+
+
+
+
+
+
+
+
VS
VS
VS
VS
VS
VS
Gemma 3n E2B Instructed LiteRT (Preview) vs Phi 4 Mini
Comprehensive side-by-side LLM comparison
Gemma 3n E2B Instructed LiteRT (Preview) leads with 3.5% higher average benchmark score. Gemma 3n E2B Instructed LiteRT (Preview) supports multimodal inputs. Both models have their strengths depending on your specific coding needs.
+
+
+
+
Gemma 3n E2B Instructed LiteRT (Preview) is a multimodal language model developed by Google. The model shows competitive results across 28 benchmarks. Notable strengths include PIQA (78.9%), BoolQ (76.4%), ARC-E (75.8%). As a multimodal model, it can process and understand text, images, and other input formats seamlessly. It's licensed for commercial use, making it suitable for enterprise applications. Released in 2025, it represents Google's latest advancement in AI technology.
+
+
+
+
Microsoft
Phi 4 Mini is a language model developed by Microsoft. It achieves strong performance with an average score of 65.4% across 17 benchmarks. It excels particularly in GSM8k (88.6%), ARC-C (83.7%), BoolQ (81.2%). It's licensed for commercial use, making it suitable for enterprise applications. Released in 2025, it represents Microsoft's latest advancement in AI technology.
3 months newer
Phi 4 Mini
Microsoft
2025-02-01
Gemma 3n E2B Instructed LiteRT (Preview)
2025-05-20
Average performance across 34 common benchmarks
+
+
+
+
Gemma 3n E2B Instructed LiteRT (Preview)
Average Score:36.2%(+3.5%)
+
+
+
+
Phi 4 Mini
Average Score:32.7%
+
+
+
+
Knowledge Cutoff
Training data recency comparison
Gemma 3n E2B Instructed LiteRT (Preview)
2024-06-01
Phi 4 Mini
2024-06-01
More recent knowledge cutoff means awareness of newer technologies and frameworks
Provider Availability & Performance
Available providers and their performance metrics
+
+
+
+
Gemma 3n E2B Instructed LiteRT (Preview)
0 providers
+
+
+
+
Phi 4 Mini
0 providers
+
+
+
+
Gemma 3n E2B Instructed LiteRT (Preview)
Avg Score:36.2%(+3.5%)
Providers:0
+
+
+
+
Phi 4 Mini
Avg Score:32.7%
Providers:0