Phi-3 Mini vs Gemma 2: Best Small Language Model for Edge Devices in 2026

Pros

• State-of-the-art reasoning for sub-4B models — beats many 7B models on MMLU
• MIT license — most permissive of any frontier SLM
• ONNX Runtime support — optimized for CPU inference on Windows/iOS/Android
• Two variants: 4K (lightweight) and 128K (long context)
• Official DirectML and CUDA inference paths for Windows AI PCs

Cons

• Weaker at creative writing vs instruction-tuned chat models
• Small knowledge base — training data cutoff earlier than larger models
• Less multilingual coverage than Gemma 2
• Context at 4K tokens in the smallest variant

Pros

• Fastest inference speed in the sub-3B class
• Strong performance on instruction following for its size
• 8K context window at 2B parameters
• Available in 2B and 9B variants for flexible deployment
• Optimized for browser inference via WebLLM and MediaPipe

Cons

• Gemma Terms of Use are more restrictive than MIT for certain use cases
• Slightly below Phi-3 Mini on reasoning benchmarks (MMLU ~52% vs ~70%)
• No official ONNX export — requires third-party conversion
• Weaker coding performance vs Phi-3