Model Comparison

Comprehensive side-by-side analysis of model capabilities and performance

Google

Gemma 3n E4B

Google

Gemma 3n E4B is a multimodal language model developed by Google. It achieves strong performance with an average score of 64.6% across 11 benchmarks. It excels particularly in ARC-E (81.6%), BoolQ (81.6%), PIQA (81.0%). As a multimodal model, it can process and understand text, images, and other input formats seamlessly. Released in 2025, it represents Google's latest advancement in AI technology.

Microsoft

Phi-3.5-MoE-instruct

Microsoft

Phi-3.5-MoE-instruct is a language model developed by Microsoft. It achieves strong performance with an average score of 65.6% across 31 benchmarks. It excels particularly in ARC-C (91.0%), OpenBookQA (89.6%), GSM8k (88.7%). The model shows particular specialization in reasoning tasks with an average performance of 85.4%. It's licensed for commercial use, making it suitable for enterprise applications. Released in 2024, it represents Microsoft's latest advancement in AI technology.

Microsoft

Phi-3.5-MoE-instruct

Microsoft

2024-08-23

Google

Gemma 3n E4B

Google

2025-06-26

10 months newer

Average performance across 7 common benchmarks

Google

Gemma 3n E4B

Average Score:68.2%
Microsoft

Phi-3.5-MoE-instruct

+15.6%
Average Score:83.8%

Performance comparison across key benchmark categories

Google

Gemma 3n E4B

reasoning
73.4%
general
59.6%
Microsoft

Phi-3.5-MoE-instruct

reasoning
+12.0%
85.4%
general
+1.3%
60.9%
Benchmark Scores - Detailed View
Side-by-side comparison of all benchmark scores
Knowledge Cutoff
Training data recency comparison

Gemma 3n E4B

2024-06-01

More recent knowledge cutoff means awareness of newer technologies and frameworks

Provider Availability & Performance

Available providers and their performance metrics

Google

Gemma 3n E4B

0 providers
Microsoft

Phi-3.5-MoE-instruct

0 providers
Google

Gemma 3n E4B

Avg Score:68.2%
Providers:0
Microsoft

Phi-3.5-MoE-instruct

+15.6%
Avg Score:83.8%
Providers:0