Microsoft’s most capable new Phi 4 AI model rivals the performance of far larger systems

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Microsoft on Wednesday launched several new “open” AI models, the most capable of which is competitive with OpenAI’s o3-mini on at least one benchmark.

As it says on the tin, all of the new permissively licensed models — Phi 4 mini reasoning, Phi 4 reasoning, and Phi 4 reasoning plus — are “reasoning” models, meaning they can spend more time fact-checking solutions to complex problems. They expand Microsoft’s Phi “small model” family, which the company launched a year ago to offer a foundation for AI developers building apps at the edge.

Phi 4 mini reasoning was trained on roughly 1 million synthetic math problems generated by Chinese AI startup DeepSeek’s R1 reasoning model. Around 3.8 billion parameters in size, Phi 4 mini reasoning is designed for educational applications, Microsoft says, like “embedded tutoring” on lightweight devices.

Parameters roughly correspond to a model’s problem-solving skills, and models with more parameters generally perform better than those with fewer parameters.

Phi 4 reasoning, a 14-billion-parameter model, was trained using “high-quality” web data as well as “curated demonstrations” from OpenAI’s aforementioned o3-mini. It’s best for math, science and coding applications, according to Microsoft.

As for Phi 4 reasoning plus, it’s Microsoft’s previously-released Phi 4 model adapted into a reasoning model to achieve better accuracy for particular tasks. Microsoft claims Phi 4 reasoning plus approaches the performance levels of DeepSeek R1, which has significantly more parameters (671 billion). The company’s internal benchmarking also has Phi 4 reasoning plus matching o3-mini on OmniMath, a math skills test.

Phi 4 mini reasoning, Phi 4 reasoning, Phi 4 reasoning plus, and their detailed technical reports, are available on the AI dev platform Hugging Face.

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“Using distillation, reinforcement learning, and high-quality data, these [new] models balance size and performance,” wrote Microsoft in a blog post. “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.”