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What is Phi-4-reasoning?

Phi-4-reasoning is a sophisticated transformer model that boasts 14 billion parameters, crafted specifically to address complex reasoning tasks such as mathematics, programming, algorithm design, and strategic decision-making. It achieves this through an extensive supervised fine-tuning process, utilizing curated "teachable" prompts and reasoning examples generated via o3-mini, which allows it to produce detailed reasoning sequences while optimizing computational efficiency during inference. By employing outcome-driven reinforcement learning techniques, Phi-4-reasoning is adept at generating longer reasoning pathways. Its performance is remarkable, exceeding that of much larger open-weight models like DeepSeek-R1-Distill-Llama-70B, and it closely rivals the more comprehensive DeepSeek-R1 model across a range of reasoning tasks. Engineered for environments with constrained computing resources or high latency, this model is refined with synthetic data sourced from DeepSeek-R1, ensuring it provides accurate and methodical solutions to problems. The efficiency with which this model processes intricate tasks makes it an indispensable asset in various computational applications, further enhancing its significance in the field. Its innovative design reflects an ongoing commitment to pushing the boundaries of artificial intelligence capabilities.

What is OpenAI o3-mini-high?

The o3-mini-high model created by OpenAI significantly boosts the reasoning capabilities of artificial intelligence, particularly in deep problem-solving across diverse fields such as programming, mathematics, and complex tasks. It features adaptive thinking time and offers users the choice of different reasoning modes—low, medium, and high—to customize performance according to task difficulty. Notably, it outperforms the o1 series by an impressive 200 Elo points on Codeforces, demonstrating exceptional efficiency at a lower cost while maintaining speed and accuracy in its functions. As a distinguished addition to the o3 lineup, this model not only pushes the boundaries of AI problem-solving but also prioritizes user experience by providing a free tier and enhanced limits for Plus subscribers, which increases accessibility to advanced AI tools. Its innovative architecture makes it a vital resource for individuals aiming to address difficult challenges with greater support and flexibility, ultimately enriching the problem-solving landscape. Furthermore, the user-centric approach ensures that a wide range of users can benefit from its capabilities, making it a versatile solution for different needs.

Media

Media

No images available

Integrations Supported

Hugging Face
Microsoft Azure
Microsoft Foundry
Microsoft Foundry Models

Integrations Supported

C
C#
CSS
ChatGPT
ChatGPT Plus
ChatGPT Pro
F#
HTML
Java
Julia
OpenAI deep research
PHP
Python
Ruby
Rust
Visual Basic

API Availability

API Availability

Has API

Pricing Information

Pricing not provided

Pricing Information

Pricing not provided

Supported Platforms

SaaS

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac

Customer Service / Support

Standard Support
Web-Based Support

Customer Service / Support

Not specified

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub

Company Facts

Organization Name

Microsoft

Date Founded

1975

Company Location

United States

Company Website

azure.microsoft.com/en-us/blog/one-year-of-phi-small-language-models-making-big-leaps-in-ai/

Company Facts

Organization Name

OpenAI

Date Founded

2015

Company Location

United States

Company Website

openai.com

Categories and Features

AI Models

Not specified

AI Reasoning Models

Not specified

Open-Weight AI Models

Not specified

Small Language Models

Not specified

Categories and Features

AI Code Refactoring

Not specified

AI Coding Models

Not specified

AI Models

Not specified

AI Reasoning Models

Not specified

Foundation Models

Not specified

Large Language Models

Not specified

Multimodal Models

Not specified

Small Language Models

Not specified

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