Phi-4 Reasoning Models

Summary

Microsoft continues to add to the conversation by unveiling its newest models, Phi-4-reasoning, Phi-4-reasoning-plus, and Phi-4-mini-reasoning. A new era of AI One year ago, Microsoft introduced small language models (SLMs) to customers with the release of Phi-3 on Azure AI Foundry, leveraging research on SLMs to expand the range of efficient AI models and tools available to customers. Today, we are excited to introduce Phi-4-reasoning, Phi-4-reasoning-plus, and Phi-4-mini-reasoning—marking a new era for small language models and once again redefining what is possible with small and efficient AI. Azure AI Foundry Find the ideal model for your business needs, then tinker, tweak, and customize within a project to achieve all your AI goals. Reasoning models, the next step forward Reasoning models are trained to leverage inference-time scaling to perform complex tasks that demand multi-step decomposition and internal reflection. They excel in mathematical reasoning and are emerging as the backbone of agentic applications with complex multi-faceted tasks. Such capabilities are typically found only in large frontier models. Phi- reasoning models introduce a new category of small language models. Using distillation, reinforcement learning, and high-quality data, these models balance size and performance. They are small enough for low-latency environments yet maintain strong reasoning capabilities that rival much bigger models. This blend allows even resource-limited devices to perform complex reasoning tasks efficiently. Phi-4-mini-reasoning Phi-4-mini-reasoning is designed to meet the demand for a compact reasoning model. This transformer-based language model is optimized for mathematical reasoning, providing high-quality, step-by-step problem solving in environments with constrained computing or latency. Fine-tuned with synthetic data generated by Deepseek-R1 model, Phi-4-mini-reasoning balances efficiency with advanced reasoning ability. It’s ideal for educational appli...