GPT-5 in Azure AI Foundry: Unlocking New Possibilities for Developers and Enterprises
By Naomi Moneypenny, Head of Azure AI Foundry Direct Models and Trupti Parkar, Product Manager, Azure AI Foundry Direct It’s been only 30 days since launch of the GPT-5 models on Azure AI Foundry, and we’re seeing unprecedented uptake in usage both inside Microsoft’s products and across our customers and partners. Not only was this the biggest launch we’ve ever done for a new set of AI models, simultaneously delivering to our customers and inside our own products from GitHub to Microsoft 365; the first month’s momentum we’re seeing in deployment and range of scenarios is skyrocketing, surpassing even what we’ve seen previously for other releases. The arrival of GPT-5 family in Azure AI Foundry represents a significant advancement in how AI can reason, generate, and automate across industries. Whether you’re a developer, researcher, or business leader, GPT-5 brings new capabilities that make intelligent workflows more accessible and impactful. Let’s break down the innovations, features, and real-world impact of GPT-5, and see how it’s changing the game for enterprise and creative applications. Core Capabilities The following section provides an overview of the core capabilities that set GPT-5 on Azure AI Foundry apart. From smarter model selection and enhanced reliability to advanced context handling and remarkable multimodal abilities, these features empower developers and enterprises to harness AI in ways that are more accessible, flexible, and effective than ever before. Smarter Model Selection: The Model Router Advantage One of the biggest headaches in AI development has been picking the right model for the job. Using Azure’s model router solves this. This is a smart system that automatically chooses the best model variant for each request. If you need a quick answer, it’ll use a lightweight model. If your task requires deep analysis or multi-step reasoning, it’ll switch to a more advanced version. This means you get the right balance of speed, cost, and intelligence, without having to specifically consider it for every task. This enables cost-efficient scaling that can help customers automatically get the right GPT-5 variant for their task: whether that’s GPT-5-Reasoning for deep analysis, GPT-5-Mini for faster turnaround, or GPT-5-Nano for lightweight calls. Real world Applicability: A retail chatbot can instantly answer simple product questions using GPT-5-mini, but when a customer asks about a delayed order, the router switches to a deeper reasoning model to analyze logistics and provide a thoughtful response. Less Sycophantic, More Reliable Like any good colleague, instead of always saying what you want to hear, GPT 5 is designed to be direct and honest, even if that means challenging your assumptions. This makes it a more trustworthy partner for production use, especially in scenarios where accuracy and reliability matter. Why it matters: In business, you want AI that can flag potential issues, not just nod along. GPT-5’s improved reliability means fewer mistakes and better decision support. Extended Context and Frontier Deep Reasoning GPT-5 isn’t just bigger, it’s also smarter. With a context window that can handle hundreds of thousands of tokens (~272K tokens), it can process long documents, codebases, or conversations without losing track. This is a game-changer for tasks like legal analysis, scientific research, or reviewing complex software projects. Multimodal and Conversational Power GPT-5 isn’t limited to text. It can understand and generate content across multiple formats including text, images, audio, and even PDFs. The gpt-5-chat variant is built for rich, multi-turn conversations, making it ideal for virtual assistants, customer support, and collaborative agents with ~128k tokens context available. Freeform Tool Calling: Flexible Automation Developers can now integrate GPT-5 with external tools using natural language instructions. Instead of rigid schemas, you can ask the model to run SQL queries, execute Python scripts, or format data—just by describing what you want. This makes automation more intuitive and reduces integration overhead. For example, a data analyst can prompt GPT-5 to pull sales data, run calculations, and generate a chart, all in one without writing complex code or switching between tools. Refer to this blog learn more! Enterprise-Grade Security and Governance Azure AI Foundry wraps GPT-5 in a robust security and compliance framework. Features like content safety, integration with Microsoft Defender, and policy-driven agent services mean organizations can deploy AI confidently, even in regulated industries. Enterprises choose Azure AI Foundry for trusted security and compliance, seamless integration across the Microsoft stack, and governance tools to deploy GPT-5 responsibly. With optimized routing, you always get the best balance of cost and performance. From healthcare to finance, enterprises need AI that’s not just powerful, but also safe and auditable. Azure’s governance tools make this seamlessly possible. Explore the Power of GPT-5 Across Real-World Use Cases Dive into our latest demo showcasing GPT-5 in action showcasing productivity, creativity, customer support, and decision-making scenarios. Watch how it transforms everyday workflows with smarter summarization, seamless task automation, intuitive conversation, and context-aware insights. Whether you're a developer, researcher, or business leader, this video highlights how GPT-5 can elevate your impact with speed, precision, and adaptability. Get Started Today GPT-5 is now available in Azure AI Foundry, with multiple variants to fit your needs. Whether you’re building a simple Q&A bot or a complex agentic workflow, the platform makes it easy to experiment, deploy, and scale. Ready to see what GPT-5 can do? Dive into Azure AI Foundry and start building the future of intelligent applications.The fantastic duo: How to build your modern APIs
🧠 Core Concept The article introduces a Chat Playground System designed to streamline AI development by managing multiple chat scenarios (e.g., technical support, creative writing) from a single dashboard. 🔧 Key Features Scenario-Aware Sessions: Launch pre-configured chat contexts with one click. Dual Access Architecture: FastAPI for RESTful web apps. MCP (Model Context Protocol) for AI tool integration. Streamlit Integration: Wrapped with MCP to allow seamless interaction with AI tools. Automatic Resource Management: Smart port allocation and process cleanup. Context Passing: Uses environment variables and temp JSON files to transfer session data. 🚧 Challenges & Solutions Bridging MCP and Streamlit: Created a wrapper to translate protocol calls and maintain session state. Process Management: Built an async manager to handle multiple Streamlit sessions reliably. Context Transfer: Developed a hybrid system for passing rich context between processes. User Experience: Simplified interface with real-time feedback and intuitive controls. 💡 Lessons Learned Innovation thrives at protocol boundaries. Supporting both REST and MCP broadens adoption. Start simple, scale gradually. Process lifecycle management is critical. Contextual awareness enhances AI utility. Developer experience drives product success. 🔮 Future DirectionsDeepening our Partnership with Mistral AI on Azure AI Foundry
We’re excited to mark a new chapter in our collaboration with Mistral AI, a leading European AI innovator, with the launch of Mistral Document AI in Azure AI Foundry Models. This marks the first in a series of Mistral models coming to Azure as a serverless API, giving customers seamless access to Mistral’s cutting-edge capabilities, fully hosted, managed, and integrated into the Foundry ecosystem. This launch also deepens our support for sovereign cloud customers —especially in Europe. At Microsoft, we believe Sovereign AI is essential for enabling organizations and regulated industries to harness the full potential of AI while maintaining control over their security, data, and governance. As Satya Nadella has said, “We want every country, every organization, to build AI in a way that respects their sovereignty—of data, of applications, and of infrastructure.” By combining Mistral’s state-of-the-art models with Azure’s enterprise-grade reliability and scale we’re enabling customers to confidently deploy AI that meets strict regulatory and data sovereignty requirements. Mistral Document AI By the Mistral AI Team “Enterprises today are overwhelmed with documents—contracts, forms, research papers, invoices—holding critical information that’s often trapped in scanned images and PDFs. With nearly 90% of enterprise data stored in unstructured formats, traditional OCR simply can’t keep up. Mistral Document AI is built with a multimodal approach that combines vision and language understanding, it interprets documents with contextual intelligence and delivers structured outputs that reflect the original layout—tables remain tables, headings remain headings, and images are preserved alongside the text.” Key Capabilities Document Parsing: Mistral Document AI interprets complex layouts and extracts rich structures such as tables, charts, and LaTeX-formatted equations with markdown-style clarity. Multilingual & Multimodal: The model supports dozens of languages and understands both text and visual elements, making it well-suited for global, diverse datasets. Structured Output & Doc-as-Prompt: Mistral Document AI delivers results in structured formats like JSON, enabling easy downstream integration with databases or AI agents. This supports use cases like Retrieval-Augmented Generation (RAG), where document content becomes a prompt for subsequent queries. Use Cases Document Digitization: Process archives of scanned PDFs or handwritten forms into structured digital records. Knowledge Extraction: Transform research papers, technical manuals, or customer guides into machine-readable formats. RAG pipelines and Intelligent Agents: Integrate structured output into pipelines that feed AI systems for Q&A, summarization, and more. Mistral Document AI on Azure AI Foundry You can now access Mistral Document AI’s capabilities through Azure AI Foundry as a serverless Azure model, sold directly from Microsoft. One-Click Deployment (Serverless) – With a few clicks, you can deploy the model as a serverless REST API, without needing to provision any GPU machines or container hosts. This makes it easy to get started. Enterprise-Grade Security & Privacy – Because the model runs within your Azure environment, you get network isolation and data security out of the box. All inferencing happens in Azure’s cloud under your account, so your documents aren’t sent to a third-party server. Azure AI Foundry ensures your data stays private (no data leaves the Azure region you choose) and offers compliance with enterprise security standards. This is critical for sensitive use cases like banking or healthcare documents. Integrated Responsible AI Capabilities – With Mistral Doc AI running in Azure AI Foundry, you can apply Azure’s built-in Responsible AI tools—such as content filtering, safety system monitoring, and evaluation frameworks—to ensure your deployments align with your organization’s ethical and compliance standards. Observability & Monitoring – Foundry’s monitoring features give you full visibility into model usage, performance, and cost. You can track API calls, latency, and error rates, enabling proactive troubleshooting and optimization. Agent Services Enablement – You can connect Mistral Document AI to Azure AI Agent Service, enabling intelligent agents to process, reason over, and act on extracted document data—unlocking new automation and decision-making scenarios. Azure Ecosystem Integration – Once deployed, the Mistral Document AI endpoint can easily plug into your existing Azure workflows. And because it’s part of Foundry, you can manage it alongside other models in a unified way. This interoperability accelerates the development of intelligent applications. Getting Started: Deploying and Using Mistral Document AI on Azure Setting up Mistral Document AI on Azure AI Foundry is straightforward. Here’s a quick guide to get you up and running: Create an Azure AI Foundry workspace – Ensure you have an Azure subscription (pay-as-you-go, not a free trial) and create an AI Foundry hub and project in the Azure portal Deploy the Mistral Document AI model – In the Azure AI Foundry Model Catalog, search for “mistral-document-ai-2505”. Then click the Deploy button. You’ll be prompted to select a pricing plan – choose deploy. Call the Mistral Document AI API – Once deployed, using the model is as easy as calling a REST API. You can do this from any programming language or even a command-line tool like cURL. Integrate and iterate – With the OCR results in hand, you can integrate Mistral Document AI into your workflows. Conclusion Mistral Document AI joins Azure AI Foundry as one of the several tools available to help organizations unlock insights from unstructured documents. This launch reflects our continued commitment to bringing the latest, most capable models into Foundry, giving developers and enterprises more choice than ever. Whether you’re digitizing records, building knowledge bases, or enhancing your AI workflows, Azure AI Foundry offers powerful and accessible solutions. Pricing Model Name Pricing /1K pages mistral-document-ai-2505 Global $3 mistral-document-ai-2505 DataZone $3.3 Resources Explore Mistral Document AI MS Learn Github Code SamplesBlack Forest Labs FLUX.1 Kontext [pro] and FLUX1.1 [pro] Now Available in Azure AI Foundry
We're excited to announce Azure AI Foundry Models now hosts FLUX.1 Kontext [pro] and FLUX1.1 [pro] as direct from Azure, giving developers a first-party, enterprise-ready path to Black Forest Labs’ (BFL) state-of-the-art image models. You get secure endpoints with pay-as-you-go model, Azure billing and Content Safety integration—no GPU wrangling required. Meet the Models Model Core task What’s new Speed Resolution / IO FLUX.1 Kontext [pro] in-context image generation and editing (text + image prompt) single model unifies local edits, full scene re-gen, style transfer, character consistency, iterative editing up to 8× faster than other SOTA editors 1024 x 1024 default; iterative multi-turn editing FLUX1.1 [pro] text-to-image Ultra mode: 4 MP images, Raw mode for natural “camera” look 6× faster than Flux 1-pro; 10 s for a 4 MP frame up to 4 MP, strong prompt adherence Under the hood: Both models sit on a rectified flow transformer backbone—BFL’s answer to diffusion and latent consistency models—yielding better sample diversity and lower inference latency. Image Capabilities & Enterprise Use-case Patterns Exploring the power of FLUX.1 Kontext [pro], we put its in-context image generation and editing capabilities to the test in Azure AI Foundry, transforming simple prompts into stunning, detailed visuals that showcase just how far generative AI has come. Prompt 1: “Two children sailing a paper boat down a winding river, surrounded by lush jungles and curious animals” Prompt 2: “Abstract digital painting of a futuristic city at sunset, with glowing neon lights and flying vehicles, in cyberpunk style” Prompt 3: “Surreal landscape made of floating islands, waterfalls spilling into the sky, and glowing crystal trees” With these Black Forest Labs models now available on Azure AI Foundry, enterprises are enabled to accelerate creative pipelines, generate e-commerce variants, automate marketing workflows and simulate digital twins at scale. Scenario Pattern to Try Creative Pipeline Acceleration Use FLUX 1.1 [pro] for storyboard ideation → pass frames into Kontext [pro] for surgical tweaks without PSD layers. E-commerce Variant Generation Inject product hero shot + prompt to FLUX.1 Kontext [pro] to auto-paint seasonal backdrops while preserving SKU angles. Marketing Automation Pair Azure OpenAI GPT-4o for copy + FLUX images via Logic Apps; send variants to A/B email testing. Digital Twin Simulation Use iterative editing to visualize wear/tear on equipment over time in maintenance portals. Benchmarks & Economics Latency: FLUX.1 Kontext [pro] averages 0.9 s per 1024 x 1024 edit—eight times faster than leading diffusion-based editors on identical A100s. Quality: On KontextBench, FLUX.1 Kontext [pro] ranks #1 on text-guided editing and character-consistency, while FLUX 1.1 [pro] tops aesthetics and prompt-following in T2I tests. Pricing Model Name Meter Type Price FLUX 1.1 [pro] Global 1K Images $40 FLUX.1 Kontext [pro] Global 1K Images $40 Tips for Production Readiness Seed for determinism: Both models accept seed for repeatable outputs—store alongside prompt history. Step budget: Ultra-mode images look best with 40-50 inference steps; FLUX.1 Kontext [pro] edits converge in < 30. Guard-rail chaining: Pipe outputs through Azure AI Content Safety and your own watermark classifier. Caching: For high-traffic apps, cache intermediate latent representations (Kontext) to speed multi-turn edits. Why Azure AI Foundry? Direct from Azure models give you the fastest time-to-value on cutting-edge foundation models, while Azure AI Foundry supplies the right tools, evaluation, deployment, safety, and lifecycle plumbing needed by real-world enterprises. What You Get Why It Matters Unified access All “Direct from Azure” models—OpenAI, DeepSeek, FLUX, Llama, Grok—share the same REST/SDK surface, auth (keys + Entra ID), metrics, and portal UX. Switch or chain models without rewriting code or juggling separate keys/resources. Enterprise-ready SLAs & security Models are hosted and sold by Microsoft under Microsoft Product Terms, with built-in content-safety, RBAC, network isolation, and Azure Monitor logging. Meets compliance officers where they live—no third-party contracts, guaranteed uptime. Scalable deployments Choose pay-as-you-go standard endpoints or capacity-backed PTU deployments that autoscale on A100/H100 pools. Start small in dev, flip to prod traffic without re-deploying. Deep toolchain hook-ups Prompt Flow, ACLI/Bicep/Terraform, Azure DevOps/GitHub Actions, Cost Management reservations, Policy, Purview & Sentinel signals—all work out of the box. Shorter path from hack-day demo to governed production workload. Build Trustworthy AI Solutions Black Forest Labs models on Azure AI Foundry are delivered under the Microsoft Product Terms, giving you enterprise-grade security and compliance out of the box. Each FLUX endpoint offers secure Content Safety controls and guardrails. Runtime protections include built-in content-safety filters, role-based access control, virtual-network isolation, and automatic Azure Monitor logging. Governance signals stream directly into Azure Policy, Purview, and Microsoft Sentinel, giving security and compliance teams real-time visibility. Together, Microsoft's capabilities let you create with more confidence, knowing that privacy, security, and safety are woven into every Black Forest Labs deployment from day one. How to Deploy BFL Models in Azure AI Foundry? If you don’t have an Azure subscription, you can sign up for an Azure account here. Search for the model name in the model catalog in Azure AI Foundry. FLUX.1-Kontext-pro FLUX-1.1-pro Open the model card in the model catalog. Click on deploy to obtain the inference API and key and also to access the playground. You should land on the deployment page that shows you the API and key in less than a minute. You can try out your prompts in the playground. You can use the API and key with various clients. The FLUX family has already re-defined speed/quality trade-offs in open image generation. Landing FLUX.1 Kontext [pro] and FLUX 1.1 [pro] inside Azure AI Foundry brings those capabilities—with Azure’s scalability, governance, and integrated tooling—to every developer building imaging workflows. Happy generating! Learn More ▶️ RSVP for the next Model Monday LIVE on YouTube or On-Demand 👩💻 Explore Azure AI Foundry Models 👋 Continue the conversation on Discord
Announcing the Text PII August preview model release in Azure AI language
Azure AI Language is excited to announce a new preview model release for the PII (Personally Identifiable Information) redaction service, which includes support for more entities and languages, addressing customer-sourced scenarios and international use cases. What’s New | Updated Model 2025-08-01-preview Tier 1 language support for DateOfBirth entity: expanding upon the original English-only support earlier this year, we’ve added support for all Tier 1 languages: French, German, Italian, Spanish, Portuguese, Brazilian Portuguese, and Dutch New entity support: SortCode - a financial code used in the UK and Ireland to identify the specific bank and branch where an account is held. Currently we support this in only English. LicensePlateNumber - the standard alphanumeric code for vehicle identification. Note that our current scope does not support a license plate that contains only letters. Currently we support this in only English. AI quality improvements for financial entities, reducing false positives/negatives These updates respond directly to customer feedback and address gaps in entity coverage and language support. The broader language support enables global deployments and the new entity types allow for more comprehensive data extraction for our customers. This ensures an improved service quality for financial, criminal justice, and many other regulatory use cases, enabling more accurate and reliable service for our customers. Get started A more detailed tutorial and overview of the service feature can be found in our public docs. Learn more about these releases and several others enhancing our Azure AI Language offerings on our What’s new page. Explore Azure AI Language and its various capabilities Access full pricing details on the Language Pricing page Find the list of sensitive PII entities supported Try out Azure AI Foundry for a code-free experience We are looking forward to continuously improving our product offerings and features to meet customer needs and are keen to hear any comments and feedback.The Future of AI: An Intern's Adventure Improving Usability with Agents
As enterprises scale model deployments, managing model versions, SKUs, and regional quotas becomes increasingly complex. In this blog, an intern on the Azure AI Foundry Product Team introduces the Model Operation Agent—an internal proof-of-concept conversational tool that simplifies model lifecycle management. The agent automates discovery, retirement analysis, quota validation, and batch execution, transforming manual operations into guided, intelligent workflows. The post also explores a visionary shift from Infrastructure as Code (IaC) to Infrastructure as Agents (IaA), where natural language and spec-driven deployment could redefine cloud orchestration.
The Future of AI: "Wigit" for computational design and prototyping
Discover how AI is revolutionizing software prototyping. Learn how Wigit, an internal AI-powered tool created with Azure AI Foundry, enables anyone—from designers to product managers—to create live, interactive prototypes in minutes. This blog explores how AI democratizes tool creation, accelerates innovation, and transforms static workflows into dynamic, collaborative environments.Start your Trustworthy AI Development with Safety Leaderboards in Azure AI Foundry
Selecting the right model for your AI application is more than a technical decision—it’s a foundational step in ensuring trust, compliance, and governance in AI. Today, we are excited to announce the public preview of safety leaderboards within Foundry model leaderboards, helping customers incorporate model safety as a first-class criterion alongside quality, cost, and throughput. This feature introduces three key components to support responsible AI development: A dedicated safety leaderboard highlighting the safest models; A quality–safety trade-off chart to balance performance and risk; Five new scenario-specific leaderboards supporting diverse responsible AI scenarios. Prioritize safety with the new leaderboard The safety leaderboard ranks the top models based on their robustness against generating harmful content. This is especially valuable in regulated or high-risk domains—such as healthcare, education, or financial services—where model outputs must meet high safety standards. To ensure benchmark rigor and relevance, we apply a structured filtering and validation process to select benchmarks. A benchmark qualifies for onboarding if it addresses high-priority risks. For safety and responsible AI leaderboards, we look at different benchmarks that can be considered reliable enough to provide some signals on the targeted areas of interest as they relate to safety. Our current safety leaderboard uses the HarmBench benchmark which includes prompts to illicit harmful behaviors from models. The benchmark covers 7 semantic categories of behaviors: Cybercrime & Unauthorized Intrusion Chemical & Biological Weapons/Drugs Copyright Violations Misinformation & Disinformation Harassment & Bullying Illegal Activities General Harm These 7 categories are organized into three broader functional groupings: Standard Harmful Behaviors Contextual Harmful Behaviors Copyright Violations Each grouping is featured in a separate responsible AI scenario leaderboard. We use the prompts evaluators from HarmBench to calculate Attack Success Rate (ASR) and aggregate them across the functional groupings to proxy model safety. Lower ASR values means that a model is more robust against attacks to illicit harmful content. We understand and acknowledge that model safety is a complex topic and has several dimensions. No single current open-source benchmark can test or represent the full spectrum of model safety in different scenarios. Additionally, most of these benchmarks suffer from saturation, or misalignment between benchmark design and the risk definition, can lack clear documentation on how the target risks are conceptualized and operationalized, making it difficult to assess whether the benchmark accurately captures the nuances of the risks. This can lead to either overestimating or underestimating model performance in real-world safety scenarios. While HarmBench dataset covers a limited set of harmful topics, it can still provide a high-level understanding of safety trends. Navigate trade-offs with the quality-safety chart Model selection often involves compromise across multiple criteria. Our new quality–safety trade-off chart helps you make informed decisions by comparing models based on their performance in safety and quality. You can: Identify the safest model measured by Attack Success Rate (lower is better) at a given level of quality performance; Or choose the highest-performing model in quality (higher is better) that still meets a defined safety threshold. Together with the quality-cost trade-off chart, you would be able to find the best trade-off between quality, safety, and cost in selecting a model: Scenario-based responsible AI leaderboards To support customers' diverse responsible AI scenarios, we have added 5 new leaderboards to rank the top models in safety and broader responsibility AI scenarios. Each leaderboard is powered by industry-standard public benchmarks covering: Model robustness against harmful behaviors using HarmBench in 3 scenarios, targeting standard harmful behaviors, contextually harmful behaviors, and copyright violations: Consistent with the safety leaderboard, lower ASR scores for a model mean better robustness against generating harmful content. Model ability to detect toxic content using the Toxigen benchmark: This benchmark targets adversarial and implicit hate speech detection. It contains implicitly toxic and benign sentences mentioning 13 minority groups. Higher accuracy based on F1-score for a model means its better ability to detect toxic content. Model knowledge of sensitive domains including cybersecurity, biosecurity, and chemical security, using the Weapons of Mass Destruction Proxy benchmark (WMDP): A higher accuracy score for a model denotes more knowledge of dangerous capabilities. These scenario leaderboards allow developers, compliance teams, and AI governance stakeholders to align model selection with organizational risk tolerance and regulatory expectations. Building Trustworthy AI Starts with the Right Tools With safety leaderboards now available in public preview, Foundry model leaderboards offer a unified, transparent, and data-driven foundation for selecting models that align with your safety requirements. This addition empowers teams to move from ad hoc evaluation to principled model selection—anchored in industry-standard benchmarks and responsible AI practices. To learn more, explore the methodology documentation and start building AI solutions you—and your stakeholders—can trust.Skill Up On The Latest AI Models & Tools on Model Mondays - Season 2 starts Jun 16!
Quick Links To RSVP for each episode: EP1: Advanced Reasoning Models: https://developer.microsoft.com/en-us/reactor/events/25905/ EP2: Model Context Protocol: https://developer.microsoft.com/en-us/reactor/events/25906/ EP3: SLMs (and Reasoning): https://developer.microsoft.com/en-us/reactor/events/25907/ Get All The Details: https://aka.ms/model-mondays Azure AI Foundry offers the best model choice Did you manage to catch up on all the talks from Microsoft Build 2025? If, like me, you are interested in building AI-driven applications on Azure, you probably started by looking at what’s new in Azure AI Foundry. I recommend you read Asha Sharma’s post for the top 10 things you need to know in this context. And it starts with New Models & Smarter Models! New Models | Azure AI Foundry now has 11,000+ models to choose from – including frontier models from partner providers, and thousands of open-source community variants from Hugging Face. But how do you pick the right one for your needs? Smarter Models | It's not just about model selection, but about the effort required to use the model and validate the results. How can you improve the user experience and build trust and confidence in your customers? Solutions like Model Router and Azure AI Evaluations SDK help. The Challenge? | New models, features, and tools being released daily - the information overload is real. How do we keep up with the latest updates – and skill up on model choices? Say Hello to Model Mondays! Model Mondays is a weekly series with a livestream on Microsoft Reactor (on Mondays) and a follow-up AMA on Azure AI Foundry Discord (on Fridays). Here are the three links to know: Visit the Model Mondays repo: https://aka.ms/model-mondays Watch the Model Mondays playlist: https://aka.ms/model-mondays/playlist Join #model-mondays on Discord: https://aka.ms/model-mondays/discord Visit the playlist or repo to catch up on replays from Season 1 (above). Learn about topics like reasoning models, visual generative models, open-source AI, forecasting models, and local AI development with Visual Studio AI Toolkit. Each 30-minute episode consists of: 5-min Highlights. Catch up on top model-related news from the previous week. 15-min Spotlight. Get a deep dive into a specific model, model family, or related tool. Q&A. Ask questions on chat during the livestream, or join the AMA on Discord on Friday. Register Now to Join Us for Season 2! Microsoft Build showed us that the model catalog is expanding quickly – and so are the tools that are available, to help you select, customize, and evaluate, your AI application. In Season 2, we’re going to dive deeper into advanced topics in models and tools. Some of the topics that we hope to cover include: Advanced Reasoning Models – Deep Research, Visual Reasoning and more. Model Context Protocol – What it is, examples of MCP Servers today. SLMs and Reasoning – We dive into the Phi-4 model ecosystem for insights. Foundry Labs – Explore projects like Magentic-UI and MCP Server. Open-Source Models – Explore the 10K+ community models from Hugging Face. Edge Models – Explore Foundry Local and the rise of on-device AI capabiltiies. Models for AI Agents – Explore the Agent Catalog samples like Red-Teaming Model Playgrounds – Explore Image, Video, Agent, Chat, Language playgrounds Advanced Fine Tuning – Learn to fine GPT models, and use the Foundry Portal AI Developer Experience – Get productive with AI Toolkit & VS Code Extension pack Our first three episodes below are open for registration right now! EP1: Advanced Reasoning Models: https://developer.microsoft.com/en-us/reactor/events/25905/ EP2: Model Context Protocol: https://developer.microsoft.com/en-us/reactor/events/25906/ EP3: SLMs (and Reasoning): https://developer.microsoft.com/en-us/reactor/events/25907/ Let's build our model IQ and get starting developing AI applications on Azure! Want to Build AI Apps - and need resources to accelerate your journey? Chat with us on Azure AI Foundry Discord - https://aka.ms/aifoundrydiscord Provide feedback on our Discussion Forum - https://aka.ms/aifoundryforum Skill up with the Azure AI Foundry Learn Course - http://aka.ms/learnatbuild Review the Azure AI Foundry Documentation - http://aka.ms/AzureAI Download and explore the Azure AI Foundry SDK - http://aka.ms/aifoundrysdk
