+
+
+
+
+
+
+
+
VS
VS
VS
VS
VS
VS
Gemma 3 4B vs Phi 4 Mini Reasoning
Comprehensive side-by-side LLM comparison
Gemma 3 4B leads with 41.9% higher average benchmark score. Gemma 3 4B supports multimodal inputs. Overall, Gemma 3 4B is the stronger choice for coding tasks.
+
+
+
+
Gemma 3 4B is a multimodal language model developed by Google. The model shows competitive results across 26 benchmarks. It excels particularly in IFEval (90.2%), GSM8k (89.2%), DocVQA (75.8%). It supports a 262K token context window for handling large documents. The model is available through 1 API provider. 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 Reasoning is a language model developed by Microsoft. It achieves strong performance with an average score of 68.0% across 3 benchmarks. It excels particularly in MATH-500 (94.6%), AIME (57.5%), GPQA (52.0%). It's licensed for commercial use, making it suitable for enterprise applications. Released in 2025, it represents Microsoft's latest advancement in AI technology.
1 month newer
Gemma 3 4B
2025-03-12
Phi 4 Mini Reasoning
Microsoft
2025-04-30
Performance Metrics
Context window and performance specifications
Average performance across 28 common benchmarks
+
+
+
+
Gemma 3 4B
Average Score:49.2%(+41.9%)
+
+
+
+
Phi 4 Mini Reasoning
Average Score:7.3%
+
+
+
+
Knowledge Cutoff
Training data recency comparison
Gemma 3 4B
2024-08-01
Phi 4 Mini Reasoning
2025-02-01
More recent knowledge cutoff means awareness of newer technologies and frameworks
Provider Availability & Performance
Available providers and their performance metrics
+
+
+
+
Gemma 3 4B
1 providers
DeepInfra
Throughput: 33 tok/s
Latency: 0.2ms
+
+
+
+
Phi 4 Mini Reasoning
+
+
+
+
Gemma 3 4B
Avg Score:49.2%(+41.9%)
Providers:1
+
+
+
+
Phi 4 Mini Reasoning
Avg Score:7.3%
Providers:0