Model Comparison
GLM-4.5V vs Qwen2.5-Omni-7B
Comparing GLM-4.5V and Qwen2.5-Omni-7B across benchmarks, pricing, and capabilities.
Performance Benchmarks
Comparative analysis across standard metrics
GLM-4.5V and Qwen2.5-Omni-7B 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-4.5V has 101.0B more parameters than Qwen2.5-Omni-7B, making it 1442.9% larger.
Context Window
Maximum input and output token capacity
Only GLM-4.5V specifies input context (131,072 tokens). Only GLM-4.5V specifies output context (131,072 tokens).
Input Capabilities
Supported data types and modalities
Both GLM-4.5V and Qwen2.5-Omni-7B support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
GLM-4.5V
Qwen2.5-Omni-7B
License
Usage and distribution terms
GLM-4.5V is licensed under MIT, while Qwen2.5-Omni-7B 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-4.5V was released on 2025-08-11, while Qwen2.5-Omni-7B was released on 2025-03-27.
GLM-4.5V is 5 months newer than Qwen2.5-Omni-7B.
Aug 11, 2025
8 months ago
4mo newerMar 27, 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-4.5V
View detailsZhipu AI
Qwen2.5-Omni-7B
View detailsAlibaba Cloud / Qwen Team
Detailed Comparison
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FAQ
Common questions about GLM-4.5V vs Qwen2.5-Omni-7B