Introducing OpenAI’s GPT-image-1.5 in Microsoft Foundry
Developers building with visual AI can often run into the same frustrations: images that drift from the prompt, inconsistent object placement, text that renders unpredictably, and editing workflows that break when iterating on a single asset. That’s why we are excited to announce OpenAI's GPT Image 1.5 is now generally available in Microsoft Foundry. This model can bring sharper image fidelity, stronger prompt alignment, and faster image generation that supports iterative workflows. Starting today, customers can request access to the model and start building in the Foundry platform. Meet GPT Image 1.5 AI driven image generation began with early models like OpenAI's DALL-E, which introduced the ability to transform text prompts into visuals. Since then, image generation models have been evolving to enhance multimodal AI across industries. GPT Image 1.5 represents continuous improvement in enterprise-grade image generation. Building on the success of GPT Image 1 and GPT Image 1 mini, these enhanced models introduce advanced capabilities that cater to both creative and operational needs. The new image model offer: Text-to-image: Stronger instruction following and highly precise editing. Image-to-image: Transform existing images to iteratively refine specific regions Improved visual fidelity: More detailed scenes and realistic rendering. Accelerated creation times: Up to 4x faster generation speed. Enterprise integration: Deploy and scale securely in Microsoft Foundry. GPT Image 1.5 delivers stronger image preservation and editing capabilities, maintaining critical details like facial likeness, lighting, composition, and color tone across iterative changes. You’ll see more consistent preservation of branded logos and key visuals, making it especially powerful for marketing, brand design, and ecommerce workflows—from graphics and logo creation to generating full product catalogs (variants, environments, and angles) from a single source image. Benchmarks Based on an internal Microsoft dataset, GPT Image 1.5 performs higher than other image generation models in prompt alignment and infographics tasks. It focuses on making clear, strong edits – performing best on single-turn modification, delivering the higher visual quality in both single and multi-turn settings. The following results were found across image generation and editing: Text to image Prompt alignment Diagram / Flowchart GPT Image 1.5 91.2% 96.9% GPT Image 1 87.3% 90.0% Qwen Image 83.9% 33.9% Nano Banana Pro 87.9% 95.3% Image editing Evaluation Aspect Modification Preservation Visual Quality Face Preservation Metrics BinaryEval SC (semantic) DINO (Visual) BinaryEval AuraFace Single-turn GPT image 1 99.2% 51.0% 0.14 79.5% 0.30 Qwen image 81.9% 63.9% 0.44 76.0% 0.85 GPT Image 1.5 100% 56.77% 0.14 89.96% 0.39 Multi-turn GPT Image 1 93.5% 54.7% 0.10 82.8% 0.24 Qwen image 77.3% 68.2% 0.43 77.6% 0.63 GPT image 1.5 92.49% 60.55% 0.15 89.46% 0.28 Using GPT Image 1.5 across industries Whether you’re creating immersive visuals for campaigns, accelerating UI and product design, or producing assets for interactive learning GPT Image 1.5 gives modern enterprises the flexibility and scalability they need. Image models can allow teams to drive deeper engagement through compelling visuals, speed up design cycles for apps, websites, and marketing initiatives, and support inclusivity by generating accessible, high‑quality content for diverse audiences. Watch how Foundry enables developers to iterate with multimodal AI across Black Forest Labs, OpenAI, and more: Microsoft Foundry empowers organizations to deploy these capabilities at scale, integrating image generation seamlessly into enterprise workflows. Explore the use of AI image generation here across industries like: Retail: Generate product imagery for catalogs, e-commerce listings, and personalized shopping experiences. Marketing: Create campaign visuals and social media graphics. Education: Develop interactive learning materials or visual aids. Entertainment: Edit storyboards, character designs, and dynamic scenes for films and games. UI/UX: Accelerate design workflows for apps and websites. Microsoft Foundry provides security and compliance with built-in content safety filters, role-based access, network isolation, and Azure Monitor logging. Integrated governance via Azure Policy, Purview, and Sentinel gives teams real-time visibility and control, so privacy and safety are embedded in every deployment. Learn more about responsible AI at Microsoft. Pricing Model Pricing (per 1M tokens) - Global GPT-image-1.5 Input Tokens: $8 Cached Input Tokens: $2 Output Tokens: $32 Cost efficiency improves as well: image inputs and outputs are now cheaper compared to GPT Image 1, enabling organizations to generate and iterate on more creative assets within the same budget. For detailed pricing, refer here. Getting started Learn more about image generation, explore code samples, and read about responsible AI protections here. Try GPT Image 1.5 in Microsoft Foundry and start building multimodal experiences today. Whether you’re designing educational materials, crafting visual narratives, or accelerating UI workflows, these models deliver the flexibility and performance your organization needs.How Do We Know AI Isn’t Lying? The Art of Evaluating LLMs in RAG Systems
🔍 1. Why Evaluating LLM Responses is Hard In classical programming, correctness is binary. Input Expected Result 2 + 2 4 ✔ Correct 2 + 2 5 ✘ Wrong Software is deterministic — same input → same output. LLMs are probabilistic. They generate one of many valid word combinations, like forming sentences from multiple possible synonyms and sentence structures. Example: Prompt: "Explain gravity like I'm 10" Possible responses: Response A Response B Gravity is a force that pulls everything to Earth. Gravity bends space-time causing objects to attract. Both are correct. Which is better? Depends on audience. So evaluation needs to look beyond text similarity. We must check: ✔ Is the answer meaningful? ✔ Is it correct? ✔ Is it easy to understand? ✔ Does it follow prompt intent? Testing LLMs is like grading essays — not checking numeric outputs. 🧠 2. Why RAG Evaluation is Even Harder RAG introduces an additional layer — retrieval. The model no longer answers from memory; it must first read context, then summarise it. Evaluation now has multi-dimensions: Evaluation Layer What we must verify Retrieval Did we fetch the right documents? Understanding Did the model interpret context correctly? Grounding Is the answer based on retrieved data? Generation Quality Is final response complete & clear? A simple story makes this intuitive: Teacher asks student to explain Photosynthesis. Student goes to library → selects a book → reads → writes explanation. We must evaluate: Did they pick the right book? → Retrieval Did they understand the topic? → Reasoning Did they copy facts correctly without inventing? → Faithfulness Is written explanation clear enough for another child to learn from? → Answer Quality One failure → total failure. 🧩 3. Two Types of Evaluation 🔹 Intrinsic Evaluation — Quality of the Response Itself Here we judge the answer, ignoring real-world impact. We check: ✔ Grammar & coherence ✔ Completeness of explanation ✔ No hallucination ✔ Logic flow & clarity ✔ Semantic correctness This is similar to checking how well the essay is written. Even if the result did not solve the real problem, the answer could still look good — that’s why intrinsic alone is not enough. 🔹 Extrinsic Evaluation — Did It Achieve the Goal? This measures task success. If a customer support bot writes a beautifully worded paragraph, but the user still doesn’t get their refund — it failed extrinsically. Examples: System Type Extrinsic Goal Banking RAG Bot Did user get correct KYC procedure? Medical RAG Was advice safe & factual? Legal search assistant Did it return the right section of the law? Technical summariser Did summary capture key meaning? Intrinsic = writing quality. Extrinsic = impact quality. A production-grade RAG system must satisfy both. 📏 4. Core RAG Evaluation Metrics (Explained with Very Simple Analogies) Metric Meaning Analogy Relevance Does answer match question? Ask who invented C++? → model talks about Java ❌ Faithfulness No invented facts Book says started 2004, response says 1990 ❌ Groundedness Answer traceable to sources Claims facts that don’t exist in context ❌ Completeness Covers all parts of question User asks Windows vs Linux → only explains Windows Context Recall / Precision Correct docs retrieved & used Student opens wrong chapter Hallucination Rate Degree of made-up info “Taj Mahal is in London” 😱 Semantic Similarity Meaning-level match “Engine died” = “Car stopped running” 💡 Good evaluation doesn’t check exact wording. It checks meaning + truth + usefulness. 🛠 5. Tools for RAG Evaluation 🔹 1. RAGAS — Foundation for RAG Scoring RAGAS evaluates responses based on: ✔ Faithfulness ✔ Relevance ✔ Context recall ✔ Answer similarity Think of RAGAS as a teacher grading with a rubric. It reads both answer + source documents, then scores based on truthfulness & alignment. 🔹 2. LangChain Evaluators LangChain offers multiple evaluation types: Type What it checks String or regex Basic keyword presence Embedding based Meaning similarity, not text match LLM-as-a-Judge AI evaluates AI (deep reasoning) LangChain = testing toolbox RAGAS = grading framework Together they form a complete QA ecosystem. 🔹 3. PyTest + CI for Automated LLM Testing Instead of manually validating outputs, we automate: Feed preset questions to RAG Capture answers Run RAGAS/LangChain scoring Fail test if hallucination > threshold This brings AI closer to software-engineering discipline. RAG systems stop being experiments — they become testable, trackable, production-grade products. 🚀 6. The Future: LLM-as-a-Judge The future of evaluation is simple: LLMs will evaluate other LLMs. One model writes an answer. Another model checks: ✔ Was it truthful? ✔ Was it relevant? ✔ Did it follow context? This enables: Benefit Why it matters Scalable evaluation No humans needed for every query Continuous improvement Model learns from mistakes Real-time scoring Detect errors before user sees them This is like autopilot for AI systems — not only navigating, but self-correcting mid-flight. And that is where enterprise AI is headed. 🎯 Final Summary Evaluating LLM responses is not checking if strings match. It is checking if the machine: ✔ Understood the question ✔ Retrieved relevant knowledge ✔ Avoided hallucination ✔ Provided complete, meaningful reasoning ✔ Grounded answer in real source text RAG evaluation demands multi-layer validation — retrieval, reasoning, grounding, semantics, safety. Frameworks like RAGAS + LangChain evaluators + PyTest pipelines are shaping the discipline of measurable, reliable AI — pushing LLM-powered RAG from cool demo → trustworthy enterprise intelligence. Useful Resources What is Retrieval-Augmented Generation (RAG) : https://azure.microsoft.com/en-in/resources/cloud-computing-dictionary/what-is-retrieval-augmented-generation-rag/ Retrieval-Augmented Generation concepts (Azure AI) : https://learn.microsoft.com/en-us/azure/ai-services/content-understanding/concepts/retrieval-augmented-generation RAG with Azure AI Search – Overview : https://learn.microsoft.com/en-us/azure/search/retrieval-augmented-generation-overview Evaluate Generative AI Applications (Microsoft Learn – Learning Path) : https://learn.microsoft.com/en-us/training/paths/evaluate-generative-ai-apps/ Evaluate Generative AI Models in Microsoft Foundry Portal : https://learn.microsoft.com/en-us/training/modules/evaluate-models-azure-ai-studio/ RAG Evaluation Metrics (Relevance, Groundedness, Faithfulness) : https://learn.microsoft.com/en-us/azure/ai-foundry/concepts/evaluation-evaluators/rag-evaluators RAGAS – Evaluation Framework for RAG Systems : https://docs.ragas.io/Introducing MAI-Transcribe-1, MAI-Voice-1, and MAI-Image-2 in Microsoft Foundry
Another Step Towards a Complete AI Platform Since inception, our goal with Microsoft Foundry has been to deliver the most complete AI and app agent factory; giving developers access to the latest frontier models, tools, infrastructure, security, and reliability to confidently build and scale their AI solutions. Today, we're taking another step towards that vision by announcing the public preview of three new models from Microsoft AI in Microsoft Foundry: MAI-Transcribe-1: Our first-generation speech recognition model, delivering enterprise-grade accuracy across 25 languages at approximately 50% lower GPU cost than leading alternatives. MAI-Voice-1: A high-fidelity speech generation model capable of producing 60 seconds of expressive audio in under one second on a single GPU. MAI-Image-2: Our highest-capability text-to-image model, which debuted on #3 on the Arena.ai leaderboard for image model families. These are the same models already powering our own products such as Copilot, Bing, PowerPoint, and Azure Speech, and now they're available exclusively on Foundry for developers to use. We can't wait to see what you create with these new multimedia AI models in public preview. Read on for a deeper look at each model's capabilities and how to start building with them in Foundry! MAI-Transcribe-1 & Voice-1: End-To-End Voice Experiences Voice and speech are rapidly becoming the primary interface for the next generation of AI agents, and building great voice experiences requires models that can both speak and listen with precision. With MAI-Voice-1 and MAI-Transcribe-1, Microsoft is delivering exactly that: a comprehensive, first-party audio AI stack purpose-built for developers. MAI-Voice-1 is a lightning-fast speech generation model capable of producing a full minute of audio in under a second on a single GPU; making it one of the most efficient speech systems available today. On the listening side, MAI-Transcribe-1 supports up to 25 languages and is engineered for enterprise-grade reliability across accents, languages, and real-world audio conditions. But what truly sets it apart is its efficiency: when benchmarked against leading transcription models, MAI-Transcribe-1 delivers competitive accuracy at nearly half the GPU cost; an advantage that translates directly into more predictable, scalable pricing for enterprises 1 . Use cases for MAI-Transcribe-1 and MAI-Voice-1 MAI-Voice-1 and MAI-Transcribe-1 are designed for production use across a broad set of real-world scenarios: Conversational AI & Agent Assist: Enable real‑time transcription for IVR systems, virtual assistants, and call‑center workflows to power voice‑driven interfaces, live agent assist, and post‑call summarization. Live Captioning & Accessibility: Deliver real‑time captions for large events, enterprise meetings, and digital communications to improve accessibility and inclusivity across spoken experiences. Media, Subtitling & Archiving: Automate video subtitling, dialogue indexing, and transcription to support scalable content production, searchability, and long‑term media archiving. Education & Training Platforms: Transcribe lectures, learning modules, and certification programs to enhance discoverability, reviewability, and knowledge retention in e‑learning environments. Customer & Market Insights: Convert spoken interactions across research interviews, focus groups, and support channels into structured data for downstream analytics and business intelligence. We're also applying these model capabilities inside Microsoft's own products. MAI-Voice-1 powers the expressive voice experiences in Copilot's Audio Expressions and podcast features. MAI-Transcribe-1 drives Copilot's Voice Mode transcriptions and the new dictation feature, connecting natural voice input with the generative power of Copilot's language models. Both models are available through Azure Speech, where developers can tap into first-party MAI model quality alongside the enterprise-grade reliability, scalability, and 700+ voice gallery of the Azure Speech ecosystem. Try MAI-Transcribe-1 & Voice-1 Today MAI-Transcribe-1 and Voice-1 are available now through Azure Speech. Here's how to get started: Experiment in MAI Playground: Speak, record, or upload audio to see the models in action at the MAI playground. Build in Foundry: deploy MAI-Transcribe-1 and MAI-Voice-1 in Azure Speech. MAI-Transcribe-1 starts at $0.36 USD per hour, while MAI-Voice-1 pricing starts at $22 USD per 1M characters. Developers looking to create custom voices using MAI-Voice-1 can do so through the Personal Voice feature in Azure Speech — including the ability to clone a voice from a short 10-second audio sample. Note that custom voice creation requires an approval process consistent with Microsoft's responsible AI policies. MAI-Image-2: Limitless Creativity For Every Builder Images are at the center of how developers build compelling AI-powered creative experiences; from marketing tools to content platforms to multimodal agents. MAI-Image-2 is Microsoft's answer to that demand. This model has been developed in close collaboration with photographers, designers, and visual storytellers and debuted in the top-3 text-to-image model families on the Arena.ai leaderboard. It raises the bar across the capabilities that matter most in real creative workflows; more natural, photorealistic image generation, stronger in-image text rendering for infographics and diagrams, and greater precision on complex layouts, detailed scenes, and cinematic visuals. Use cases for MAI-Image-2 Developers can integrate MAI-Image-2 across a range of high-impact workflows: Media & Creative Ideation: Designers, illustrators, and creative teams use text‑to‑image generation to explore visual directions, styles, and compositions early in the creative process—moving from concept to exploration faster. Enterprise Communications & Internal Branding: Organizations create custom visuals for internal campaigns, training materials, and executive communications directly from text, ensuring clarity, polish, and brand alignment without relying on stock imagery. UX & Product Concept Visualization: Product teams visualize interfaces, workflows, environments, and conceptual product scenarios from text descriptions, helping teams communicate ideas and align early—before engineering or design resources are engaged. WPP, one of the world's largest marketing and communications groups, is among the first enterprise partners building with MAI-Image-2 at scale, using it to power creative production workflows that previously required significant manual effort. "MAI-Image-2 is a genuine game-changer. It's a platform that not only responds to the intricate nuance of creative direction, but deeply respects the sheer craft involved in generating real-world, campaign-ready images. WPP has some of the best creative talent in the world and MAI-Image-2 is making them even better." -Rob Reilly, Global Chief Creative Officer, WPP We’re also implementing MAI-Image-2 to power image generation within Microsoft’s own products, including Copilot, Bing Image Creator, and PowerPoint, and now you have access to this powerful, cost effective model for your own apps. Try MAI-Image-2 Today Experiment in the MAI Playground: Preview MAI-Image-2 at MAI playground and share feedback directly with the team. Build in Foundry: deploy MAI-Image-2 via the API and start building your apps and agents! MAI-Image-2 starts at $5 USD per 1M tokens for text input and $33 USD per 1M tokens for image output. We look forward to your feedback on these models in Foundry. References: 1 1 st on overall WER on the FLEURS benchmark. Out of the top 25 global languages, MAI-Transcribe-1 ranks 1st by FLEURS in 11 core languages. It wins against Whisper-large-v3 on the remaining 14 and Gemini 3.1 Flash on 11 of those 14.
Now in Foundry: NVIDIA Nemotron-3-Super-120B-A12B, IBM Granite-4.0-1b-Speech, and Sarvam-105B
This week's Model Mondays edition highlights three models now available in Hugging Face collection on Microsoft Foundry: NVIDIA's Nemotron-3-Super-120B-A12B, a hybrid Latent Mixture-of-Experts (MOE) model with 12B active parameters and context handling up to 1 million tokens; IBM Granite's Granite-4.0-1b-Speech, a compact Automatic Speech Recognition (ASR) and Automatic Speech Translation (AST) model that achieves a 5.52% average Word Error Rate (WER) at 280× real-time speed with runtime keyword biasing for domain adaptation; and Sarvam's Sarvam-105B, a 105B Mixture-of-Experts (MoE) model with 10.3B active parameters optimized for complex reasoning and 22 Indian languages, with comparable agentic performance compared to other larger proprietary models on web search and task-planning benchmarks. Models of the week NVIDIA Nemotron-3-Super-120B-A12B Model Specs Parameters / size: 120B total with 12B active Context length: Up to 1M tokens Primary task: Text generation (reasoning, agentic workflows, long-context tasks, tool use, RAG) Why it's interesting (Spotlight) Hybrid Latent MoE architecture with selective attention: Nemotron-3-Super combines interleaved Mamba-2 state-space layers and sparse MoE layers with a select number of full attention layers—a design called Latent MoE. Tokens are routed into a smaller latent space for computation, which improves accuracy per parameter while keeping only 12B parameters active at inference time. Multi-Token Prediction (MTP) heads where the model simultaneously predicts multiple upcoming tokens during training enable native speculative decoding, reducing time-to-first-token on long outputs without a separate draft model. Configurable reasoning mode: The model supports toggling extended chain-of-thought reasoning on or off via the chat template flag enable_thinking. This lets developers suppress the reasoning trace for latency-sensitive tasks while keeping it available for high-stakes or multi-step agentic use cases without loading a separate model. Sustained 1M-token context reliability: On RULER, the standard long-context evaluation suite, Nemotron-3-Super achieves 91.75% at 1M tokens. This makes it practical for full-document retrieval-augmented generation (RAG), long-form code analysis, and extended agentic sessions without chunking or windowing strategies. Try it Use cases Best practices Ultra‑long document ingestion & consolidation (e.g., end‑to‑end review of massive specs, logs, or multi‑volume manuals without chunking) Use the native 1M‑token context to avoid windowing strategies; feed full corpora in one pass to reduce stitching errors. Prefer default decoding for general analysis (NVIDIA recommends temperature≈1.0, top_p≈0.95) before tuning; this aligns with the model’s training and MTP‑optimized generation path. Leverage MTP for throughput (multi‑token prediction improves output speed on long outputs), making single‑pass synthesis practical at scale. Latency‑sensitive chat & tool‑calling at scale (e.g., high‑volume enterprise assistants where response time matters) Toggle reasoning traces intentionally via the chat template (enable_thinking on/off): turn off for low‑latency interactions; on for harder prompts where accuracy benefits from explicit reasoning. Use model‑recommended sampling for tool calls (many guides tighten temperature for tool use) to improve determinism while keeping top_p near 0.95. Rely on the LatentMoE + MTP design to sustain high tokens/sec under load instead of adding a draft model for speculative decoding. IBM Granite-4.0-1b-Speech Model Specs Parameters / size: ~1B Context length: 128K tokens (LLM backbone; audio processed per utterance through the speech encoder) Primary task: Multilingual Automatic Speech Recognition (ASR) and bidirectional Automatic Speech Translation (AST) Why it's interesting (Spotlight) Compact ASR with speculative decoding at near-real-time speed: At roughly 1B parameters, Granite-4.0-1b-Speech achieves a 5.52% average WER across eight English benchmarks at 280× real-time speed (RTFx—the ratio of audio duration processed to wall-clock time) on the Open ASR Leaderboard. Runtime keyword biasing for domain adaptation without fine-tuning: Granite-4.0-1b-Speech accepts a runtime keyword list—proper nouns, brand names, technical terms, acronyms—that adjusts decoding probabilities toward those terms. This allows domain-specific vocabulary to be injected at inference time rather than requiring a fine-tuning run, practical for legal transcription, medical dictation, or financial meeting notes where terminology changes across clients. Bidirectional speech translation across 6 languages in one model: Beyond ASR, the model supports translation both to and from English for French, German, Spanish, Portuguese, and Japanese, plus English-to-Italian and English-to-Mandarin. A single deployed endpoint handles ASR and AST tasks without routing audio to separate models, reducing infrastructure surface area. Try it Test the model in the Hugging Face space before deploying in Foundry here: Sarvam’s Sarvam-105B Model Specs Parameters / size: 105B total with 10.3B active (Mixture of Experts, BF16) Context length: 128K tokens (with YaRN-based long-context extrapolation, scale factor 40) Primary task: Text generation (reasoning, coding, agentic tasks, Indian language understanding) Why it's interesting (Spotlight) Broad Indian language coverage at scale: Sarvam-105B supports English and 22 Indian languages—Hindi, Bengali, Tamil, Telugu, Marathi, Gujarati, Kannada, Malayalam, Punjabi, Odia, Assamese, Urdu, Sanskrit, Maithili, Dogri, Manipuri, Santali, Kashmiri, Nepali, Sindhi, Konkani, and Tibetan—the broadest open-model coverage for this language set at this parameter range. Training explicitly prioritized the Indian context, resulting in reported state-of-the-art performance across these languages for models of comparable size. Strong agentic and web-search performance: Sarvam-105B scores 49.5% on BrowseComp (web research benchmark with search tool access)—substantially above GLM-4.5-Air (21.3%) and Qwen3-Next-80B-A3B-Thinking (38.0%). It also achieves 68.3% average on τ² Bench (multi-domain task-planning benchmark), above GPT-OSS-120B (65.8%) and GLM-4.5-Air (53.2%). This reflects training emphasis on multi-step agentic workflows in addition to standard reasoning. Try it Use cases Best practices Agentic web research & technical troubleshooting (multi-step reasoning, planning, troubleshooting) Use longer context when needed: the model is designed for long-context workflows (up to 128K context with YaRN-based extrapolation noted). Start from the model’s baseline decoding settings (as shown in the model’s sample usage) and adjust for your task: temperature ~0.8, top_p ~0.95, repetition_penalty ~1.0, and set an explicit max_new_tokens (sample shows 2048). Suggestion (general, not stated verbatim in the sources): For agentic tasks, keep the prompt structured (goal → constraints → tools available → required output format), and ask for a short plan + final answer to reduce wandering. Multilingual (Indic) customer support & content generation (English + 22 Indian languages; native-script / romanized / code-mixed inputs) Be explicit about the language/script you want back (e.g., Hindi in Devanagari vs romanized Hinglish), since training emphasized Indian languages and code-mixed/romanized inputs. Provide in-language examples (a short “good response” example in the target language/script) to anchor tone and terminology. (Suggestion—general best practice; not stated verbatim in sources.) Use the model’s baseline generation settings first (sample decoding params) and then tighten creativity for support use cases (e.g., lower temperature) if you see variability. Getting started You can deploy open-source Hugging Face models directly in Microsoft Foundry by browsing the Hugging Face collection in the Foundry model catalog and deploying to managed endpoints in just a few clicks. Or start from the Hugging Face Hub and choose the "Deploy on Microsoft Foundry" option, which brings you straight into Foundry. Learn how to discover models and deploy them using Microsoft Foundry here: Follow along the Model Mondays series and access the GitHub to stay up to date on the latest Read Hugging Face on Azure docs Learn about one-click deployments from the Hugging Face Hub on Microsoft Foundry Explore models in Microsoft FoundryIntroducing OpenAI’s GPT-5.4 mini and GPT-5.4 nano for low-latency AI
Imagine you’re a developer building a research assistant agent on top of GPT‑5.4. The agent retrieves documents, summarizes findings, and answers follow‑up questions across multiple turns. In early testing, the reasoning quality is strong, but as the agent chains together retrieval, tool calls, and generation, latency starts to add up. For interactive experiences, those delays matter—so many teams adopt a multi‑model approach, using a larger model to plan and smaller models to execute subtasks quickly at scale. This is where GPT‑5.4 mini and GPT‑5.4 nano come in. These smaller variants of GPT-5.4 are optimized for developer workloads where latency, cost savings, and agentic design are top of mind. GPT-5.4 mini and GPT-5.4 nano will be rolling out today in Microsoft Foundry, so you can evaluate them in the model catalog and deploy the right option for each workload. GPT-5.4 mini: efficient reasoning for production workflows GPT-5.4 mini distills GPT-5.4’s strengths into a smaller, more efficient model for developer workloads where responsiveness matters. It significantly improves over GPT-5 mini across coding, reasoning, multimodal understanding, and tool use while running about 2X faster. Text and image inputs: build multimodal experiences that combine prompts with screenshots or other images. Tool use and function calling: reliably invoke tools and APIs for agentic workflows. Web search and file search: ground responses in external or enterprise content as part of multi-step tasks. Computer use: support software-interaction loops where the model interprets UI state and takes well-scoped actions. Where GPT-5.4 mini thrives Developer copilots and coding assistants: latency-sensitive coding help, code review suggestions, and fast iteration loops where turnaround time matters. Multimodal developer workflows: applications that interpret screenshots, understand UI state, or process images as part of coding and debugging loops. Computer-use sub-agents: fast executors that take well-scoped actions in software (for example, navigating UIs or completing repetitive steps) within a larger agent loop coordinated by a planner model. GPT-5.4 nano: ultra-low latency automation at scale GPT-5.4 nano is the smallest and fastest model in the lineup, designed for low-latency and low-cost API usage at high throughput. It’s optimized for short-turn tasks like classification, extraction, and ranking, plus lightweight sub-agent work where speed and cost are the priority and extended multi-step reasoning isn’t required. Strong instruction following: consistent adherence to developer intent across short, well-defined interactions. Function and tool calling: dependable invocation of tools and APIs for lightweight agent and automation scenarios. Coding support: optimized performance for common coding tasks where fast turnaround is required. Image understanding: multimodal image input support for basic image interpretation alongside text. Low-latency, low-cost execution: designed to deliver responses quickly and efficiently at scale. Where GPT-5.4 nano thrives GPT-5.4 nano is a strong fit when you need predictable behavior at very high throughput and the task can be expressed as short, well-scoped instructions. Classification and intent detection: fast labeling and routing decisions for high-volume requests. Extraction and normalization: pull structured fields from text, validate formats, and standardize outputs. Ranking and triage: reorder candidates, prioritize tickets/leads, and select best-next actions under tight latency budgets. Guardrails and policy checks: lightweight safety and policy classification, prompt gating, and enforcement decisions before dispatching to tools or larger models. High-volume text processing pipelines: batch transformation, cleanup, deduping, and normalization steps where unit cost and throughput dominate. Routing and prioritization at the edge: select the right downstream workflow (template, queue, or model) for each request under tight latency budgets. Choosing the right GPT-5.4 model Microsoft Foundry makes it possible to deploy multiple GPT-5.4 variants side by side, so teams can route requests to the model that best fits each task. Here’s a practical way to think about the lineup: Model Best suited for Typical workloads GPT-5.4 Sustained, multi-step reasoning with reliable follow-through Agentic workflows, research assistants, document analysis, complex internal tools GPT-5.4 Pro Deeper, higher-reliability reasoning for complex production scenarios High-stakes agentic workflows, long-form analysis and synthesis, complex planning, advanced internal copilots GPT-5.4 mini Balanced reasoning with lower latency for interactive systems Real-time agents, developer tools, retrieval-augmented applications GPT-5.4 nano Ultra-low latency and high throughput High-volume request routing, real-time chat, lightweight automation Responsible AI in Microsoft Foundry At Microsoft, our mission to empower people and organizations remains constant. In the age of AI, trust is foundational to adoption, and earning that trust requires a commitment to transparency, safety, and accountability. Microsoft Foundry provides governance controls, monitoring, and evaluation capabilities to help organizations deploy GPT-5.4 models responsibly in production environments, aligned with Microsoft's Responsible AI principles. Pricing Model Deployment Input (USD $/M tokens) Cached input (USD $/M tokens) Output (USD $/M tokens) GPT-5.4 mini Standard Global $0.75 $0.075 $4.5 GPT-5.4 nano Standard Global $0.20 $0.02 $1.25 The models are also available in Data Zone US. It is rolling out to Data Zone EU. Getting started Explore the models in Microsoft Foundry. Sign in to the Foundry portal and browse the model catalog to evaluate GPT-5.4 mini and GPT-5.4 nano alongside other options, then deploy the right model for each workload.Now in Foundry: VibeVoice-ASR, MiniMax M2.5, Qwen3.5-9B
This week's Model Mondays edition features two models that have just arrived in Microsoft Foundry: Microsoft's VibeVoice-ASR, a unified speech-to-text model that handles 60-minute audio files in a single pass with built-in speaker diarisation and timestamps, and MiniMaxAI's MiniMax-M2.5, a frontier agentic model that leads on coding and tool-use benchmarks with performance comparable to the strongest proprietary models at a fraction of their cost; and Qwen's Qwen3.5-9B, the largest of the Qwen3.5 Small Series. All three represent a shift toward long-context, multi-step capability: VibeVoice-ASR processes up to an hour of continuous audio without chunking; MiniMax-M2.5 handles complex, multi-phase agentic tasks more efficiently than its predecessor—completing SWE-Bench Verified 37% faster than M2.1 with 20% fewer tool-use rounds; and Qwen3.5-9B brings multimodal reasoning on consumer hardware that outperforms much larger models. Models of the week VibeVoice-ASR Model Specs Parameters / size: ~8.3B Primary task: Automatic Speech Recognition with diarisation and timestamps Why it's interesting 60-minute single-pass with full speaker attribution: VibeVoice-ASR processes up to 60 minutes of continuous audio without chunk-based segmentation—yielding structured JSON output with start/end timestamps, speaker IDs, and transcribed content for each segment. This eliminates the speaker-tracking drift and semantic discontinuities that chunk-based pipelines introduce at segment boundaries. Joint ASR, diarisation, and timestamps in one model: Rather than running separate systems for transcription, speaker separation, and timing, VibeVoice-ASR produces all three outputs in a single forward pass. Users can also inject customized hot words—proper nouns, technical terms, or domain-specific phrases—to improve recognition accuracy on specialized content without fine-tuning. Multilingual with native code-switching: Supports 50+ languages with no explicit language configuration required and handles code-switching within and across utterances natively. This makes it suitable for multilingual meetings and international call center recordings without pre-routing audio by language. Benchmarks: On the Open ASR Leaderboard, VibeVoice-ASR achieves an average WER of 7.77% across 8 English datasets (RTFx 51.80), including 2.20% on LibriSpeech Clean and 2.57% on TED-LIUM. On the MLC-Challenge multi-speaker benchmark: DER 4.28%, cpWER 11.48%, tcpWER 13.02%. Try it Use case What to build Best practices Long-form, multi-speaker transcription for meetings + compliance A transcription service that ingests up to 60 minutes of audio per request and returns structured segments with speaker IDs + start/end timestamps + transcript text (ready for search, summaries, or compliance review). Keep audio un-chunked (single-pass) to preserve speaker coherence and avoid stitching drift; rely on the model’s joint ASR, diarisation, and timestamping so you don’t need separate diarisation/timestamp pipelines or postprocessing. Multilingual + domain-specific transcription (global support, technical reviews) A global transcription workflow for multilingual meetings or call center recordings that outputs “who/when/what,” and supports vocabulary injection for product names, acronyms, and technical terms. Provide customized hot words (names / technical terms) in the request to improve recognition on specialized content; don’t require explicit language configuration—VibeVoice-ASR supports 50+ languages and code-switching, so you can avoid pre-routing audio by language. Read more about the model and try out the playground Microsoft for Hugging Face Spaces to try the model for yourself. MiniMax-M2.5 Model Specs Parameters / size: ~229B (FP8, Mixture of Experts) Primary task: Text generation (agentic coding, tool use, search) Why it's interesting? Leading coding benchmark performance: Scores 80.2% on SWE-Bench Verified and 51.3% on Multi-SWE-Bench across 10+ programming languages (Go, C, C++, TypeScript, Rust, Python, Java, and others). In evaluations across different agent harnesses, M2.5 scores 79.7% on Droid and 76.1% on OpenCode—both ahead of Claude Opus 4.6 (78.9% and 75.9% respectively). The model was trained across 200,000+ real-world coding environments covering the full development lifecycle: system design, environment setup, feature iteration, code review, and testing. Expert-level search and tool use: M2.5 achieves industry-leading performance in BrowseComp, Wide Search, and Real-world Intelligent Search Evaluation (RISE), laying a solid foundation for autonomously handling complex tasks. Professional office work: Achieves a 59.0% average win rate against other mainstream models in financial modeling, Word, and PowerPoint tasks, evaluated via the GDPval-MM framework with pairwise comparison by senior domain professionals (finance, law, social sciences). M2.5 was co-developed with these professionals to incorporate domain-specific tacit knowledge—rather than general instruction-following—into the model's training. Try it Use case What to build Best practices Agentic software engineering Multi‑file code refactors, CI‑gated patch generation, long‑running coding agents working across large repositories Start prompts with a clear architecture or refactor goal. Let the model plan before editing files, keep tool calls sequential, and break large changes into staged tasks to maintain state and coherence across long workflows. Autonomous productivity agents Research assistants, web‑enabled task agents, document and spreadsheet generation workflows Be explicit about intent and expected output format. Decompose complex objectives into smaller steps (search → synthesize → generate), and leverage the model’s long‑context handling for multi‑step reasoning and document creation. With these use cases and best practices in mind, the next step is translating them into a clear, bounded prompt that gives the model a specific goal and the right tools to act. The example below shows how a product or engineering team might frame an automated code review and implementation task, so the model can reason through the work step by step and return results that map directly back to the original requirement: “You're building an automated code review and feature implementation system for a backend engineering team. Deploy MiniMax-M2.5 in Microsoft Foundry with access to your repository's file system tools and test runner. Given a GitHub issue describing a new API endpoint requirement, have the model first write a functional specification decomposing the requirement into sub-tasks, then implement the endpoint across the relevant service files, write unit tests with at least 85% coverage, and return a pull request summary explaining each code change and its relationship to the original requirement. Flag any implementation decisions that deviate from the patterns found in the existing codebase.” Qwen3.5-9B Model Specs Parameters / size: 9B Context length: 262,144 tokens natively; extensible to 1,010,000 tokens Primary task: Image-text-to-text (multimodal reasoning) Why it’s interesting High intelligence density at small sizes: Qwen 3.5 Small models show large reasoning gains relative to parameter count, with the 4B and 9B variants outperforming other sub‑10B models on public reasoning benchmarks. Long‑context by default: Support for up to 262K tokens enables long‑document analysis, codebase review, and multi‑turn workflows without chunking. Native multimodal architecture: Vision is built into the model architecture rather than added via adapters, allowing small models (0.8B, 2B) to handle image‑text tasks efficiently. Open and deployable: Apache‑2.0 licensed models designed for local, edge, or cloud deployment scenarios. Benchmarks AI Model & API Providers Analysis | Artificial Analysis Try it Use case When to use Best‑practice prompt pattern Long‑context reasoning Analyzing full PDFs, long research papers, or large code repositories where chunking would lose context Set a clear goal and scope. Ask the model to summarize key arguments, surface contradictions, or trace decisions across the entire document before producing an output. Lightweight multimodal document understanding OCR‑driven workflows using screenshots, scanned forms, or mixed image‑text inputs Ground the task in the artifact. Instruct the model to first describe what it sees, then extract structured information, then answer follow‑up questions. With these best practices in mind, Qwen 3.5-9B demonstrates how compact, multimodal models can handle complex reasoning tasks without chunking or manual orchestration. The prompt below shows how an operations analyst might use the model to analyze a full report end‑to‑end: "You are assisting an operations analyst. Review the attached PDF report and extracted tables. Identify the three largest cost drivers, explain how they changed quarter‑over‑quarter, and flag any anomalies that would require follow‑up. If information is missing, state what data would be needed." Getting started You can deploy open-source Hugging Face models directly in Microsoft Foundry by browsing the Hugging Face collection in the Foundry model catalog and deploying to managed endpoints in just a few clicks. You can also start from the Hugging Face Hub. First, select any supported model and then choose "Deploy on Microsoft Foundry", which brings you straight into Azure with secure, scalable inference already configured. Learn how to discover models and deploy them using Microsoft Foundry documentation. Follow along the Model Mondays series and access the GitHub to stay up to date on the latest Read Hugging Face on Azure docs Learn about one-click deployments from the Hugging Face Hub on Microsoft Foundry Explore models in Microsoft FoundryAnnouncing Fireworks AI on Microsoft Foundry
We’re excited to announce that starting today, Microsoft Foundry customers can access high performance, low latency inference performance of popular open models hosted on the Fireworks cloud from their Foundry projects, and even deploy their own customized versions, too! As part of the Public Preview launch, we’re offering the most popular open models for serverless inference in both pay-per-token (US Data Zone) and provisioned throughput (Global Provisioned Managed) deployments. This includes: Minimax M2.5 🆕 OpenAI’s gpt-oss-120b MoonshotAI’s Kimi-K2.5 DeepSeek-v3.2 For customers that have been looking for a path to production with models they’ve post-trained, you can now import your own fine-tuned versions of popular open models and deploy them at production scale with Fireworks AI on Microsoft Foundry. Serverless (pay-per-token) For customers wanting per-token pricing, we’re launching with Data Zone Standard in the United States. You can make model deployments for Foundry resources in the following regions: East US East US 2 Central US North Central US West US West US 3 Depending on your Azure subscription type, you’ll automatically receive either a 250K or 25K tokens per minute (TPM) quota limit per region and model. (Azure Student and Trial subscriptions will not receive quota at this time.) Per-token pricing rates include input, cached input, and output tokens priced per million tokens. Model Input Tokens ($/1M tokens) Cached Tokens ($/1M tokens) Output Tokens ($/1M tokens) gpt-oss-120b $0.17 $0.09 $0.66 kimi-k2.5 $0.66 $0.11 $3.30 deepseek-v3.2 $0.62 $0.31 $1.85 minimax-m2.5 $0.33 $0.03 $1.32 As we work together with Fireworks to launch the latest OSS models, the supported models will evolve as popular research labs push the frontier! Provisioned Throughput For customers looking to shift or scale production workloads on these models, we’re launching with support for Global provisioned throughput. (Data Zone support will be coming soon!) Provisioned throughput for Fireworks models works just like it does for Foundry models: PTUs are designed to deliver consistent performance in terms of time between token latency. Your existing quota for Global PTUs works as does any reservation commitments! gpt-oss-120b Kimi-K2.5 DeepSeek-v3.2 MiniMax-M2.5 Global provisioned minimum deployment 80 800 1,200 400 Global provisioned scale increment 40 400 600 200 Input TPM per PTU 13,500 530 1,500 3,000 Latency Target Value 99% > 50 Tokens Per Second^ 99% > 50 Tokens Per Second^ 99% > 50 Tokens Per Second^ 99% > 50 Tokens Per Second^ ^ Calculated as p50 request latency on a per 5 minute basis. Custom Models Have you post-trained a model like gpt-oss-120b for your particular use case? With Fireworks on Foundry you can deploy, govern, and scale your custom models all within your Foundry project. This means full fine-tuned versions of models from the following families can be imported and deployed as part of preview: Qwen3-14B OpenAI gpt-oss-120b Kimi K2 and K2.5 DeepSeek v3.1 and v3.2 The new Custom Models page in the Models experience lets you initiate the import process for copying your model weights into your Foundry project. > Models -> Custom Models. For performing a high-speed transfer of the files into Foundry, we’ve added a new feature to Azure Developer CLI (azd) for facilitating the transfer of a directory of model weights. The Foundry UI will give you cli arguments to copy and paste for quickly running azd ai models create pointed to your Foundry project. Enabling Fireworks AI on Microsoft Foundry in your Subscription While in preview, customers must opt-in to integrate their Microsoft Foundry resources with the Fireworks inference cloud to perform model deployments and send inference requests. Opt-in is self-service and available in the Preview features panel within your Azure portal. For additional details on finding and enabling the preview feature, please see the new product documentation for Fireworks on Foundry. Frequently Asked Questions How are Fireworks AI on Microsoft Foundry models different than Foundry Models? Models provided direct from Azure include some open-source models as well as proprietary models from labs like Black Forest Labs, Cohere, and xAI, and others. These models undergo rigorous model safety and risks assessments based on Microsoft’s Responsible AI standard. For customers needing the latest open-source models from emerging frontier labs, break-neck speed, or the ability to deploy their own post-trained custom models, Fireworks delivers best-in-class inference performance. Whether you’re focused on minimizing latency or just staying ahead of the trends, Fireworks AI on Microsoft Foundry gives you additional choice in the model catalog. Still need to quantify model safety and risk? Foundry provides a suite of observability tools with built-in risk and safety evaluators, letting you build AI systems confidently. How is model retirement handled? Customers using serverless per-token offers of models via Fireworks on Foundry will receive notice no less than 30 days before potential model retirement. You’ll be recommended to upgrade to either an equivalent, longer-term supported Azure Direct model or a newer model provided by Fireworks. For customers looking to use models beyond the retirement period, they may do so via Provisioned throughput deployments. How can I get more quota? For TPM quota, you may submit requests via our current Fireworks on Foundry quota form. For PTU quota, please contact your Microsoft account team. Can you support my custom model? Let’s talk! In general, if your model meets Fireworks’ current requirements, we have you covered. You can either reach out to your Microsoft account team or your contacts you may already have with Fireworks.Introducing GPT-5.4 in Microsoft Foundry
Today, we’re thrilled to announce that OpenAI’s GPT‑5.4 is now generally available in Microsoft Foundry: a model designed to help organizations move from planning work to reliably completing it in production environments. As AI agents are applied to longer, more complex workflows; consistency and follow‑through become as important as raw intelligence. GPT‑5.4 combines stronger reasoning with built in computer use capabilities to support automation scenarios, and dependable execution across tools, files, and multi‑step workflows at scale. GPT-5.4: Enhanced Reliability in Production AI GPT-5.4 is designed for organizations operating AI in real production environments, where consistency, instruction adherence, and sustained context are critical to success. The model brings together advances in reasoning, coding, and agentic workflows to help AI systems not only plan tasks but complete them with fewer interruptions and reduced manual oversight. Compared with earlier generations, GPT-5.4 emphasizes stability across longer interactions, enabling teams to deploy agentic AI with greater confidence in day-to-day production use. GPT-5.4 introduces advancements that aim for production grade AI: More consistent reasoning over time, helping maintain intent across multi‑turn and multi‑step interactions Enhanced instruction alignment to reduce prompt tuning and oversight Latency improved performance for responsive, real-time workflows Integrated computer use capabilities for structured orchestration of tools, file access, data extraction, guarded code execution, and agent handoffs More dependable tool invocation reducing prompt tuning and human oversight Higher‑quality generated artifacts, including documents, spreadsheets, and presentations with more consistent structure Together, these improvements support AI systems that behave more predictably as tasks grow in length and complexity. From capability to real-world outcomes GPT‑5.4 delivers practical value across a wide range of production scenarios where follow‑through and reliability are essential: Agent‑driven workflows, such as customer support, research assistance, and business process automation Enterprise knowledge work, including document drafting, data analysis, and presentation‑ready outputs Developer workflows, spanning code generation, refactoring, debugging support, and UI scaffolding Extended reasoning tasks, where logical consistency must be preserved across longer interactions Teams benefit from reduced task drift, fewer mid‑workflow failures, and more predictable outcomes when deploying GPT‑5.4 in production. GPT-5.4 Pro: Deeper analysis for complex decision workflows GPT‑5.4 Pro, a premium variant designed for scenarios where analytical depth and completeness are prioritized over latency. Additional capabilities include: Multi‑path reasoning evaluation, allowing alternative approaches to be explored before selecting a final response Greater analytical depth, supporting problems with trade‑offs or multiple valid solutions Improved stability across long reasoning chains, especially in sustained analytical tasks Enhanced decision support, where rigor and thoroughness outweigh speed considerations Organizations typically select GPT‑5.4 Pro when deeper analysis is required such as scientific research and complex problems, while GPT‑5.4 remains the right choice for workloads that prioritize reliable execution and agentic follow‑through. Microsoft Foundry: Enterprise‑Grade Control from Day One GPT‑5.4 and GPT‑5.4 Pro are available through Microsoft Foundry, which provides the operational controls organizations need to deploy AI responsibly in production environments. Foundry supports policy enforcement, monitoring, version management, and auditability, helping teams manage AI systems throughout their lifecycle. By deploying GPT‑5.4 through Microsoft Foundry, organizations can integrate advanced agentic capabilities into existing environments while aligning with security, compliance, and operational requirements from day one. Customer Spotlight Get Started with GPT-5.4 in Microsoft Foundry GPT‑5.4 sets a new bar for production‑ready AI by combining stronger reasoning with dependable execution. Through enterprise‑grade deployment in Microsoft Foundry, organizations can move beyond experimentation and confidently build AI systems that complete complex work at scale. Computer use capabilities will be introduced shortly after launch. GPT‑5.4 <272K input tokens context length in Microsoft Foundry is priced at $2.50 per million input tokens, $0.25 per million cached input tokens, and $15.00 per million output tokens. The GPT‑5.4 >272K input tokens context length in Microsoft Foundry is priced at $5.00 per million input tokens, $0.50 per million cached input tokens, and $22.50 per million output tokens. The GPT-5.4 is available at launch in Standard Global and Standard Data Zone (US), with additional deployment options coming soon. GPT‑5.4 Pro is priced at $30.00 per million input tokens, and $180.00 per million output tokens, and is available at launch in Standard Global. Build agents for real-world workloads. Start building with GPT‑5.4 in Microsoft Foundry today.NVIDIA NIM for NVIDIA Nemotron, Cosmos, & Microsoft Trellis: Now Available in Azure AI Foundry
We’re excited to announce 7 new powerful NVIDIA NIM™ additions to Azure AI Foundry Models now on Managed Compute. The latest wave of models—NVIDIA Nemotron Nano 9B v2, Llama 3.1 Nemotron Nano VL 8B, Llama 3.3 Nemotron Super 49B v1.5 (coming soon), Cosmos Reason1-7B, Cosmos Predict 2.5 (coming soon), Cosmos Transfer 2.5. (coming soon), and Microsoft Trellis—marks a significant leap forward in intelligent application development. Collectively, these models redefine what’s possible in advanced instruction-following, vision-language understanding, and efficient language modeling, empowering developers to build multimodal, visually rich, and context-aware solutions. By combining robust reasoning, flexible input handling, and enterprise-grade deployment options, these additions accelerate innovation across industries—from robotics and autonomous vehicles to immersive retail and digital twins—enabling smarter, safer, and more adaptive experiences at scale. Meet the Models Model Name Size Primary Use Cases NVIDIA Nemotron Nano 9B v2 Available Now 9B parameters Multilingual Reasoning: Multilingual and code-based reasoning tasks Enterprise Agents: AI and productivity agents Math/Science: Scientific reasoning, advanced math Coding: Software engineering and tool calling Llama 3.3 Nemotron Super 49B v1.5 Available Now 49B Enterprise Agents: AI and productivity agents Math/Science: Scientific reasoning, advanced math Coding: Software engineering and tool calling Llama 3.1 Nemotron Nano VL 8B Available Now 8B Multimodal: Multimodal vision-language tasks, document intelligence and understanding Edge Agents: Mobile and edge AI agents Cosmos Reason1-7B Available Now 7B Robotics: Planning and executing tasks with physical constraints. Autonomous Vehicles: Understanding environments and making decisions. Video Analytics Agents: Extracting insights and performing root-cause analysis from video data. Cosmos Predict 2.5 Coming Soon 2B Generalist Model: World state generation and prediction Cosmos Transfer 2.5 Coming Soon 2B Structural Conditioning: Physical AI Microsoft TRELLIS by Microsoft Research Available Now - Digital Twins: Generate accurate 3D assets from simple prompts Immersive Retail experiences: photorealistic product models for AR, virtual try-ons Game and simulation development: Turn creative ideas into production-ready 3D content Meet the NVIDIA Nemotron Family NVIDIA Nemotron Nano 9B v2: Compact power for high-performance reasoning and agentic tasks NVIDIA Nemotron Nano 9B v2 is a high-efficiency large language model built with a hybrid Mamba-Transformer architecture, designed to excel in both reasoning and non-reasoning tasks. Efficient architecture for high-performance reasoning: Combines Mamba-2 and Transformer components to deliver strong reasoning capabilities with higher throughput. Extensive multilingual and code capabilities: Trained on diverse language and programming data, it performs exceptionally well across tasks involving natural language (English, German, French, Italian, Spanish and Japanese), code generation, and complex problem solving. Reasoning Budget Control: Supports runtime “thinking” budget control. During inference, the user can specify how many tokens the model is allowed to "think" for helping balance speed, cost, and accuracy during inference. For example, a user can tell the model to think for “1K tokens or 3K tokens, etc ” for different use cases with far better cost predictability. Fig 1. provided by NVIDIA Nemotron Nano 9B v2 is built from the ground up with training data spanning 15 languages and 43 programming languages, giving it broad multilingual and coding fluency. Its capabilities were sharpened through advanced post-training techniques like GRPO and DPO enabling it to reason deeply, follow instructions precisely, and adapt dynamically to different tasks. -> Explore the model card on Azure AI Foundry Llama 3.3 Nemotron Super 49B v1.5: High-throughput reasoning at scale Llama 3.3 Nemotron Super 49Bv1.5 (coming soon) is a significantly upgraded version of Llama-3.3-Nemotron-Super-49B-v1 and is a large language model which is a derivative of Meta Llama-3.3-70B-Instruct (the reference model) optimized for advanced reasoning, instruction following, and tool use across a wide range of tasks. Excels in applications such as chatbots, AI agents, and retrieval-augmented generation (RAG) systems Balances accuracy and compute efficiency for enterprise-scale workloads Designed to run efficiently on a single NVIDIA H100 GPU, making it practical for real-world applications Llama-3.3-Nemotron-Super-49B-v1.5 was trained through a multi-phase process combining human expertise, synthetic data, and advanced reinforcement learning techniques to refine its reasoning and instruction-following abilities. Its impressive performance across benchmarks like MATH500 (97.4%) and AIME 2024 (87.5%) highlights its strength in tackling complex tasks with precision and depth. Llama 3.1 Nemotron Nano VL 8B: Multimodal intelligence for edge deployments Llama 3.1 Nemotron Nano VL 8B is a compact vision-language model that excels in tasks such as report generation, Q&A, visual understand, and document intelligence. This model delivers low latency and high efficiency, reducing TCO. This model was trained on a diverse mix of human-annotated and synthetic data, enabling robust performance across multimodal tasks such as document understanding and visual question answering. It achieved strong results on evaluation benchmarks including DocVQA (91.2%), ChartQA (86.3%), AI2D (84.8%), and OCRBenchV2 English (60.1%). -> Explore the model card on Azure AI Foundry What Sets Nemotron Apart NVIDIA Nemotron is a family of open models, datasets, recipes, and tools. 1. Open-source AI technologies: Open models, data, and recipes offer transparency, allowing developers to create trustworthy custom AI for their specific needs, from creating new agents to refining existing applications. Open Weights: NVIDIA Open Model License offers enterprises data control and flexible deployment. Open Data: Models are trained with transparent, permissively-licensed NVIDIA data, available on Hugging Face, ensuring confidence in use. Additionally, it allows developers to train their high-accuracy custom models with these open datasets. Open Recipe: NVIDIA shares development techniques, like NAS, hybrid architecture, Minitron, as well as NeMo tools enabling customization or creation of custom models. 2. Highest Accuracy & Efficiency: Engineered for efficiency, Nemotron delivers industry leading accuracy in the least amount of time for reasoning, vision, and agentic tasks. 3. Run Anywhere On Cloud: Packaged as NVIDIA NIM, for secure and reliable deployment of high-performance AI model inferencing across Azure platforms. Meet the Cosmos Family NVIDIA Cosmos™ is a world foundation model (WFM) development platform to advance physical AI. At its core are Cosmos WFMs, openly available pretrained multimodal models that developers can use out-of-the-box for generating world states as videos and physical AI reasoning, or post-train to develop specialized physical AI models. Cosmos Reason1-7B: Physical AI Cosmos Reason1-7B combines chain-of-thought reasoning, flexible input handling for images and video, a compact 7B parameter architecture, and advanced physical world understanding making it ideal for real-time robotics, video analytics, and AI agents that require contextual, step-by-step decision-making in complex environments. This model transforms how AI and robotics interact with the real world giving your systems the power to not just see and describe, but truly understand, reason, and make decisions in complex environments like factories, cities, and autonomous vehicles. With its ability to analyze video, plan robot actions, and verify safety protocols, Cosmos Reason1-7B helps developers build smarter, safer, and more adaptive solutions for real-world challenges. Cosmos Reason1-7B is physical AI for 4 embodiments: Fig.2 Physical AI Model Strengths Physical World Reasoning: Leverages prior knowledge, physics laws, and common sense to understand complex scenarios. Chain-of-Thought (CoT) Reasoning: Delivers contextual, step-by-step analysis for robust decision-making. Flexible Input: Handles images, video (up to 30 seconds, 1080p), and text with a 16k context window. Compact & Deployable: 7B parameters runs efficiently from edge devices to the cloud. Production-Ready: Available via Hugging Face, GitHub, and NVIDIA NIM; integrates with industry-standard APIs. Enterprise Use Cases Cosmos Reason1-7B is more than a model, it’s a catalyst for building intelligent, adaptive solutions that help enterprises shape a safer, more efficient, and truly connected physical world. Fig.3 Use Cases Reimagine safety and efficiency by empowering AI agents to analyze millions of live streams and recorded videos, instantly verifying protocols and detecting risks in factories, cities, and industrial sites. Accelerate robotics innovation with advanced reasoning and planning, enabling robots to understand their environment, make methodical decisions, and perform complex tasks—from autonomous vehicles navigating busy streets to household robots assisting with daily chores. Transform data curation and annotation by automating the selection, labeling, and critiquing of massive, diverse datasets, fueling the next generation of AI with high-quality training data. Unlock smarter video analytics with chain-of-thought reasoning, allowing systems to summarize events, verify actions, and deliver actionable insights for security, compliance, and operational excellence. -> Explore the model card on Azure AI Foundry Also coming soon to Azure AI Foundry are two models of the Cosmos WFM, designed for world generation and data augmentation. Cosmos Predict 2.5 2B Cosmos Predict 2.5 is a next-generation world foundation model that generates realistic, controllable video worlds from text, images, or videos—all through a unified architecture. Trained on 200M+ high-quality clips and enhanced with reinforcement learning, it delivers stronger physics and prompt alignment while cutting compute cost and post-training time for faster Physical AI workflows. Cosmos Transfer 2.5 2B While Predict 2.5 generates worlds, Transfer 2.5 that transforms structured simulation inputs—like segmentation, depth, or LiDAR maps—into photorealistic synthetic data for Physical AI training and development. What Sets Cosmos Apart Built for Physical AI — Purpose-built for robotics, autonomous systems, and embodied agents that understand physics, motion, and spatial environments. Multimodal World Modeling — Combines images, video, depth, segmentation, LiDAR, and trajectories to create physics-aware, controllable world simulations. Scalable Synthetic Data Generation — Generates diverse, photorealistic data at scale using structured simulation inputs for faster Sim2Real training and adaptation. Microsoft Trellis by Microsoft Research: Enterprise-ready 3D Generation Microsoft Trellis by Microsoft Research is a cutting-edge 3D asset generation model developed by Microsoft Research, designed to create high-quality, versatile 3D assets, complete with shapes and textures, from text or image prompts. Seamlessly integrated within the NVIDIA NIM microservice, Trellis accelerates asset generation and empowers creators with flexible, production-ready outputs. Quickly generate high-fidelity 3D models from simple text or image prompts perfect for industries like manufacturing, energy, and smart infrastructure looking to accelerate digital twin creation, predictive maintenance, and immersive training environments. From virtual try-ons in retail to production-ready assets in media, TRELLIS empowers teams to create stunning 3D content at scale, cutting down production time and unlocking new levels of interactivity and personalization. -> Explore the model card on Azure AI Foundry Pricing The pricing breakdown consists of the Azure Compute charges plus a flat fee per GPU for the NVIDIA AI Enterprise license that is required to use the NIM software. Pay-as-you-go (per gpu hour) NIM Surcharge: $1 per gpu hour Azure Compute charges also apply based on deployment configuration Why use Managed Compute? Managed Compute is a deployment option within Azure AI Foundry Models that lets you run large language models (LLMs), SLMs, HuggingFace models and custom models fully hosted on Azure infrastructure. Azure Managed Compute is a powerful deployment option for models not available via standard (pay-go) endpoints. It gives you: Custom model support: Deploy open-source or third-party models Infrastructure flexibility: Choose your own GPU SKUs (NVIDIA A10, A100, H100) Detailed control: Configure inference servers, protocols, and advanced settings Full integration: Works with Azure ML SDK, CLI, Prompt Flow, and REST APIs Enterprise-ready: Supports VNet, private endpoints, quotas, and scaling policies NVIDIA NIM Microservices on Azure These models are available as NVIDIA NIM™ microservices on Azure AI Foundry. NVIDIA NIM, part of NVIDIA AI Enterprise, is a set of easy-to-use microservices designed for secure, reliable deployment of high-performance AI model inferencing. NIM microservices are pre-built, containerized AI endpoints that simplify deployment and scale across environments. They allow developers to run models securely and efficiently in the cloud environment. If you're ready to build smarter, more capable AI agents, start exploring Azure AI Foundry. Build Trustworthy AI Solutions Azure AI Foundry delivers managed compute designed for enterprise-grade security, privacy, and governance. Every deployment of NIM microservices through Azure AI Foundry is backed by Microsoft’s Responsible AI principles and Secure Future Initiative ensuring fairness, reliability, and transparency so organizations can confidently build and scale agentic AI workflows. How to Get Started in Azure AI Foundry Explore Azure AI Foundry: Begin by accessing the Azure AI Foundry portal and then following the steps below. Navigate to ai.azure.com. Select on top left existing project that is (Hub) resource provider. If you do not have a HUB Project, create new Hub Project using “+ Create New” link. Choose AI Hub Resource: Deploy with NIM Microservices: Use NVIDIA’s optimized containers for secure, scalable deployment. Select Model Catalog from the left sidebar menu: In the "Collections" filter, select NVIDIA to see all the NIM microservices that are available on Azure AI Foundry. Select the NIM you want to use. Click Deploy. Choose the deployment name and virtual machine (VM) type that you would like to use for your deployment. VM SKUs that are supported for the selected NIM and also specified within the model card will be preselected. Note that this step requires having sufficient quota available in your Azure subscription for the selected VM type. If needed, follow the instructions to request a service quota increase. Use this NVIDIA NeMo Agent Toolkit: designed to orchestrate, monitor, and optimize collaborative AI agents. Note about the License Users are responsible for compliance with the terms of NVIDIA AI Product Agreement . Learn More How to Deploy NVIDIA NIM Docs Learn More about Accelerating agentic workflows with Azure AI Foundry, NVIDIA NIM, and NVIDIA NeMo Agent Toolkit Register for Microsoft Ignite 2025Introducing GPT-5.3 Chat in Microsoft Foundry: A more grounded way to chat at enterprise scale
OpenAI’s GPT‑5.3 Chat marks the next step in the GPT‑5 series, designed to deliver more dependable, context‑aware chat experiences for enterprise workloads. The model emphasizes steadier instruction handling and clearer responses, supporting high‑volume, real‑world conversations with greater consistency. GPT‑5.3 Chat is now available via API in Microsoft Foundry, where teams will be able to deploy production‑ready chat and agent experiences that are standardized, governed, and built to scale across the enterprise. What’s new in GPT‑5.3 Chat GPT‑5.3 Chat centers on predictable behavior, relevance, and response quality, helping teams build chat experiences that operate reliably across end‑to‑end workflows while aligning with enterprise safety and compliance expectations. Fewer dead ends, more resolved conversations Reduces unnecessary refusals by responding more proportionately when safe context is available Supports compliant reformulation to keep interactions moving forward Enables end‑to‑end resolution in support, IT, and policy‑driven workflows Grounded answers you can operationalize Combines built‑in web search with model reasoning to surface relevant, actionable information Prioritizes relevance and context over long lists of loosely related results Keeps responses actionable while maintaining enterprise controls and traceability Consistent outputs at scale Improved tone, explanation quality, and instruction following Easier to template, govern, and monitor across apps Less downstream cleanup as usage scales Built for production in Microsoft Foundry Production‑grade infrastructure Observability, failover, quota management, and performance monitoring Designed for real workloads—not experiments Consistent behavior across regions and use cases without re‑architecting Smarter scaling with quota tiers Automatic quota increases with sustained usage Fewer rate‑limit interruptions as demand grows Flexible tiers from Free through Tier 6 Security and compliance by default Identity, access controls, policy enforcement, and data boundaries built in Meets regulated‑industry requirements out of the box Teams can move fast without compromising trust GPT-5.3 Chat in Microsoft Foundry is priced at $1.75 per million input tokens, $0.175 per million cached input tokens, and $14.00 per million output tokens. Ready to build with GPT‑5.3 Chat in Foundry? Start turning reliable conversations into real applications. Explore GPT-5.3 Chat in Microsoft Foundry and begin building production ready‑ chat and agent experiences today.
