Model Comparison
GLM-4.7 vs Qwen3 VL 32B Thinking
GLM-4.7 significantly outperforms across most benchmarks.
Performance Benchmarks
Comparative analysis across standard metrics
GLM-4.7 outperforms in 4 benchmarks (AIME 2025, GPQA, LiveCodeBench v6, MMLU-Pro), while Qwen3 VL 32B Thinking is better at 0 benchmarks.
GLM-4.7 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-4.7 has 325.0B more parameters than Qwen3 VL 32B Thinking, making it 984.8% larger.
Context Window
Maximum input and output token capacity
Only GLM-4.7 specifies input context (202,800 tokens). Only GLM-4.7 specifies output context (131,072 tokens).
Input Capabilities
Supported data types and modalities
Both GLM-4.7 and Qwen3 VL 32B Thinking support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
GLM-4.7
Qwen3 VL 32B Thinking
License
Usage and distribution terms
GLM-4.7 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-4.7 was released on 2025-12-22, while Qwen3 VL 32B Thinking was released on 2025-09-22.
GLM-4.7 is 3 months newer than Qwen3 VL 32B Thinking.
Dec 22, 2025
3 months ago
3mo 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-4.7
View detailsZhipu AI
Qwen3 VL 32B Thinking
View detailsAlibaba Cloud / Qwen Team
Detailed Comparison
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FAQ
Common questions about GLM-4.7 vs Qwen3 VL 32B Thinking