Model Comparison
GLM-4.6 vs Qwen2.5 14B Instruct
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 14B Instruct 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 342.3B more parameters than Qwen2.5 14B Instruct, making it 2328.6% 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
GLM-4.6 supports multimodal inputs, whereas Qwen2.5 14B Instruct does not.
GLM-4.6 can handle both text and other forms of data like images, making it suitable for multimodal applications.
GLM-4.6
Qwen2.5 14B Instruct
License
Usage and distribution terms
GLM-4.6 is licensed under MIT, while Qwen2.5 14B 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-4.6 was released on 2025-09-30, while Qwen2.5 14B Instruct was released on 2024-09-19.
GLM-4.6 is 13 months newer than Qwen2.5 14B Instruct.
Sep 30, 2025
6 months ago
1.0yr newerSep 19, 2024
1.6 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 14B Instruct
View detailsAlibaba Cloud / Qwen Team
Detailed Comparison
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FAQ
Common questions about GLM-4.6 vs Qwen2.5 14B Instruct