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 yr in the past, Microsoft launched small language fashions (SLMs) to prospects with the discharge of Phi-3 on Azure AI Foundry, leveraging analysis on SLMs to increase the vary of environment friendly AI fashions and instruments out there to prospects.
At this time, 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 subsequent step ahead
Reasoning fashions are educated to leverage inference-time scaling to carry out complicated duties that demand multi-step decomposition and inner reflection. They excel in mathematical reasoning and are rising because the spine of agentic functions with complicated, multi-faceted duties. Such capabilities are sometimes discovered solely in giant frontier fashions. Phi-reasoning fashions introduce a brand new class of small language fashions. Utilizing distillation, reinforcement studying, and high-quality knowledge, these fashions stability measurement and efficiency. They’re sufficiently small for low-latency environments but preserve robust reasoning capabilities that rival a lot larger fashions. This mix permits even resource-limited gadgets to carry out complicated 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 complicated reasoning duties. Skilled through 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 knowledge curation and high-quality artificial datasets permit 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 measurement, 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. degree science questions. They obtain efficiency higher than the complete 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 serious enchancment over Phi-4, surpass bigger fashions like DeepSeek-R1-Distill-70B and strategy Deep-Search-R1 throughout numerous reasoning and normal capabilities, together with math, coding, algorithmic drawback fixing, and planning. The technical report gives in depth quantitative proof of those enhancements by way of numerous reasoning duties.
Phi-4-mini-reasoning
Phi-4-mini-reasoning is designed to fulfill the demand for a compact reasoning mannequin. This transformer-based language mannequin is optimized for mathematical reasoning, offering high-quality, step-by-step drawback fixing in environments with constrained computing or latency. Tremendous-tuned with artificial knowledge generated by Deepseek-R1 mannequin, Phi-4-mini-reasoning balances effectivity with superior reasoning capacity. It’s splendid for academic functions, embedded tutoring, and light-weight deployment on edge or cellular programs, and is educated on over a million numerous math issues spanning a number of ranges of problem from center college to Ph.D. degree. Check out the mannequin on Azure AI Foundry or HuggingFace at this time.
For extra details about the mannequin, learn the technical report that gives extra quantitative insights.
Phi’s evolution over the past yr has regularly pushed this envelope of high quality vs. measurement, increasing the household with new options to handle numerous wants. Throughout the dimensions of Home windows 11 gadgets, these fashions can be found to run domestically on CPUs and GPUs.
As Home windows works in the direction of creating a brand new kind of PC, Phi fashions have develop into 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 out there with blazing quick time to first token responses, and energy environment friendly token throughput so it may be concurrently invoked with different functions working 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 on the market 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 out there to run quickly on Copilot+ PC NPUs.
Security and Microsoft’s strategy to accountable AI
At Microsoft, accountable AI is a elementary precept guiding the event and deployment of AI programs, together with our Phi fashions. Phi fashions are developed in accordance with Microsoft AI ideas: accountability, transparency, equity, reliability and security, privateness and safety, and inclusiveness.
The Phi household of fashions has adopted a strong security post-training strategy, leveraging a mix of Supervised Tremendous-Tuning (SFT), Direct Desire Optimization (DPO), and Reinforcement Studying from Human Suggestions (RLHF) strategies. These strategies make the most of numerous datasets, together with publicly out there datasets centered on helpfulness and harmlessness, in addition to numerous safety-related questions and solutions. Whereas the Phi household of fashions is designed to carry out a variety of duties successfully, you will need to acknowledge that every one AI fashions might exhibit limitations. To higher perceive these limitations and the measures in place to handle them, please discuss with the mannequin playing cards under, which give detailed info on accountable AI practices and tips.
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