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GPT-4.1, Mini, and Nano
Data Science   Latest   Machine Learning

GPT-4.1, Mini, and Nano

Naveen Krishnan
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April 14, 2025
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Last Updated on April 15, 2025 by Editorial Team

Author(s): Naveen Krishnan

Originally published on Towards AI.

Image Source—unsplash.com

The GPT-4.1 model series release through Azure AI Foundry represents a major step forward in AI capabilities. The model series provides multiple options that suit different application requirements, from complex reasoning to basic cost-effective solutions. This blog examines the features and pricing structure of these models as well as their deployment methods and the exclusive benefits Azure enterprise customers receive over general users.​ The GPT-4.1 model series represents a new frontier of AI innovation, which Microsoft Azure has introduced to Azure AI Foundry. The models have unique features that distinguish them for different customer groups.

GPT-4.1

GPT-4.1 stands as the current version of the GPT-4o model, which received specialized training to excel at coding and instruction-following tasks. The system aims to enhance agentic workflows while simultaneously boosting developer productivity throughout different project scenarios.

GPT-4.1 includes several fundamental features among them.

  1. GPT-4.1 demonstrates exceptional performance in complex technical and coding problems. The system creates straightforward front-end code while precisely detecting required modifications in existing code and consistently delivers outputs that function correctly after compilation.
  2. The model accepts one million token inputs through its long context feature. The model provides exceptional benefits for tasks needing detailed understanding and multi-step agents that expand context during operation.
  3. The model demonstrates a superior ability to execute instructions, which becomes more effective when working with agents that contain multiple requests. The system operates with enhanced natural understanding, which enables better teamwork with different applications.

The GPT-4.1 Model Series: A New Era of Efficient AI

The models operate within Azure AI Foundry to enable developers who need tools for deploying and managing AI solutions.

The GPT-4.1 series demonstrates a major advancement in optimized AI models. The models excel in various applications because they deliver high performance without excessive resource usage, especially when resource limitations exist. The first version includes three essential variants.

GPT-4.1 is OpenAI’s latest flagship model and an evolution of GPT-4, GPT-4 Turbo, and GPT-4o. It’s engineered for:

  • Code generation
  • Long-context understanding (up to 1 million tokens)
  • Instruction following
  • Multi-modal reasoning (including vision and audio inputs)

Key Benchmark Highlights:

Image Source: Author

GPT 4.1 Mini and Nano: Smarter, Faster, Cheaper

GPT-4.1 Mini

  • Cost reduction: ~83% cheaper than GPT-4o
  • Latency: 50% lower
  • Performance: Matches or exceeds GPT-4o on intelligence evaluations

Perfect for:

  • Chatbots
  • Real-time applications
  • Enterprises looking for high performance at lower cost

GPT-4.1 Nano

  • Fastest and most lightweight model
  • Context window: 1M tokens

Benchmarks:

  • MMLU: 80.1%
  • GPQA: 50.3%
  • Coding (Aider Polyglot): 9.8%

Great for:

  • Autocompletion
  • Classification
  • Local-device or edge AI workloads

Azure AI Foundry: Your Gateway to GPT-4.1

Azure AI Foundry provides a comprehensive platform for developing, deploying, and managing AI solutions. Using GPT-4.1 Mini and Nano within Azure AI Foundry offers several advantages:

  • Simplified Deployment: Azure AI Foundry streamlines the deployment process, allowing you to quickly integrate GPT-4.1 models into your existing infrastructure.
  • Scalability: Leverage Azure’s robust infrastructure to scale your AI applications as needed, ensuring consistent performance even under heavy load.
  • Security and Compliance: Benefit from Azure’s enterprise-grade security and compliance features, protecting your data and ensuring regulatory adherence.

How to Use GPT-4.1 from Azure AI Foundry

To start using GPT-4.1 Mini and Nano, follow these steps:

  1. Access Azure AI Foundry: Log in to your Azure portal and navigate to the Azure AI Foundry service.
  2. Select the GPT-4.1 model: Choose either the Mini or Nano model based on your application’s requirements.
  1. Configure Your Deployment: Customize the deployment settings, including resource allocation and API endpoints.
  2. Integrate into Your Application: Use the provided API endpoints to integrate the GPT-4.1 model into your application.

Pricing on Azure

Understanding the pricing structure is crucial for managing your AI development costs. Here’s a breakdown of the pricing details for the GPT-4.1 models on Azure

Image Source—Author

Use Cases Across Model Tiers:

Fine-Tuning for Your Business Needs

The upcoming days will bring supervised fine-tuning capabilities for GPT-4.1 and 4.1-mini, which enable developers to modify these models according to their business needs. The fine-tuning process allows you to securely modify the base models using your datasets so responses match your organization’s tone and domain terminology and task workflows. The Azure AI Foundry enables you to manage and deploy fine-tuned models while providing complete control over versioning and security and scalability features.

Conclusion

The launch of GPT-4.1, Mini, and Nano on Azure AI Foundry marks a significant leap forward in AI capabilities. These models offer enhanced performance, efficiency, and versatility across a wide array of applications. GPT-4.1 has something to offer whether you are looking to improve your customer service chatbot, develop cutting-edge data analysis tools, or explore new frontiers in machine learning. Try out these models today in Azure AI Foundry to access this powerful tool, stay ahead in the rapidly evolving world of AI, and deploy and build applications using these models.

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} strongTag.remove(); }); }); } removeStrongFromHeadings(); "use strict"; window.onload = () => { /* //This is an object for each category of subjects and in that there are kewords and link to the keywods let keywordsAndLinks = { //you can add more categories and define their keywords and add a link ds: { keywords: [ //you can add more keywords here they are detected and replaced with achor tag automatically 'data science', 'Data science', 'Data Science', 'data Science', 'DATA SCIENCE', ], //we will replace the linktext with the keyword later on in the code //you can easily change links for each category here //(include class="ml-link" and linktext) link: 'linktext', }, ml: { keywords: [ //Add more keywords 'machine learning', 'Machine learning', 'Machine Learning', 'machine Learning', 'MACHINE LEARNING', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, ai: { keywords: [ 'artificial intelligence', 'Artificial intelligence', 'Artificial Intelligence', 'artificial Intelligence', 'ARTIFICIAL INTELLIGENCE', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, nl: { keywords: [ 'NLP', 'nlp', 'natural language processing', 'Natural Language Processing', 'NATURAL LANGUAGE PROCESSING', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, des: { keywords: [ 'data engineering services', 'Data Engineering Services', 'DATA ENGINEERING SERVICES', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, td: { keywords: [ 'training data', 'Training Data', 'training Data', 'TRAINING DATA', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, ias: { keywords: [ 'image annotation services', 'Image annotation services', 'image Annotation services', 'image annotation Services', 'Image Annotation Services', 'IMAGE ANNOTATION SERVICES', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, l: { keywords: [ 'labeling', 'labelling', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, pbp: { keywords: [ 'previous blog posts', 'previous blog post', 'latest', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, mlc: { keywords: [ 'machine learning course', 'machine learning class', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, }; //Articles to skip let articleIdsToSkip = ['post-2651', 'post-3414', 'post-3540']; //keyword with its related achortag is recieved here along with article id function searchAndReplace(keyword, anchorTag, articleId) { //selects the h3 h4 and p tags that are inside of the article let content = document.querySelector(`#${articleId} .entry-content`); //replaces the "linktext" in achor tag with the keyword that will be searched and replaced let newLink = anchorTag.replace('linktext', keyword); //regular expression to search keyword var re = new RegExp('(' + keyword + ')', 'g'); //this replaces the keywords in h3 h4 and p tags content with achor tag content.innerHTML = content.innerHTML.replace(re, newLink); } function articleFilter(keyword, anchorTag) { //gets all the articles var articles = document.querySelectorAll('article'); //if its zero or less then there are no articles if (articles.length > 0) { for (let x = 0; x < articles.length; x++) { //articles to skip is an array in which there are ids of articles which should not get effected //if the current article's id is also in that array then do not call search and replace with its data if (!articleIdsToSkip.includes(articles[x].id)) { //search and replace is called on articles which should get effected searchAndReplace(keyword, anchorTag, articles[x].id, key); 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mlclinks = document.querySelectorAll(`#${c.id} .entry-content a.mlc-link`); llinks = document.querySelectorAll(`#${c.id} .entry-content a.l-link`); pbplinks = document.querySelectorAll(`#${c.id} .entry-content a.pbp-link`); //sending the anchor tags list of each article one by one to remove extra anchor tags removeLinks(dslinks); removeLinks(mllinks); removeLinks(ailinks); removeLinks(nllinks); removeLinks(deslinks); removeLinks(tdlinks); removeLinks(iaslinks); removeLinks(mlclinks); removeLinks(llinks); removeLinks(pbplinks); } }); } //To remove extra achor tags of each category (ds, ml, ai) and only have 2 of each category per article cleanLinks(); */ //Recommended Articles var ctaLinks = [ /* ' ' + '

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