep understanding of Chinese language, culture, and domain-specific knowledge. It achieves GPT-4-class performance on comprehensive Chinese-language benchmarks spanning language understanding, reasoning, mathematics, and code generation. ERNIE 4.0 powers Baidu's Wenxin Yiyan consumer chatbot and is available via Baidu AI Cloud API for enterprise integrations.

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OpenAI

o3 Deep Research

o3-deep-research is OpenAI's advanced model for deep research, designed to tackle complex, multi-step research tasks. Note: This model always uses the 'web_search' tool which adds additional cost.

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Technical Specifications

SpecificationERNIE 4.0o3 Deep Research
ProviderBaiduOpenAI
Context Window128,000 tokens200,000 tokens
Agent Suitability87/100N/A
Time to First Token (TTFT)380 msN/A
Deployment Modelmanaged apimanaged api
Production Stabilitystablestable
API AvailableYesYes
Released Date2025-08-302025-10-10

API Pricing Comparison

Input Price per Million Tokens

ERNIE 4.0

$1.20

o3 Deep Research

$10.00

Output Price per Million Tokens

ERNIE 4.0

$2.40

o3 Deep Research

$40.00

Want to test both models live?

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Benchmark Performance Metrics

Scores show the raw performance percentages verified across key evaluation suites. Higher bars indicate superior accuracy and capability in that domain.

ERNIE 4.0 Quirks & Gotchas

  • β–ΈSuperior Chinese-language understanding β€” best-in-class for China-market applications
  • β–ΈAPI requires Baidu Cloud account with Chinese phone verification

o3 Deep Research Quirks & Gotchas

No developer gotchas reported.