One 12 months of Phi: Small language fashions making huge leaps in AI

Microsoft continues so as to add to the dialog by unveiling its latest fashions, Phi-4-reasoning, Phi-4-reasoning-plus, and Phi-4-mini-reasoning. 

A brand new period of AI 

One 12 months in the past, Microsoft launched small language fashions (SLMs) to clients with the discharge of Phi-3 on Azure AI Foundry, leveraging analysis on SLMs to broaden the vary of environment friendly AI fashions and instruments accessible to clients. 

At the moment, we’re excited to introduce Phi-4-reasoning, Phi-4-reasoning-plus, and Phi-4-mini-reasoning—marking a brand new period for small language fashions and as soon as once more redefining what is feasible with small and environment friendly AI. 

Reasoning fashions, the following step ahead

Reasoning fashions are educated to leverage inference-time scaling to carry out advanced duties that demand multi-step decomposition and inside reflection. They excel in mathematical reasoning and are rising because the spine of agentic functions with advanced, multi-faceted duties. Such capabilities are sometimes discovered solely in massive frontier fashions. Phi-reasoning fashions introduce a brand new class of small language fashions. Utilizing distillation, reinforcement studying, and high-quality information, these fashions steadiness dimension and efficiency. They’re sufficiently small for low-latency environments but keep robust reasoning capabilities that rival a lot larger fashions. This mix permits even resource-limited gadgets to carry out advanced reasoning duties effectively.

Phi-4-reasoning and Phi-4-reasoning-plus 

Phi-4-reasoning is a 14-billion parameter open-weight reasoning mannequin that rivals a lot bigger fashions on advanced reasoning duties. Skilled by way of supervised fine-tuning of Phi-4 on rigorously curated reasoning demonstrations from OpenAI o3-mini, Phi-4-reasoning generates detailed reasoning chains that successfully leverage extra inference-time compute. The mannequin demonstrates that meticulous information curation and high-quality artificial datasets enable smaller fashions to compete with bigger counterparts.

Phi-4-reasoning-plus builds upon Phi-4-reasoning capabilities, additional educated with reinforcement studying to make the most of extra inference-time compute, utilizing 1.5x extra tokens than Phi-4-reasoning, to ship larger accuracy.

Regardless of their considerably smaller dimension, each fashions obtain higher efficiency than OpenAI o1-mini and DeepSeek-R1-Distill-Llama-70B at most benchmarks, together with mathematical reasoning and Ph.D. stage science questions. They obtain efficiency higher than the total DeepSeek-R1 mannequin (with 671-billion parameters) on the AIME 2025 check, the 2025 qualifier for the USA Math Olympiad. Each fashions can be found on Azure AI Foundry and HuggingFace, right here and right here.

Phi-4-reasoning fashions introduce a significant enchancment over Phi-4, surpass bigger fashions like DeepSeek-R1-Distill-70B and method Deep-Search-R1 throughout varied reasoning and common capabilities, together with math, coding, algorithmic drawback fixing, and planning. The technical report offers in depth quantitative proof of those enhancements by way of various reasoning duties.

Phi-4-mini-reasoning

For extra details about the mannequin, learn the technical report that gives extra quantitative insights.

Phi’s evolution over the past 12 months has regularly pushed this envelope of high quality vs. dimension, increasing the household with new options to handle various wants. Throughout the size of Home windows 11 gadgets, these fashions can be found to run regionally on CPUs and GPUs.

As Home windows works in direction of creating a brand new sort of PC, Phi fashions have grow to be an integral a part of Copilot+ PCs with the NPU-optimized Phi Silica variant. This extremely environment friendly and OS-managed model of Phi is designed to be preloaded in reminiscence, and accessible with blazing quick time to first token responses, and energy environment friendly token throughput so it may be concurrently invoked with different functions operating in your PC.

It’s utilized in core experiences like Click on to Do, offering helpful textual content intelligence instruments for any content material in your display screen, and is offered as developer APIs to be readily built-in into functions—already being utilized in a number of productiveness functions like Outlook, providing its Copilot abstract options offline. These small however mighty fashions have already been optimized and built-in for use throughout a number of functions throughout the breadth of our PC ecosystem. The Phi-4-reasoning and Phi-4-mini-reasoning fashions leverage the low-bit optimizations for Phi Silica and will likely be accessible to run quickly on Copilot+ PC NPUs.

Security and Microsoft’s method to accountable AI 

At Microsoft, accountable AI is a elementary precept guiding the event and deployment of AI methods, together with our Phi fashions. Phi fashions are developed in accordance with Microsoft AI rules: accountability, transparency, equity, reliability and security, privateness and safety, and inclusiveness. 

The Phi household of fashions has adopted a sturdy security post-training method, leveraging a mix of Supervised Positive-Tuning (SFT), Direct Choice Optimization (DPO), and Reinforcement Studying from Human Suggestions (RLHF) strategies. These strategies make the most of varied datasets, together with publicly accessible datasets centered on helpfulness and harmlessness, in addition to varied safety-related questions and solutions. Whereas the Phi household of fashions is designed to carry out a variety of duties successfully, it is very important acknowledge that each one AI fashions could exhibit limitations. To raised perceive these limitations and the measures in place to handle them, please seek advice from the mannequin playing cards under, which give detailed info on accountable AI practices and pointers.

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