Model Comparison
GLM-4.6 vs Qwen2.5-Omni-7B
GLM-4.6 significantly outperforms across most benchmarks.
Performance Benchmarks
Comparative analysis across standard metrics
GLM-4.6 outperforms in 1 benchmarks (GPQA), while Qwen2.5-Omni-7B is better at 0 benchmarks.
GLM-4.6 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-4.6 has 350.0B more parameters than Qwen2.5-Omni-7B, making it 5000.0% larger.
Context Window
Maximum input and output token capacity
Only GLM-4.6 specifies input context (131,072 tokens). Only GLM-4.6 specifies output context (131,072 tokens).
Input Capabilities
Supported data types and modalities
Both GLM-4.6 and Qwen2.5-Omni-7B support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
GLM-4.6
Qwen2.5-Omni-7B
License
Usage and distribution terms
GLM-4.6 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-4.6 was released on 2025-09-30, while Qwen2.5-Omni-7B was released on 2025-03-27.
GLM-4.6 is 6 months newer than Qwen2.5-Omni-7B.
Sep 30, 2025
7 months ago
6mo 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-4.6
View detailsZhipu AI
Qwen2.5-Omni-7B
View detailsAlibaba Cloud / Qwen Team
Detailed Comparison
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FAQ
Common questions about GLM-4.6 vs Qwen2.5-Omni-7B