Model Comparison
ERNIE 4.5 vs Qwen2.5 VL 32B Instruct
Qwen2.5 VL 32B Instruct shows notably better performance in the majority of benchmarks.
Performance Benchmarks
Comparative analysis across standard metrics
ERNIE 4.5 outperforms in 1 benchmarks (GPQA), while Qwen2.5 VL 32B Instruct is better at 3 benchmarks (MATH, MMLU, MMLU-Pro).
Qwen2.5 VL 32B Instruct shows notably better performance in the majority of benchmarks.
Arena Performance
Human preference votes
Pricing Analysis
Price comparison per million tokens
Cost data unavailable.
Model Size
Parameter count comparison
Qwen2.5 VL 32B Instruct has 12.5B more parameters than ERNIE 4.5, making it 59.5% larger.
Context Window
Maximum input and output token capacity
Only ERNIE 4.5 specifies input context (128,000 tokens). Only ERNIE 4.5 specifies output context (65,536 tokens).
Input Capabilities
Supported data types and modalities
Qwen2.5 VL 32B Instruct supports multimodal inputs, whereas ERNIE 4.5 does not.
Qwen2.5 VL 32B Instruct can handle both text and other forms of data like images, making it suitable for multimodal applications.
ERNIE 4.5
Qwen2.5 VL 32B Instruct
License
Usage and distribution terms
ERNIE 4.5 is licensed under a proprietary license, while Qwen2.5 VL 32B Instruct uses Apache 2.0.
License differences may affect how you can use these models in commercial or open-source projects.
Proprietary
Closed source
Apache 2.0
Open weights
Release Timeline
When each model was launched
ERNIE 4.5 was released on 2025-06-25, while Qwen2.5 VL 32B Instruct was released on 2025-02-28.
ERNIE 4.5 is 4 months newer than Qwen2.5 VL 32B Instruct.
Jun 25, 2025
10 months ago
3mo newerFeb 28, 2025
1.1 years ago
Knowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Outputs Comparison
Key Takeaways
Qwen2.5 VL 32B Instruct
View detailsAlibaba Cloud / Qwen Team
Detailed Comparison
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FAQ
Common questions about ERNIE 4.5 vs Qwen2.5 VL 32B Instruct