Model Comparison
GLM-4.7 vs Qwen2 7B Instruct
GLM-4.7 significantly outperforms across most benchmarks.
Performance Benchmarks
Comparative analysis across standard metrics
GLM-4.7 outperforms in 2 benchmarks (GPQA, MMLU-Pro), while Qwen2 7B Instruct 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 350.4B more parameters than Qwen2 7B Instruct, making it 4598.2% 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
GLM-4.7 supports multimodal inputs, whereas Qwen2 7B Instruct does not.
GLM-4.7 can handle both text and other forms of data like images, making it suitable for multimodal applications.
GLM-4.7
Qwen2 7B Instruct
License
Usage and distribution terms
GLM-4.7 is licensed under MIT, while Qwen2 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-4.7 was released on 2025-12-22, while Qwen2 7B Instruct was released on 2024-07-23.
GLM-4.7 is 17 months newer than Qwen2 7B Instruct.
Dec 22, 2025
3 months ago
1.4yr newerJul 23, 2024
1.7 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-4.7
View detailsZhipu AI
Qwen2 7B Instruct
View detailsAlibaba Cloud / Qwen Team
Detailed Comparison
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FAQ
Common questions about GLM-4.7 vs Qwen2 7B Instruct