Phi-4 Reasoning

Phi-4 Reasoning

WebsiteContact for PricingLarge Language Models (LLMs)AI Code Assistant
Phi-4-reasoning is a 14-billion parameter open-weight reasoning model from Microsoft that excels at complex mathematical and scientific reasoning tasks while maintaining a relatively small size compared to larger language models.
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https://azure.microsoft.com/en-us/blog/one-year-of-phi-small-language-models-making-big-leaps-in-ai?ref=aipure
Phi-4 Reasoning

Product Information

Updated:Jun 16, 2025

Phi-4 Reasoning Monthly Traffic Trends

Phi-4 Reasoning experienced a 7.4% decline in traffic, likely due to the lack of significant product updates and the introduction of Microsoft Copilot in Azure, which offers advanced AI capabilities for cost analysis and may have drawn users away.

View history traffic

What is Phi-4 Reasoning

Phi-4-reasoning is Microsoft's latest advancement in small language models (SLMs), designed to perform sophisticated reasoning tasks typically associated with much larger AI models. Released as part of the Phi family of models, it represents a significant breakthrough in balancing model size with performance. The model is trained via supervised fine-tuning of Phi-4 on carefully curated reasoning demonstrations from OpenAI o3-mini, enabling it to generate detailed reasoning chains while efficiently utilizing computational resources. It is publicly available through Azure AI Foundry and Hugging Face, making it accessible for various applications and development needs.

Key Features of Phi-4 Reasoning

Phi-4 Reasoning is a 14-billion parameter open-weight reasoning model developed by Microsoft that excels at complex mathematical and scientific reasoning tasks despite its relatively small size. The model leverages inference-time scaling, supervised fine-tuning, and high-quality synthetic datasets to achieve performance that rivals or exceeds much larger models, including those with hundreds of billions of parameters. It's designed for efficient deployment in resource-constrained environments while maintaining strong reasoning capabilities.
Advanced Reasoning Capabilities: Excels at complex mathematical and scientific reasoning tasks, including Ph.D. level questions and math competition problems, using multi-step decomposition and internal reflection
Efficient Architecture: 14B parameter model that achieves superior performance while being significantly smaller than competing models, making it suitable for deployment in resource-limited environments
High-Quality Training: Trained using carefully curated reasoning demonstrations, high-quality synthetic datasets, and advanced post-training innovations including supervised fine-tuning
Flexible Deployment Options: Available on both Azure AI Foundry and HuggingFace, with support for various deployment scenarios including edge devices and local computing

Use Cases of Phi-4 Reasoning

Educational Applications: Provides step-by-step problem solving and mathematical reasoning for tutoring and educational support systems
Scientific Research: Assists researchers with complex mathematical calculations and scientific reasoning tasks in research environments
Edge Computing Applications: Powers AI applications on resource-constrained devices like IoT devices and mobile phones where efficient processing is crucial
Windows Copilot+ Integration: Enables advanced reasoning capabilities in Windows PCs with NPU optimization for efficient local processing

Pros

Exceptional performance despite small size compared to larger models
Efficient resource utilization making it suitable for edge devices
Strong mathematical and scientific reasoning capabilities

Cons

Not designed for in-depth knowledge retrieval like larger language models
Limited by smaller training dataset compared to larger models
May require additional mitigations for sensitive contexts

How to Use Phi-4 Reasoning

Access Azure AI Foundry: Visit Azure AI Foundry platform (https://ai.azure.com/) and sign in with your Azure account
Find Phi-4 Reasoning Model: Navigate to the model catalog and search for 'Phi-4-reasoning' in the Azure AI Foundry model collection
Choose Model Variant: Select between Phi-4-reasoning (14B parameters) or Phi-4-reasoning-plus for higher accuracy with 1.5x more tokens
Deploy the Model: Follow Azure AI Foundry's deployment process to set up the model in your workspace. You can also alternatively access it through HuggingFace
Configure Parameters: Set up the model parameters according to your specific use case - particularly for mathematical reasoning, scientific questions, or complex problem-solving tasks
Integrate Safety Measures: Implement recommended safety services like Azure AI Content Safety for additional guardrails and responsible AI practices
Test the Model: Start with sample problems to test the model's reasoning capabilities, particularly in areas like math problems, scientific reasoning, or step-by-step problem solving
Monitor Performance: Use Azure AI Foundry's monitoring tools to track the model's performance, accuracy, and resource usage
Optimize and Scale: Based on performance metrics, adjust parameters and scale the deployment as needed for your specific application requirements

Phi-4 Reasoning FAQs

Phi-4-reasoning is a 14-billion parameter open-weight reasoning model that can compete with much larger models on complex reasoning tasks. Despite its small size, it outperforms larger models like OpenAI o1-mini and DeepSeek-R1-Distill-Llama-70B on most benchmarks, including mathematical reasoning and Ph.D. level science questions.

Analytics of Phi-4 Reasoning Website

Phi-4 Reasoning Traffic & Rankings
6.8M
Monthly Visits
-
Global Rank
-
Category Rank
Traffic Trends: Jun 2024-May 2025
Phi-4 Reasoning User Insights
00:01:57
Avg. Visit Duration
1.94
Pages Per Visit
61.09%
User Bounce Rate
Top Regions of Phi-4 Reasoning

  1. US: 19.1%

  2. IN: 9.73%

  3. JP: 5.14%

  4. BR: 4.24%

  5. GB: 4.1%

  6. Others: 57.68%

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