Model Comparison
GLM-5 vs Qwen3.6-27B
Both models are evenly matched across the benchmarks.
Performance Benchmarks
Comparative analysis across standard metrics
GLM-5 outperforms in 1 benchmarks (SWE-Bench Verified), while Qwen3.6-27B is better at 1 benchmark (Terminal-Bench 2.0).
Both models are evenly matched across the benchmarks.
Arena Performance
Human preference votes
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Pricing Analysis
Price comparison per million tokens
Cost data unavailable.
Model Size
Parameter count comparison
GLM-5 has 716.2B more parameters than Qwen3.6-27B, making it 2578.0% larger.
Context Window
Maximum input and output token capacity
Only GLM-5 specifies input context (200,000 tokens). Only GLM-5 specifies output context (128,000 tokens).
Input Capabilities
Supported data types and modalities
Qwen3.6-27B supports multimodal inputs, whereas GLM-5 does not.
Qwen3.6-27B can handle both text and other forms of data like images, making it suitable for multimodal applications.
GLM-5
Qwen3.6-27B
License
Usage and distribution terms
GLM-5 is licensed under MIT, while Qwen3.6-27B 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-5 was released on 2026-02-11, while Qwen3.6-27B was released on 2026-04-21.
Qwen3.6-27B is 2 months newer than GLM-5.
Feb 11, 2026
2 months ago
Apr 21, 2026
2 days ago
2mo 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-5
View detailsZhipu AI
Qwen3.6-27B
View detailsAlibaba Cloud / Qwen Team
Detailed Comparison
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FAQ
Common questions about GLM-5 vs Qwen3.6-27B