Audience
Developers wanting an AI model for educational tools and embedded tutoring
About Phi-4-mini-reasoning
Phi-4-mini-reasoning is a 3.8-billion parameter transformer-based language model optimized for mathematical reasoning and step-by-step problem solving in environments with constrained computing or latency. Fine-tuned with synthetic data generated by the DeepSeek-R1 model, it balances efficiency with advanced reasoning ability. Trained on over one million diverse math problems spanning multiple levels of difficulty from middle school to Ph.D. level, Phi-4-mini-reasoning outperforms its base model on long sentence generation across various evaluations and surpasses larger models like OpenThinker-7B, Llama-3.2-3B-instruct, and DeepSeek-R1. It features a 128K-token context window and supports function calling, enabling integration with external tools and APIs. Phi-4-mini-reasoning can be quantized using Microsoft Olive or Apple MLX Framework for deployment on edge devices such as IoT, laptops, and mobile devices.
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Phi-4-reasoning-plus
Phi-4-reasoning-plus is a 14-billion parameter open-weight reasoning model that builds upon Phi-4-reasoning capabilities. It is further trained with reinforcement learning to utilize more inference-time compute, using 1.5x more tokens than Phi-4-reasoning, to deliver higher accuracy. Despite its significantly smaller size, Phi-4-reasoning-plus achieves better performance than OpenAI o1-mini and DeepSeek-R1 at most benchmarks, including mathematical reasoning and Ph.D. level science questions. It surpasses the full DeepSeek-R1 model (with 671 billion parameters) on the AIME 2025 test, the 2025 qualifier for the USA Math Olympiad. Phi-4-reasoning-plus is available on Azure AI Foundry and HuggingFace.
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Phi-4-reasoning
Phi-4-reasoning is a 14-billion parameter transformer-based language model optimized for complex reasoning tasks, including math, coding, algorithmic problem solving, and planning. Trained via supervised fine-tuning of Phi-4 on carefully curated "teachable" prompts and reasoning demonstrations generated using o3-mini, it generates detailed reasoning chains that effectively leverage inference-time compute. Phi-4-reasoning incorporates outcome-based reinforcement learning to produce longer reasoning traces. It outperforms significantly larger open-weight models such as DeepSeek-R1-Distill-Llama-70B and approaches the performance levels of the full DeepSeek-R1 model across a wide range of reasoning tasks. Phi-4-reasoning is designed for environments with constrained computing or latency. Fine-tuned with synthetic data generated by DeepSeek-R1, it provides high-quality, step-by-step problem solving.
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DeepSeek R1
DeepSeek-R1 is an advanced open-source reasoning model developed by DeepSeek, designed to rival OpenAI's Model o1. Accessible via web, app, and API, it excels in complex tasks such as mathematics and coding, demonstrating superior performance on benchmarks like the American Invitational Mathematics Examination (AIME) and MATH. DeepSeek-R1 employs a mixture of experts (MoE) architecture with 671 billion total parameters, activating 37 billion parameters per token, enabling efficient and accurate reasoning capabilities. This model is part of DeepSeek's commitment to advancing artificial general intelligence (AGI) through open-source innovation.
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OpenAI o3-mini
OpenAI o3-mini is a lightweight version of the advanced o3 AI model, offering powerful reasoning capabilities in a more efficient and accessible package. Designed to break down complex instructions into smaller, manageable steps, o3-mini excels in coding tasks, competitive programming, and problem-solving in mathematics and science. This compact model provides the same high-level precision and logic as its larger counterpart but with reduced computational requirements, making it ideal for use in resource-constrained environments. With built-in deliberative alignment, o3-mini ensures safe, ethical, and context-aware decision-making, making it a versatile tool for developers, researchers, and businesses seeking a balance between performance and efficiency.
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Company Information
Microsoft
Founded: 1975
United States
azure.microsoft.com/en-us/blog/one-year-of-phi-small-language-models-making-big-leaps-in-ai/
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Product Details
Platforms Supported
Cloud
Training
Documentation
Live Online
Webinars
In Person
Videos
Support
Phone Support
Online
Phi-4-mini-reasoning Frequently Asked Questions
Q: What kinds of users and organization types does Phi-4-mini-reasoning work with?
Phi-4-mini-reasoning works with these users and organization types: Mid Size Business, Small Business, Enterprise, Freelance, Nonprofit, Government, and Startup.
Q: What languages does Phi-4-mini-reasoning support in their product?
Phi-4-mini-reasoning supports these languages: English.
Q: What kind of support options does Phi-4-mini-reasoning offer?
Phi-4-mini-reasoning offers support via phone support and online.
Q: What other applications or services does Phi-4-mini-reasoning integrate with?
Phi-4-mini-reasoning integrates with: Microsoft Foundry, Microsoft Foundry Models, Microsoft Azure, ExecuTorch, and Hugging Face.
Q: Does Phi-4-mini-reasoning have an API?
Yes, Phi-4-mini-reasoning has an API.
Q: What type of training does Phi-4-mini-reasoning provide?
Phi-4-mini-reasoning provides training in the form of documentation, live online, webinars, in person, and videos.
