GLM-4.7-Flash vs Qwen3.5-9B Comparison
Comparing GLM-4.7-Flash and Qwen3.5-9B across benchmarks, pricing, and capabilities.
Performance Benchmarks
Comparative analysis across standard metrics
GLM-4.7-Flash outperforms in 0 benchmarks, while Qwen3.5-9B is better at 1 benchmark (GPQA).
Qwen3.5-9B 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-Flash has 21.0B more parameters than Qwen3.5-9B, making it 233.3% larger.
Context Window
Maximum input and output token capacity
Only GLM-4.7-Flash specifies input context (128,000 tokens). Only GLM-4.7-Flash specifies output context (16,384 tokens).
Input Capabilities
Supported data types and modalities
Qwen3.5-9B supports multimodal inputs, whereas GLM-4.7-Flash does not.
Qwen3.5-9B can handle both text and other forms of data like images, making it suitable for multimodal applications.
GLM-4.7-Flash
Qwen3.5-9B
License
Usage and distribution terms
GLM-4.7-Flash is licensed under MIT, while Qwen3.5-9B 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-Flash was released on 2026-01-19, while Qwen3.5-9B was released on 2026-03-02.
Qwen3.5-9B is 1 month newer than GLM-4.7-Flash.
Jan 19, 2026
1 months ago
Mar 2, 2026
1 weeks ago
1mo newerKnowledge 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-Flash
View detailsZhipu AI
Qwen3.5-9B
View detailsAlibaba Cloud / Qwen Team
Detailed Comparison
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