Small Language Models proving that data quality outperforms raw parameter scale.
About
Microsoft Research's Phi model series demonstrates that carefully curated training data produces models that punch far above their parameter weight. Phi-4 (14B parameters) outperforms many 70B models on reasoning benchmarks, while Phi-3.5 Mini (3.8B) runs efficiently on-device. The Phi series is especially popular for edge deployments, mobile AI, and research into the scaling laws of high-quality synthetic data.
Key highlights
Phi-4 outperforms models 5× its size on GPQA Diamond and MATH benchmarks
MIT licence — free for commercial use, modification, and redistribution
Phi-3.5 Mini targets on-device inference for Windows Copilot+ and mobile
Wide availability on Azure AI Studio, Ollama, llama.cpp, and Hugging Face
Research-grade focus on synthetic data quality rather than raw scale
Models
Context windows, pricing, capabilities, and API identifiers — everything you need to choose the right model for your workload.
2024
Microsoft's latest flagship small model. At 14B parameters, Phi-4 outperforms many much larger models on GPQA and MATH benchmarks thanks to high-quality synthetic training data and careful curriculum.
Phi-42024
A 3.8B model with a 128K context window that runs efficiently on CPU and NPU hardware. Designed for device-side AI in Windows Copilot+ PCs and mobile applications.
Phi-3.5-mini-instruct2024
A mixture-of-experts variant of Phi-3.5 that adds vision capability. Uses 16 experts with 2 active per token for efficient inference at competitive quality.
Phi-3.5-MoE-instructUse Cases
Each provider has specialisms shaped by their architecture decisions, training data, and API design. Here is where Microsoft (Phi) performs best.
Get started
Visit the official Microsoft (Phi) platform to access API keys, documentation, and pricing details.