Model Comparison
GLM-5 vs Qwen3 VL 32B Thinking
Comparing GLM-5 and Qwen3 VL 32B Thinking across benchmarks, pricing, and capabilities.
Performance Benchmarks
Comparative analysis across standard metrics
GLM-5 and Qwen3 VL 32B Thinking don't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.
Arena Performance
Human preference votes
Pricing Analysis
Price comparison per million tokens
Cost data unavailable.
Model Size
Parameter count comparison
GLM-5 has 711.0B more parameters than Qwen3 VL 32B Thinking, making it 2154.5% larger.
Context Window
Maximum input and output token capacity
Only GLM-5 specifies input context (200,000 tokens). Only GLM-5 specifies output context (128,000 tokens).
Input Capabilities
Supported data types and modalities
Qwen3 VL 32B Thinking supports multimodal inputs, whereas GLM-5 does not.
Qwen3 VL 32B Thinking can handle both text and other forms of data like images, making it suitable for multimodal applications.
GLM-5
Qwen3 VL 32B Thinking
License
Usage and distribution terms
GLM-5 is licensed under MIT, while Qwen3 VL 32B Thinking 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-5 was released on 2026-02-11, while Qwen3 VL 32B Thinking was released on 2025-09-22.
GLM-5 is 5 months newer than Qwen3 VL 32B Thinking.
Feb 11, 2026
2 months ago
4mo newerSep 22, 2025
6 months 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-5
View detailsZhipu AI
Qwen3 VL 32B Thinking
View detailsAlibaba Cloud / Qwen Team
Detailed Comparison
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FAQ
Common questions about GLM-5 vs Qwen3 VL 32B Thinking