Model Comparison
GLM-4.5 vs Qwen2.5 VL 32B Instruct
GLM-4.5 significantly outperforms across most benchmarks.
Performance Benchmarks
Comparative analysis across standard metrics
GLM-4.5 outperforms in 2 benchmarks (GPQA, MMLU-Pro), while Qwen2.5 VL 32B Instruct is better at 0 benchmarks.
GLM-4.5 significantly outperforms across most benchmarks.
Arena Performance
Human preference votes
Pricing Analysis
Price comparison per million tokens
Cost data unavailable.
Model Size
Parameter count comparison
GLM-4.5 has 321.5B more parameters than Qwen2.5 VL 32B Instruct, making it 959.7% larger.
Context Window
Maximum input and output token capacity
Only GLM-4.5 specifies input context (131,072 tokens). Only GLM-4.5 specifies output context (131,072 tokens).
Input Capabilities
Supported data types and modalities
Qwen2.5 VL 32B Instruct supports multimodal inputs, whereas GLM-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.
GLM-4.5
Qwen2.5 VL 32B Instruct
License
Usage and distribution terms
GLM-4.5 is licensed under MIT, 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.
MIT
Open weights
Apache 2.0
Open weights
Release Timeline
When each model was launched
GLM-4.5 was released on 2025-07-28, while Qwen2.5 VL 32B Instruct was released on 2025-02-28.
GLM-4.5 is 5 months newer than Qwen2.5 VL 32B Instruct.
Jul 28, 2025
8 months ago
5mo 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
GLM-4.5
View detailsZhipu AI
Qwen2.5 VL 32B Instruct
View detailsAlibaba Cloud / Qwen Team
Detailed Comparison
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FAQ
Common questions about GLM-4.5 vs Qwen2.5 VL 32B Instruct