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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 Azure OpenAI Service?

Leverage advanced coding and linguistic models across a wide range of applications. Tap into the capabilities of extensive generative AI models that offer a profound understanding of both language and programming, facilitating innovative reasoning and comprehension essential for creating cutting-edge applications. These models find utility in various areas, such as writing assistance, code generation, and data analytics, all while adhering to responsible AI guidelines to mitigate any potential misuse, supported by robust Azure security measures. Utilize generative models that have been exposed to extensive datasets, enabling their use in multiple contexts like language processing, coding assignments, logical reasoning, inferencing, and understanding. Customize these generative models to suit your specific requirements by employing labeled datasets through an easy-to-use REST API. You can improve the accuracy of your outputs by refining the model’s hyperparameters and applying few-shot learning strategies to provide the API with examples, resulting in more relevant outputs and ultimately boosting application effectiveness. By implementing appropriate configurations and optimizations, you can significantly enhance your application's performance while ensuring a commitment to ethical practices in AI application. Additionally, the continuous evolution of these models allows for ongoing improvements, keeping pace with advancements in technology.

Media

Media

Integrations Supported

Microsoft Azure
Microsoft Foundry Models
Activepieces
Aim
BotDojo
ChatGPT
Cranium
CrowdWorx Innovation Engine
GPT-5.1 Instant
GPT-5.2 Instant
GPT-5.4 Pro
Glassix
Jitterbit
LLaforM
Langtail
Mazaal AI
Phi-3
StackAI
WebOrion Protector Plus

Integrations Supported

Microsoft Azure
Microsoft Foundry Models
Activepieces
Aim
BotDojo
ChatGPT
Cranium
CrowdWorx Innovation Engine
GPT-5.1 Instant
GPT-5.2 Instant
GPT-5.4 Pro
Glassix
Jitterbit
LLaforM
Langtail
Mazaal AI
Phi-3
StackAI
WebOrion Protector Plus

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Pricing Information

$0.0004 per 1000 tokens
Free Trial Offered?
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

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

Microsoft

Date Founded

1975

Company Location

United States

Company Website

azure.microsoft.com/en-us/products/cognitive-services/openai-service

Categories and Features

Categories and Features

Artificial Intelligence

Chatbot
For Healthcare
For Sales
For eCommerce
Image Recognition
Machine Learning
Multi-Language
Natural Language Processing
Predictive Analytics
Process/Workflow Automation
Rules-Based Automation
Virtual Personal Assistant (VPA)

Natural Language Generation

Business Intelligence
CRM Data Analysis and Reports
Chatbot
Email Marketing
Financial Reporting
Multiple Language Support
SEO
Web Content

Natural Language Processing

Co-Reference Resolution
In-Database Text Analytics
Named Entity Recognition
Natural Language Generation (NLG)
Open Source Integrations
Parsing
Part-of-Speech Tagging
Sentence Segmentation
Stemming/Lemmatization
Tokenization

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