open-sourcereasoning

Microsoft (Phi)

Small Language Models proving that data quality outperforms raw parameter scale.

3
Models tracked
1975
Founded
Phi-4
Flagship model
Redmond
HQ

About

About Microsoft (Phi)

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

3 models tracked

Context windows, pricing, capabilities, and API identifiers — everything you need to choose the right model for your workload.

FlagshipOpen Source

Phi-4

2024

16Kcontext
TextCodeReasoning

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.

  • Outperforms 70B models on reasoning
  • MIT licence
  • 14B parameters
  • High-quality synthetic data
Phi-4
Open Source

Phi-3.5 Mini

2024

128Kcontext
TextCode

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.

  • On-device inference
  • 3.8B parameters
  • 128K context
  • CPU & NPU optimised
Phi-3.5-mini-instruct
Open Source

Phi-3.5 MoE

2024

128Kcontext
TextVisionCode

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.

  • Vision capable
  • MoE efficiency
  • 128K context
  • Open weights
Phi-3.5-MoE-instruct

Use Cases

What Microsoft (Phi) is best for

Each provider has specialisms shaped by their architecture decisions, training data, and API design. Here is where Microsoft (Phi) performs best.

Edge & on-device AI
Resource-constrained deployments
Academic & ML research
Fine-tuning for domain-specific tasks
Windows & mobile AI features

Get started

Start building with Microsoft (Phi)

Visit the official Microsoft (Phi) platform to access API keys, documentation, and pricing details.