Gemma 3n E4B vs GLM-4.7-Flash Comparison
Comparing Gemma 3n E4B and GLM-4.7-Flash across benchmarks, pricing, and capabilities.
Performance Benchmarks
Comparative analysis across standard metrics
Gemma 3n E4B and GLM-4.7-Flash 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-4.7-Flash has 22.0B more parameters than Gemma 3n E4B, making it 275.0% 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
Gemma 3n E4B supports multimodal inputs, whereas GLM-4.7-Flash does not.
Gemma 3n E4B can handle both text and other forms of data like images, making it suitable for multimodal applications.
Gemma 3n E4B
GLM-4.7-Flash
License
Usage and distribution terms
Gemma 3n E4B is licensed under a proprietary license, while GLM-4.7-Flash uses MIT.
License differences may affect how you can use these models in commercial or open-source projects.
Proprietary
Closed source
MIT
Open weights
Release Timeline
When each model was launched
Gemma 3n E4B was released on 2025-06-26, while GLM-4.7-Flash was released on 2026-01-19.
GLM-4.7-Flash is 7 months newer than Gemma 3n E4B.
Jun 26, 2025
8 months ago
Jan 19, 2026
1 months ago
6mo newerKnowledge Cutoff
When training data ends
Gemma 3n E4B has a documented knowledge cutoff of 2024-06-01, while GLM-4.7-Flash's cutoff date is not specified.
We can confirm Gemma 3n E4B's training data extends to 2024-06-01, but cannot make a direct comparison without GLM-4.7-Flash's cutoff date.
Jun 2024
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Outputs Comparison
Key Takeaways
Gemma 3n E4B
View detailsGLM-4.7-Flash
View detailsZhipu AI
Detailed Comparison
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