Model Comparison
GLM-4.6 vs Qwen2.5-Coder 7B Instruct
Comparing GLM-4.6 and Qwen2.5-Coder 7B Instruct across benchmarks, pricing, and capabilities.
Performance Benchmarks
Comparative analysis across standard metrics
GLM-4.6 and Qwen2.5-Coder 7B Instruct 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-4.6 has 350.0B more parameters than Qwen2.5-Coder 7B Instruct, 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
GLM-4.6 supports multimodal inputs, whereas Qwen2.5-Coder 7B 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-Coder 7B Instruct
License
Usage and distribution terms
GLM-4.6 is licensed under MIT, while Qwen2.5-Coder 7B 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-Coder 7B Instruct was released on 2024-09-19.
GLM-4.6 is 13 months newer than Qwen2.5-Coder 7B Instruct.
Sep 30, 2025
7 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-Coder 7B Instruct
View detailsAlibaba Cloud / Qwen Team
Detailed Comparison
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FAQ
Common questions about GLM-4.6 vs Qwen2.5-Coder 7B Instruct