Now in Foundry: Qwen3.5 Medium Model Series
This week's spotlight focuses on the Qwen3.5 Medium Model Series, now available in Microsoft Foundry. All three models are Vision Language Models (VLMs) built with early-fusion multimodal training, a 262K native context window, and support for 201 languages, released under Apache 2.0. They range from a 27B dense model optimized for latency-sensitive deployments to a 122B sparse Mixture-of-Experts (MoE) model that activates only 10B parameters per inference call, delivering frontier-class multimodal performance at lower inference cost. Models of the week What the Qwen3.5 Medium Model Series brings Before looking at each model individually, three architectural advances apply to all three and are worth understanding: Unified Vision-Language training (early fusion): Rather than attaching a separate vision encoder to a text model as an afterthought, Qwen3.5 trains on text and image tokens together from the beginning. This can enable stronger reasoning over diagrams, charts, and documents compared to prior Qwen3-VL models, which used a separate vision pipeline. Gated Delta Networks: A novel linear attention mechanism that replaces standard self-attention in most transformer layers. Combined with sparse MoE routing in the two larger models, this hybrid can deliver high-throughput inference at lower latency than equivalent dense architectures. Scalable RL across agent environments: Post-training uses reinforcement learning scaled across large multi-agent environments, contributing to strong performance on instruction-following and agentic task benchmarks. On vision-language reasoning tasks like MMMU and MathVista, these are models small enough to run on local hardware, yet competitive with large, frontier models on multimodal benchmarks. Qwen3.5-27B Model Specs Parameters / size: 27B (dense) Context length: 262,144 tokens Primary task: Vision Language Model (image-text-to-text) Why it's interesting (Spotlight) The dense baseline of the family: Unlike its MoE siblings, Qwen3.5-27B activates all 27B parameters on every forward pass. This gives it predictable, consistent latency per token—an important property for real-time applications and latency-sensitive deployments where MoE routing variability is a concern. Instruction-following leader across the family: Scores 95.0 on IFEval, the highest in the family (vs 93.4 for 122B-A10B and 91.9 for 35B-A3B), and 76.5 on IFBench—making it the strongest choice for structured-output tasks, complex multi-step instruction chains, and agent scaffolds that rely on precise format compliance. Try it You're building a visual quality inspection system for a circuit board manufacturer. Deploy Qwen3.5-27B in Microsoft Foundry to process images captured by a production line camera. Manufacturing sample prompt: Given an image of a printed circuit board (PCB), identify visible defects such as solder bridges, missing components, or misaligned pads. Return a JSON object with defect type, approximate board location, and severity (low / medium / high). Flag any board containing at least one high-severity defect for immediate rework routing. Qwen3.5-35B-A3B Model Specs Parameters / size: 35B total, 3B activated per forward pass (MoE) Context length: 262,144 tokens Primary task: Vision Language Model (image-text-to-text) Why it's interesting (Spotlight) The throughput-optimized pick: With only 3B parameters active per token despite a 35B parameter pool, this model delivers performance close to much larger dense models at substantially lower inference cost. 256-expert MoE routing at compact scale: Routes each token through 8 of 256 routed experts plus 1 shared expert. This breadth of specialization at a scale that only activates 3B parameters makes the 35B-A3B well-suited for high-throughput serving scenarios where cost per inference matters. Try it You're building a contract review assistant for an in-house legal team at a multinational company. Deploy Qwen3.5-35B-A3B in Microsoft Foundry to process scanned contract pages provided as images. Legal document sample prompt: Given a page from a commercial services agreement, extract all defined terms, identify obligation and liability clauses, and flag any termination conditions that deviate from standard commercial practice. Return a structured summary with clause type, section reference, and a one-sentence plain-language explanation of each flagged item. Qwen3.5-122B-A10B Model Specs Parameters / size: 122B total, 10B activated per forward pass (MoE) Context length: 262,144 tokens Primary task: Vision Language Model (image-text-to-text) Why it's interesting (Spotlight) Highest capability in the family: Leads across most benchmarks—76.9 on MMMU-Pro, 83.9 on MMMU, and 86.7 on MMLU-Pro. It also leads the family on SuperGPQA at 67.1 and MMLU-Redux at 94.0, reflecting stronger expert-level knowledge depth. Vision + language reasoning at scale: With the largest routing pool (256 experts, 8 routed + 1 shared) and 10B active parameters, this model handles the most demanding multimodal tasks in the family—long-document analysis over images, multi-step visual reasoning, and complex cross-modal instruction following at extended context lengths. Try it You're building an earnings research assistant for an investment team. Deploy Qwen3.5-122B-A10B in Microsoft Foundry to analyze earnings presentation slides submitted as images. Financial research sample prompt: Given a slide containing a combination of charts, tables, and management commentary, extract key financial metrics (revenue, EBITDA, year-over-year growth), interpret the trend shown in any charts, and generate a two-paragraph analyst summary suitable for a morning briefing. Flag any metrics that deviate materially from prior-quarter guidance and indicate the direction of the deviation. 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 FoundryModel router updates: new regions, a refreshed model pool, and understanding the hill climb
Across Microsoft, "hill climbing" has become shorthand for how real AI progress happens: not in one dramatic leap, but through a disciplined loop. Microsoft AI defines the hill climb as an organization that continuously improves, cycle after cycle, through more compute, better data, and sharper evaluation. Reinforcement fine-tuning in Foundry defines it as improving the deployable model package one measured step at a time across quality, latency, and cost. Different altitudes, same premise: progress is not a one-shot decision. It's a loop. For most teams, the decision of what model to use when is made manually or with custom routing tools. A developer picks a model based on benchmarks, familiarity, or the last launch that made headlines, ships it, and revisits the choice only when something breaks. In an ecosystem where the frontier moves monthly, that decision goes stale fast. Model router in Foundry Models brings the hill climb to the selection layer. What's new: a bigger pool, in more places This release expands where teams can deploy model router, broaden the supported model pool, and delivers updates through a stable endpoint. Together, these changes help teams run production workloads in more locations, match a wider range of tasks to suitable models, and adopt supported updates without changing the application integration. A refreshed model pool. The supported model list now includes Anthropic Claude Opus 4.8 — a high-capability model built for complex reasoning and long-form generation, for scenarios that demand depth, structure, and quality — and the GPT-5.6 family. Just as importantly, the pool is pruned: gpt-5-chat, gpt-5.2-chat, gpt-5.3-chat, Deepseek-V3.1 have been removed from the model router as models reach the end of their lifecycle and are deprecated in Foundry. New region availability. The model router is now available in 28 regions for global standard and 21 data zone regions. For many organizations, inference requests must stay within specific geographic boundaries for regulatory, governance, or customer-trust reasons — and intelligent routing shouldn't force a compromise on that. Find the full list of regions here. The most important detail is what you don't have to do: these updates occur automatically*. The endpoint remains stable as the supported model pool is refreshed, so teams do not need to redeploy the model router to receive the update. Applications can continue using the same integration while the model router evaluates requests against the current supported pool. Teams should continue monitoring routing traces and application outcomes to confirm that quality, cost, latency, and governance requirements are met. *Models from Anthropic still need to be deployed separately before they can be routed to through the model router. Interested in hearing more about what's new to the model router? Tune in for the next episode of Model Mondays with Sanjeev Jagtap and Lee Stott, where they talk all things model router from evaluations to hill climbing. Sign up here to watch live or view the replay: Model Mondays - Spotlight On Model router in Microsoft Foundry | Microsoft Reactor The selection-layer hill climb At the selection layer, a step is a routing decision. Each one is a micro-optimization against your objective, and each one is instrumented: every response from the model router includes a model field showing which underlying model was selected, so the climb leaves a complete, auditable trail. Model router supports three parts of the optimization loop: A/B testing to compare two router configurations to understand quality, cost, and latency tradeoffs; model decomposition to use routing results to decompose a single-model application into a multi-model or multi-agent design, and continuous routing to keep the router in production for continuous per-request selection. Each pattern turns model choice into a measured, repeatable process rather than a fixed decision. 1. A/B Testing Question: Which model or routing strategy should I use in production? A/B testing helps teams compare candidate models, model families, or router configurations against the same workload. Representative traffic is sent to competing deployments, and teams compare quality, cost, latency, and governance outcomes. The goal is to understand tradeoffs and identify the model or routing strategy that best meets workload requirements before promoting it to production. 2. Model Decomposition Question: What work is my application actually doing? Model decomposition uses model router as a diagnostic tool. By deploying the model router against a representative workload and examining routing telemetry, teams can see how requests naturally separate into different task classes. Simple retrieval, classification, and summarization requests may route to smaller models, while reasoning, planning, and agentic workflows may require more capable models. The goal is not to choose a winner, but to understand the structure of the workload and uncover opportunities for optimization, specialization, or architectural improvements. 3. Route continuously Question: Why choose a single model at all? Route continuously is the pattern model router was designed for but is not limited to. Rather than treating model selection as a one-time decision, teams leave the model router in production and allow the best-fit model to be selected for each request. As the supported model pool, regional availability, and platform capabilities evolve, teams can continue using the same endpoint while evaluating whether updates improve workload outcomes. Model selection becomes an ongoing optimization process rather than a project that must be repeated every time the model landscape changes. Together, these patterns illustrate a broader shift: the model router is more than a model. It is a tool for the optimization loop itself, helping teams evaluate tradeoffs, understand workload behavior, test hypotheses, and continuously refine model selection as requirements evolve. Whether used to compare candidate models, decompose applications into specialized tasks, or automate per-request routing in production, model router turns model selection into an observable, measurable, and repeatable process. As the model landscape continues to change, that optimization loop becomes a durable advantage. Getting Started Ready to start your own hill climb? Whether you're exploring the model router for the first time, evaluating routing strategies against your workload, or building a long-term optimization practice, these resources can help you move from experimentation to production with Microsoft Foundry. What's new in model router? Sign up for the next Model Mondays episode for a deep dive into new features, optimization patterns, and the latest model router updates. How do I build agents with model router? Check out the Model Router Agents Lab and build agent experiences with routing, retrieval, web search, tool calling, and multi-agent patterns. How do I evaluate model router? Compare model router against baseline models using your own prompts, then review quality, cost, latency, and routing decisions with the Auto Evaluation Toolkit. How do I optimize model router for my workload? Start your hill-climbing journey with the Model Mastery workshop, where you'll test one optimization lever at a time and measure how each change impacts workload outcomes. How do I build a model router optimization playbook? Explore the Model Releases repository to track new capabilities, understand the optimization question behind each release, and try focused notebooks that demonstrate one optimization lever at a time.What is trending in Hugging Face on Microsoft Foundry? Feb, 2, 2026
Open‑source AI is moving fast, with important breakthroughs in reasoning, agentic systems, multimodality, and efficiency emerging every day. Hugging Face has been a leading platform where researchers, startups, and developers share and discover new models. Microsoft Foundry brings these trending Hugging Face models into a production‑ready experience, where developers can explore, evaluate, and deploy them within their Azure environment. Our weekly Model Monday’s series highlights Hugging Face models available in Foundry, focusing on what matters most to developers: why a model is interesting, where it fits, and how to put it to work quickly. This week’s Model Mondays edition highlights three Hugging Face models, including a powerful Mixture-of-Experts model from Z. AI designed for lightweight deployment, Meta’s unified foundation model for image and video segmentation, and MiniMax’s latest open-source agentic model optimized for complex workflows. Models of the week Z.AI’s GLM-4.7-flash Model Basics Model name: zai-org/GLM-4.7-Flash Parameters / size: 30B total -3B active Default settings: 131,072 max new tokens Primary task: Agentic, Reasoning and Coding Why this model matters Why it’s interesting: It utilizes a Mixture-of-Experts (MoE) architecture (30B total parameters and 3B active parameters) to offer a new option for lightweight deployment. It demonstrates strong performance on logic and reasoning benchmarks, outperforming similar sized models like gpt-oss-20b on AIME 25 and GPQA benchmarks. It supports advanced inference features like "Preserved Thinking" mode for multi-turn agentic tasks. Best‑fit use cases: Lightweight local deployment, multi-turn agentic tasks, and logical reasoning applications. What’s notable: From the Foundry catalog, users can deploy on a A100 instance or unsloth/GLM-4.7-Flash-GGUF on a CPU. ource SOTA scores among models of comparable size. Additionally, compared to similarly sized models, GLM-4.7-Flash demonstrates superior frontend and backend development capabilities. Click to see more: https://docs.z.ai Try it Use case Best‑practice prompt pattern Agentic coding (multi‑step repo work, debugging, refactoring) Treat the model as an autonomous coding agent, not a snippet generator. Explicitly require task decomposition and step‑by‑step execution, then a single consolidated result. Long‑context agent workflows (local or low‑cost autonomous agents) Call out long‑horizon consistency and context preservation. Instruct the model to retain earlier assumptions and decisions across turns. Now that you know GLM‑4.7‑Flash works best when you give it a clear goal and let it reason through a bounded task, here’s an example prompt that a product or engineering team might use to identify risks and propose mitigations: You are a software reliability analyst for a mid‑scale SaaS platform. Review recent incident reports, production logs, and customer issues to uncover edge‑case failures outside normal usage (e.g., rare inputs, boundary conditions, timing/concurrency issues, config drift, or unexpected feature interactions). Prioritize low‑frequency, high‑impact risks that standard testing misses. Recommend minimal, low‑cost fixes (validation, guardrails, fallback logic, or documentation). Deliver a concise executive summary with sections: Observed Edge Cases, Root Causes, User Impact, Recommended Lightweight Fixes, and Validation Steps. Meta's Segment Anything 3 (SAM3) Model Basics Model name: facebook/sam3 Parameters / size: 0.9B Primary task: Mask Generation, Promptable Concept Segmentation (PCS) Why this model matters Why it’s interesting: It handles a vastly larger set of open-vocabulary prompts than SAM 2, and unifies image and video segmentation capabilities. It includes a "SAM 3 Tracker" mode that acts as a drop-in replacement for SAM 2 workflows with improved performance. Best‑fit use cases: Open-vocabulary object detection, video object tracking, and automatic mask generation What’s notable: Introduces Promptable Concept Segmentation (PCS), allowing users to find all matching objects (e.g., "dial") via text prompt rather than just single instances. Try it This model enables users to identify specific objects within video footage and isolate them over extended periods. With just one line of code, it is possible to detect multiple similar objects simultaneously. The accompanying GIF demonstrates how SAM3 efficiently highlights players wearing white on the field as they appear and disappear from view. Additional examples are available at the following repository: https://github.com/facebookresearch/sam3/blob/main/assets/player.gif Use case Best‑practice prompt pattern Agentic coding (multi‑step repo work, debugging, refactoring) Treat SAM 3 as a concept detector, not an interactive click tool. Use short, concrete noun‑phrase concept prompts instead of describing the scene or asking questions. Example prompt: “yellow school bus” or “shipping containers”. Avoid verbs or full sentences. Video segmentation + object tracking Specify the same concept prompt once, then apply it across the video sequence. Do not restate the prompt per frame. Let the model maintain identity continuity. Example: “person wearing a red jersey”. Hard‑to‑name or visually subtle objects Use exemplar‑based prompts (image region or box) when text alone is ambiguous. Optionally combine positive and negative exemplars to refine the concept. Avoid over‑constraining with long descriptions. Using the GIF above as a leading example, here is a prompt that shows how SAM 3 turns raw sports footage into structured, reusable data. By identifying and tracking players based on visual concepts like jersey color so that sports leagues can turn tracked data into interactive experiences where automated player identification can relay stats, fun facts, etc when built into a larger application. Here is a prompt that will allow you to start identifying specific players across video: Act as a sports analytics operator analyzing football match footage. Segment and track all football players wearing blue jerseys across the video. Generate pixel‑accurate segmentation masks for each player and assign persistent instance IDs that remain stable during camera movement, zoom, and player occlusion. Exclude referees, opposing team jerseys, sidelines, and crowd. Output frame‑level masks and tracking metadata suitable for overlays, player statistics, and downstream analytics pipelines. MiniMax AI's MiniMax-M2.1 Model Basics Model name: MiniMaxAI/MiniMax-M2.1 Parameters / size: 229B-10B Active Default settings: 200,000 max new tokens Primary task: Agentic and Coding Why this model matters Why it’s interesting: It is optimized for robustness in coding, tool use, and long-horizon planning, outperforming Claude Sonnet 4.5 in multilingual scenarios. It excels in full-stack application development, capable of architecting apps "from zero to one”. Previous coding models focused on Python optimization, M2.1 brings enhanced capabilities in Rust, Java, Golang, C++, Kotlin, Objective-C, TypeScript, JavaScript, and other languages. The model delivers exceptional stability across various coding agent frameworks. Best‑fit use cases: Lightweight local deployment, multi-turn agentic tasks, and logical reasoning applications. What’s notable: The release of open-source weights for M2.1 delivers a massive leap over M2 on software engineering leaderboards. https://www.minimax.io/ Try it Use case Best‑practice prompt pattern End‑to‑end agentic coding (multi‑file edits, run‑fix loops) Treat the model as an autonomous coding agent, not a snippet generator. Explicitly require task decomposition and step‑by‑step execution, then a single consolidated result. Long‑horizon tool‑using agents (shell, browser, Python) Explicitly request stepwise planning and sequential tool use. M2.1’s interleaved thinking and improved instruction‑constraint handling are designed for complex, multi‑step analytical tasks that require evidence tracking and coherent synthesis, not conversational back‑and‑forth. Long‑context reasoning & analysis (large documents / logs) Declare the scope and desired output structure up front. MiniMax‑M2.1 performs best when the objective and final artifact are clear, allowing it to manage long context and maintain coherence. Because MiniMax‑M2.1 is designed to act as a long‑horizon analytical agent, it shines when you give it a clear end goal and let it work through large volumes of information—here’s a prompt a risk or compliance team could use in practice: You are a financial risk analysis agent. Analyze the following transaction logs and compliance policy documents to identify potential regulatory violations and systemic risk patterns. Plan your approach before executing. Work through the data step by step, referencing evidence where relevant. Deliver a final report with the following sections: Key Risk Patterns Identified, Supporting Evidence, Potential Regulatory Impact, Recommended Mitigations. Your response should be a complete, executive-ready report, not a conversational draft. 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 FoundryNow in Foundry: Microsoft Harrier and NVIDIA EGM-8B
This week's Model Mondays edition highlights three models that share a common thread: each achieves results comparable to larger leading models, as a result of targeted training strategies rather than scale. Microsoft Research's harrier-oss-v1-0.6b from achieves state-of-the-art results on the Multilingual MTEB v2 embedding benchmark at 0.6B parameters through contrastive learning and knowledge distillation. NVIDIA's EGM-8B scores 91.4 average IoU on the RefCOCO visual grounding benchmark by training a small Vision Language Model (VLM) with reinforcement learning to match the output quality of much larger models. Together they represent a practical argument for efficiency-first model development: the gap between small and large models continues to narrow when training methodology is the focus rather than parameter count alone. Models of the week Microsoft Research: harrier-oss-v1-0.6b Model Specs Parameters / size: 0.6B Context length: 32,768 tokens Primary task: Text embeddings (retrieval, semantic similarity, classification, clustering, reranking) Why it's interesting State-of-the-art on Multilingual MTEB v2 from Microsoft Research: harrier-oss-v1-0.6b is a new embedding model released by Microsoft Research, achieving a 69.0 score on the Multilingual MTEB v2 (Massive Text Embedding Benchmark) leaderboard—placing it at the top of its size class at release. It is part of the harrier-oss family spanning harrier-oss-v1-270m (66.5 MTEB v2), harrier-oss-v1-0.6b (69.0), and harrier-oss-v1-27b (74.3), with the 0.6B variant further trained with knowledge distillation from the larger family members. Benchmarks: Multilingual MTEB v2 Leaderboard. Decoder-only architecture with task-instruction queries: Unlike most embedding models that use encoder-only transformers, harrier-oss-v1-0.6b uses a decoder-only architecture with last-token pooling and L2 normalization. Queries are prefixed with a one-sentence task instruction (e.g., "Instruct: Retrieve relevant passages that answer the query\nQuery: ...") while documents are encoded without instructions—allowing the same deployed model to be specialized for retrieval, classification, or similarity tasks through the prompt alone. Broad task coverage across six embedding scenarios: The model is trained and evaluated on retrieval, clustering, semantic similarity, classification, bitext mining, and reranking—making it suitable as a general embedding backbone for multi-task pipelines rather than a single-use retrieval model. One endpoint, consistent embeddings across the stack. 100+ language support: Trained on a large-scale mixture of multilingual data covering Arabic, Chinese, Japanese, Korean, and 100+ additional languages, with strong cross-lingual transfer for tasks that span language boundaries. Try it Use Case Prompt Pattern Multilingual semantic search Prepend task instruction to query; encode documents without instruction; rank by cosine similarity Cross-lingual document clustering Embed documents across languages; apply clustering to group semantically related content Text classification with embeddings Encode labeled examples + new text; classify by nearest-neighbor similarity in embedding space Bitext mining Encode parallel corpora in source and target languages; align segments by embedding similarity Sample prompt for a global enterprise knowledge base deployment: You are building a multilingual internal knowledge base for a global professional services firm. Using the harrier-oss-v1-0.6b endpoint deployed in Microsoft Foundry, encode all internal documents—policy guides, project case studies, and technical documentation—across English, French, German, and Japanese. At query time, prepend the task instruction to each employee query: "Instruct: Retrieve relevant internal documents that answer the employee's question\nQuery: {question}". Retrieve the top-5 most similar documents by cosine similarity and pass them to a language model with the instruction: "Using only the provided documents, answer the question and cite the source document title for each claim. If no document addresses the question, say so." NVIDIA: EGM-8B Model Specs Parameters / size: ~8.8B Context length: 262,144 tokens Primary task: Image-text-to-text (visual grounding) Why it's interesting Preforms well on visual grounding compared to larger models even at its small size: EGM-8B achieves 91.4 average Intersection over Union (IoU) on the RefCOCO benchmark—the standard measure of how accurately a model localizes a described region within an image. Compared to its base model Qwen3-VL-8B-Thinking (87.8 IoU), EGM-8B achieves a +3.6 IoU gain through targeted Reinforcement Learning (RL) fine-tuning. Benchmarks: EGM Project Page. 5.9x faster than larger models at inference: EGM-8B achieves 737ms average latency. The research demonstrates that test-time compute can be scaled horizontally across small models—generating many medium-quality responses and selecting the best—rather than relying on a single expensive forward pass through a large model. Two-stage training: EGM-8B is trained first with Supervised Fine-Tuning (SFT) on detailed chain-of-thought reasoning traces generated by a proprietary VLM, then refined with Group Relative Policy Optimization (GRPO) using a reward function combining IoU accuracy and task success. The intermediate SFT checkpoint is available as nvidia/EGM-8B-SFT for developers who want to experiment with the intermediate stage. Addresses a root cause of small model grounding errors: The EGM research identifies that 62.8% of small model errors on visual grounding stem from complex multi-relational descriptions—where a model must reason about spatial relationships, attributes, and context simultaneously. By focusing test-time compute on reasoning through these complex prompts, EGM-8B closes the gap without increasing the underlying model size. Try it Use Case Prompt Pattern Object localization Submit image + natural language description; receive bounding box coordinates Document region extraction Provide scanned document image + field description; extract specific regions Visual quality control Submit product image + defect description; localize defect region for downstream classification Retail shelf analysis Provide shelf image + product description; return location of specified SKU Sample prompt for a retail and logistics deployment: You are building a visual inspection system for a logistics warehouse. Using the EGM-8B endpoint deployed in Microsoft Foundry, submit each incoming package scan image along with a natural language grounding query describing the region of interest: "Please provide the bounding box coordinate of the region this sentence describes: {description}". For example: "the label on the upper-left side of the box", "the barcode on the bottom face", or "the damaged corner on the right side". Use the returned bounding box coordinates to route each package to the appropriate inspection station based on the identified region. 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 FoundryNow 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 FoundryNow in Foundry: Qwen3-Coder-Next, Qwen3-ASR-1.7B, Z-Image
This week's spotlight features three models from that demonstrate enterprise-grade AI across the full scope of modalities. From low latency coding agents to state-of-the-art multilingual speech recognition and foundation-quality image generation, these models showcase the breadth of innovation happening in open-source AI. Each model balances performance with practical deployment considerations, making them viable for production systems while pushing the boundaries of what's possible in their respective domains. This week's Model Mondays edition highlights Qwen3-Coder-Next, an 80B MoE model that activates only 3B parameters while delivering coding agent capabilities with 256k context; Qwen3-ASR-1.7B, which achieves state-of-the-art accuracy across 52 languages and dialects; and Z-Image from Tongyi-MAI, an undistilled text-to-image foundation model with full Classifier-Free Guidance support for professional creative workflows. Models of the week Qwen: Qwen3-Coder-Next Model Specs Parameters / size: 80B total (3B activated) Context length: 262,144 tokens Primary task: Text generation (coding agents, tool use) Why it's interesting Extreme efficiency: Activates only 3B of 80B parameters while delivering performance comparable to models with 10-20x more active parameters, making advanced coding agents viable for local deployment on consumer hardware Built for agentic workflows: Excels at long-horizon reasoning, complex tool usage, and recovering from execution failures, a critical capability for autonomous development that go beyond simple code completion Benchmarks: Competitive performance with significantly larger models on SWE-bench and coding benchmarks (Technical Report) Try it Use Case Prompt Pattern Code generation with tool use Provide task context, available tools, and execution environment details Long-context refactoring Include full codebase context within 256k window with specific refactoring goals Autonomous debugging Present error logs, stack traces, and relevant code with failure recovery instructions Multi-file code synthesis Describe architecture requirements and file structure expectations Financial services sample prompt: You are a coding agent for a fintech platform. Implement a transaction reconciliation service that processes batches of transactions, detects discrepancies between internal records and bank statements, and generates audit reports. Use the provided database connection tool, logging utility, and alert system. Handle edge cases including partial matches, timing differences, and duplicate transactions. Include unit tests with 90%+ coverage. Qwen: Qwen3-ASR-1.7B Model Specs Parameters / size: 1.7B Context length: 256 tokens (default), configurable up to 4096 Primary task: Automatic speech recognition (multilingual) Why it's interesting All-in-one multilingual capability: Single 1.7B model handles language identification plus speech recognition for 30 languages, 22 Chinese dialects, and English accents from multiple regions—eliminating the need to manage separate models per language Specialized audio versatility: Transcribes not just clean speech but singing voice, songs with background music, and extended audio files, expanding use cases beyond traditional ASR to entertainment and media workflows State-of-the-art accuracy: Outperforms GPT-4o, Gemini-2.5, and Whisper-large-v3 across multiple benchmarks. English: Tedlium 4.50 WER vs 7.69/6.15/6.84; Chinese: WenetSpeech 4.97/5.88 WER vs 15.30/14.43/9.86 (Technical Paper) Language ID included: 97.9% average accuracy across benchmark datasets for automatic language identification, eliminating the need for separate language detection pipelines Try it Use Case Prompt Pattern Multilingual transcription Send audio files via API with automatic language detection Call center analytics Process customer service recordings to extract transcripts and identify languages Content moderation Transcribe user-generated audio content across multiple languages Meeting transcription Convert multilingual meeting recordings to text for documentation Customer support sample prompt: Deploy Qwen3-ASR-1.7B to a Microsoft Foundry endpoint and transcribe multilingual customer service calls. Send audio files via API to automatically detect the language (from 52 supported options including 30 languages and 22 Chinese dialects) and generate accurate transcripts. Process calls from customers speaking English, Spanish, Mandarin, Cantonese, Arabic, French, and other languages without managing separate models per language. Use transcripts for quality assurance, compliance monitoring, and customer sentiment analysis. Tongyi-MAI: Z-Image Model Specs Parameters / size: 6B Context length: N/A (text-to-image) Primary task: Text-to-image generation Why it's interesting Undistilled foundation model: Full-capacity base without distillation preserves complete training signal with Classifier-Free Guidance support (a technique that improves prompt adherence and output quality), enabling complex prompt engineering and negative prompting that distilled models cannot achieve High output diversity: Generates distinct character identities in multi-person scenes with varied compositions, facial features, and lighting, critical for creative applications requiring visual variety rather than consistency Aesthetic versatility: Handles diverse visual styles from hyper-realistic photography to anime and stylized illustrations within a single model, supporting resolutions from 512×512 to 2048×2048 at any aspect ratio with 28-50 inference steps (Technical Paper) Try it Use Case Prompt Pattern Multilingual transcription Send audio files via API with automatic language detection Call center analytics Process customer service recordings to extract transcripts and identify languages Content moderation Transcribe user-generated audio content across multiple languages Meeting transcription Convert multilingual meeting recordings to text for documentation E-commerce sample prompt: Professional product photography of a modern ergonomic office chair in a bright Scandinavian-style home office. Natural window lighting from left, clean white desk with laptop and succulent plant, light oak hardwood floor. Chair positioned at 45-degree angle showing design details. Photorealistic, commercial photography, sharp focus, 85mm lens, f/2.8, soft shadows. 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 FoundryNow in Foundry: IBM Granite 4.1, NVIDIA Nemotron Nano Omni, and Qwen3.6-35B-A3B
This week Microsoft Foundry adds two major model families alongside a reasoning powerhouse that spans the full spectrum from specialized speech and vision to general-purpose coding and long-context analysis. IBM's Granite 4.1 is a famiily of 10: six LLMs across 3B, 8B, and 30B sizes in both full-precision and FP8 variants, plus a safety model, a vision-language model for document extraction, and a multilingual speech recognition model. NVIDIA's Nemotron-3-Nano-Omni-30B-A3B-Reasoning brings multimodal capability—video, audio, image, and text—to a 31B Mamba2-Transformer Hybrid Mixture-of-Experts (MoE) architecture that activates only 3B parameters per forward pass; three variants are available in Foundry (BF16, FP8, and NVFP4), with the FP8 variant featured here. Qwen3.6-35B-A3B is designed for agentic coding among open models, with thinking preservation across conversation turns and a context window extensible to 1 million tokens. Models of the week IBM: granite-4.1-30b Model Specs Parameters / size: 30B (flagship of the Granite 4.1 family) Context length: 131,072 tokens Primary task: Text generation (multilingual instruction following, RAG, tool calling, code, summarization) Why it's interesting The Granite 4.1 release brings 10 models to Microsoft Foundry. The LLM lineup covers granite-4.1-3b-instruct, granite-4.1-8b-instruct, and granite-4.1-30b-instruct with FP8 variants for each, plus granite-guardian-4.1-8b for safety, granite-vision-4.1-4b for document and chart understanding, and granite-speech-4.1-2b for multilingual speech recognition. This is a deployment-ready stack where teams can mix and match model sizes and modalities from a single provider. Strong instruction following and reasoning at the 30B scale: granite-4.1-30b-instruct scores 80.16 on MMLU, 64.09 on MMLU-Pro, 83.74 on Big-Bench Hard (BBH), 77.80 on AGI Eval, 45.76 on GPQA (Graduate-Level Google-Proof Q&A, a graduate-level science reasoning benchmark), and 89.65 average on IFEval (instruction following). These results reflect SFT and reinforcement learning post-training focused specifically on instruction compliance, tool calling accuracy, and long-context retrieval. (View benchmarks here) Enhanced tool calling and 12-language support: Granite 4.1 models are trained for structured function calling and support 12 languages—Arabic, Chinese, Czech, Dutch, English, French, German, Italian, Japanese, Korean, Portuguese, and Spanish—with dialog, extraction, and summarization capabilities. Safety and multimodal coverage within the same family: The inclusion of granite-guardian-4.1-8b (a safety classifier for detecting harmful content and prompt injections), granite-vision-4.1-4b (a Vision Language Model optimized for document extraction from PDFs, charts, and tables), and granite-speech-4.1-2b (a 2B multilingual Automatic Speech Recognition model) means teams can address safety, document parsing, and audio ingestion within the same model family—reducing integration complexity across a full pipeline. Try it Use Case Prompt Pattern Multilingual RAG Submit retrieved document passages in any of 12 supported languages; ask model to synthesize and cite sources Agentic tool calling Provide function definitions + user goal; model plans and executes tool calls in structured format Document extraction (granite-vision-4.1-4b) Submit PDF page image; extract tables, key figures, or form fields as structured JSON Safety classification (granite-guardian-4.1-8b) Pass user input or model output; receive structured risk assessment before serving response Sample prompt for an enterprise document processing deployment: You are building a multilingual document intelligence pipeline for a global financial institution. Using the granite-4.1-30b-instruct endpoint deployed in Microsoft Foundry, submit each incoming policy or regulatory document with the following system instruction: "You are a compliance analysis assistant. Review the document and extract: (1) all regulatory requirements described, (2) the entities to which each requirement applies, (3) any compliance deadlines mentioned, and (4) any penalties or consequences for non-compliance. Return the output as a structured JSON array with one entry per requirement." For documents that include scanned pages, first route them through granite-vision-4.1-4b to extract text and table content before passing to the 30B model for compliance analysis. Pass all user-facing outputs through granite-guardian-4.1-8b to screen for sensitive information before returning results. NVIDIA: Nemotron-3-Nano-Omni-30B-A3B-Reasoning-FP8 Model Specs Parameters / size: 31B total, ~3B activated per forward pass (Mamba2-Transformer Hybrid Mixture-of-Experts) Context length: 256,000 tokens Primary task: Video-audio-image-text-to-text (Multimodal understanding, reasoning, tool calling) Why it's interesting Multimodal input from a single efficient endpoint: Nemotron-3-Nano-Omni-30B-A3B-Reasoning supports video (up to 2 minutes), audio (up to 1 hour), images (RGB), and text—all from a single model deployed in Microsoft Foundry. Three variants are available in Foundry: full-precision BF16, FP8, and NVFP4. Paper: Nemotron Nano Omni technical report. Strong results across vision, document, video, and audio benchmarks: With reasoning mode enabled, the model scores 82.8 on MathVista-MINI (visual math reasoning), 67.04 on OCRBenchV2-EN (document OCR), 63.6 on Charxiv Reasoning (chart understanding), 72.2 on Video MME (video Q&A), 74.52 on Daily Omni (video+audio omnimodal understanding), and 89.39 on VoiceBench (speech instruction following). On OSWorld (GUI agent benchmark measuring autonomous computer use), it scores 47.4—a notable result for a model at the 3B active parameter scale. (Please see above model cards for further benchmark data) Mamba2-Transformer Hybrid MoE for efficient long-context inference: The model's layers alternate between Mamba2 state-space blocks (which process sequences with linear rather than quadratic cost) and standard Transformer attention blocks, combined with Mixture-of-Experts feedforward layers. Only ~3B parameters are activated per token despite the 31B total, making the 256K context window practically usable at lower compute cost than a comparably sized dense model. Word-level timestamps, JSON output, and tool calling for structured media workflows: The model produces word-level timestamps from audio, enabling precise transcript-to-timecode alignment for review and indexing workflows. Combined with JSON-structured output, chain-of-thought reasoning, and native tool calling, it can serve as an agentic step that ingests raw media (meeting recordings, M&E assets, training videos) and produces structured outputs for downstream systems without requiring separate transcription or OCR preprocessing stages. Try it Use Case Prompt Pattern Meeting intelligence Submit audio recording (up to 1 hr); extract transcript with word-level timestamps, action items, and decisions as structured JSON Video content analysis Submit video clip (up to 2 min) + query; retrieve timestamped summary of key events or spoken content Document + audio joint analysis Submit scanned document image alongside narrated walkthrough audio; extract and reconcile information from both modalities Multimodal tool calling Provide tool definitions + combined image/audio input; model reasons over content and executes structured tool calls Sample prompt for a media and compliance deployment: You are building a broadcast compliance review system for a media company. Using the Nemotron-3-Nano-Omni-30B-A3B-Reasoning-FP8 endpoint deployed in Microsoft Foundry, submit each recorded segment as video input with the following instruction: "Review this video segment and produce a compliance report as a JSON object with the following fields: transcript (full text with word-level timestamps), flagged_segments (array of objects with start_time, end_time, content, and reason for flagging), speaker_count (estimated number of distinct speakers), and compliance_summary (overall assessment). Flag any content that includes unverified factual claims, restricted product categories, or regulatory disclosures that may be incomplete." Use the word-level timestamps from the compliance report to route flagged segments directly to the editorial review queue with precise timecode references. Qwen: Qwen3.6-35B-A3B Model Specs Parameters / size: 35B total, 3B activated (Mixture-of-Experts) Context length: 262,144 tokens natively, extensible to 1,010,000 tokens Primary task: Image-text-to-text (agentic coding, reasoning, vision) Why it's interesting Agentic coding improvements over Qwen3.5-35B-A3B: Qwen3.6-35B-A3B scores 73.4 on SWE-bench Verified (vs. 70.0 for Qwen3.5-35B-A3B and 52.0 for Gemma 4 31B), 67.2 on SWE-bench Multilingual (vs. 60.3 and 51.7), and 49.5 on SWE-bench Pro (vs. 44.6 and 35.7). Terminal-Bench 2.0 reaches 51.5 (vs. 40.5 and 42.9). The update targets frontend workflows and repository-level reasoning specifically, areas where earlier Qwen3.5 iterations showed gaps. Blog post: Qwen3.6-35B-A3B. Hybrid architecture: Gated DeltaNet and Mixture-of-Experts: The model's 40 layers alternate between Gated DeltaNet blocks (a form of linear attention that avoids the quadratic cost of standard self-attention), Gated Attention blocks (using Grouped Query Attention with 16 query heads and 2 key-value heads), and Mixture-of-Experts (MoE) feedforward layers with 256 experts (8 routed + 1 shared active per token). Only 3B parameters are activated per forward pass, keeping inference cost comparable to a 3B dense model while retaining the capacity of a 35B model for knowledge and specialization. Thinking preservation across conversation turns: Qwen3.6 introduces an option to retain reasoning context from previous messages in multi-turn conversations. In prior models, chain-of-thought traces were stripped between turns, requiring the model to re-derive context it had already reasoned through. With thinking preservation enabled, iterative coding workflows—such as debugging across multiple exchanges—benefit from accumulated reasoning without repeating earlier analysis. Natively extensible to 1 million token context: The 262K native context is already among the largest in open models at this size, and the architecture supports extension to 1,010,000 tokens. On GPQA Diamond (science reasoning), Qwen3.6-35B-A3B scores 86.0—above both Gemma 4 31B (84.3) and Qwen3.5-27B (85.5)—while matching Gemma 4 31B on MMLU Pro (85.2) and LiveCodeBench v6 (80.4 vs. 80.0). Try it Use Case Prompt Pattern Repository-level code change Provide repository structure + task description; model plans file edits and outputs unified diff Multi-turn iterative debugging Enable thinking preservation; submit failing test + code across multiple turns; accumulate reasoning context Frontend code generation Provide design spec or screenshot + existing codebase context; generate component implementation Long-document reasoning Submit technical specification (up to 262K tokens); ask model to identify ambiguities or implementation gaps Sample prompt for a software engineering deployment: You are building an automated code review and implementation assistant for a platform engineering team. Using the Qwen3.6-35B-A3B endpoint deployed in Microsoft Foundry, enable thinking preservation for multi-turn sessions. In the first turn, submit the repository file tree and a GitHub issue describing a required API endpoint change. Prompt the model: "Review the repository structure and describe your implementation plan, including which files need to change and why." In the second turn, submit the relevant source files and prompt: "Based on your earlier plan, implement the changes and produce a unified diff." In the third turn, submit the test suite and prompt: "Write additional unit tests for the new endpoint, covering edge cases identified in your reasoning." The thinking preservation feature ensures the model carries forward its understanding of the codebase across all three turns without re-explaining context. 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 FoundryNow in Foundry: Command A+ (W4A4), Chandra OCR 2, and GLM-OCR
We are seeing two distinct trends this week. The first is around how low-bit quantization has developed to the point where large reasoning models can fit on a single accelerator with less quality loss. Second, a new wave of OCR-specialized vision-language models are redefining the accuracy-throughput frontier for document understanding. This week we are highlighting three Hugging Face models in Microsoft Foundry: Cohere Labs' Command A+ (W4A4), a 218B-parameter Sparse Mixture-of-Experts (MoE) reasoning model optimized for agentic, multilingual, and reasoning-heavy tasks; Datalab's Chandra OCR 2, a 5.3B vision-language model that converts images and PDFs to markdown, HTML, and JSON while preserving layout, with state-of-the-art results on the olmOCR benchmark and 90+ language coverage; and Z.ai's GLM-OCR, a 0.9B compact OCR model—roughly 6× smaller than Chandra OCR 2—built on the GLM-V encoder–decoder architecture that ranks first on OmniDocBench V1.5 while serving at high concurrency. Models of the week Cohere Labs: Command A+ (W4A4) Model Specs Parameters / size: 218B total, 25B active per token Context length: 128K input, 64K output Primary task: Text generation with vision input, reasoning, and tool use Why it's interesting Efficient, low compute deployment: Command A+ is designed to run on relatively minimal hardware for its size while maintaining high performance. It achieves this through advanced quantization and optimization techniques that reduce compute, latency, and cost. However, reasoning models are especially sensitive to quantization, as errors can accumulate over long decoding sequences. To mitigate this, the quantized student model is post-trained against the full-precision teacher’s output distribution, using fake quantization in the forward pass and straight-through estimators during backpropagation. CohereLabs recommends the W4A4 quantization for its strong balance of speed and latency. Multilingual, multimodal, and reasoning focused performance gains: Command A+ extends to 48 different languages (previously 23) and is built for complex reasoning and multimodal tasks with measureable improvements across document understanding, math reasoning, and enterprise QA workflows. Try it Test this prompt in the CohereLabs Hugging Face Space before deploying the model in Foundry: Sample prompt: You are Command, a legal AI for multinational contract review with access to CONTRACT_VAULT_QUERY and POLICY_TEMPLATE_RETRIEVAL tools. Analyze the input clause by first detecting language and classifying obligation type, then use CONTRACT_VAULT to find comparable {jurisdiction} clauses and retrieve the relevant policy template. Output structured JSON with obligation classification, comparative findings, risk assessment, and English recommendations with exact document citations. Include confidence scores, similarity metrics, and a reasoning trace showing each analysis step. Handle Polish/Japanese legal terminology accurately, preserve legal precision, and ensure all citations reference actual source documents. Use chain-of-thought reasoning, stay within 128K tokens, and never hallucinate references—state limitations explicitly when tools fail. Datalab: Chandra OCR 2 Model Specs Parameters / size: 5.3B Output formats: Markdown, HTML, and JSON Primary task: Document OCR (image-text-to-text) Why it's interesting State-of-the-art on the olmOCR benchmark: Chandra OCR 2 recieved 85.9% bench score on the olmOCR Benchmark and a 77.8% multilingual bench score (12% improvement over Chandra 1). Support for 90 world languages: Indic script, European languages, and languages that read right to left say substantial improvemtns based on Datalab’s internal benchmarking. View the full list of languages and the benchmark results here: Chandra 2 Language List Better complex layout understanding: Handles multi-level tables, nested structures, forms, math, and mixed handwriting with structured outputs (HTML/JSON/Markdown + bounding boxes), removing the need for post-OCR layout reconstruction. Take a look here: Try it Build an automated compliance intake pipeline using Chandra OCR 2 for structured extraction across complex, handwritten and form-based documents. In this scenario, you’re supporting a state election commission processing large volumes of candidate filings submitted as scanned forms or mobile-captured images. These documents often include mixed handwriting quality, checkbox selections, signatures, and structured fields that must be validated for compliance. Chandra OCR 2 can extract both printed and handwritten fields, identify form structure, and capture key elements such as candidate information, filing details, checkbox states, and signed declarations in a consistent JSON format. This structured output can then be passed into a compliance workflow to validate completeness, detect inconsistencies, and flag filings that require manual review. This approach helps streamline high-volume intake while improving accuracy and reducing manual processing across complex document types. Sample prompt: Extract all fields from this filing and return a structured JSON output including form type, candidate name, office sought, district, committee name, treasurer, filing date, checkbox states, and a transcription of the signed declaration. Include bounding boxes for each extracted field. Z.ai: GLM-OCR Model Specs Parameters / size: 0.9B Languages: Chinese, English, French, Spanish, Russian, German, Japanese, Korean Primary task: Document OCR (image-text-to-text) Why it's interesting High accuracy at a compact scale: GLM-OCR achieves a score of 94.62 on OmniDocBench V1.5, showing strong performance on tasks such as formula recognition, table extraction, and document parsing—even at sub-1B scale Designed for structured document understanding: The model performs well across complex document layouts, enabling extraction of tables, forms, and mixed text-image content Optimized training for consistency across tasks: Uses Multi-Token Prediction (MTP) and full-task reinforcement learning to improve stability and accuracy across diverse document types Efficient for real-world deployment: Its smaller footprint makes it well suited for scalable OCR pipelines where cost, latency, and throughput matter Try it Build a high-throughput document ingestion pipeline using GLM-OCR for structured extraction across diverse document types. Imagine you are operating a customer onboarding platform that processes identity documents, invoices, and proof-of-income statements across multiple languages. GLM-OCR can be used to extract key fields—such as names, ID numbers, dates, and addresses—and output them in a consistent structured format for downstream systems. The model’s compact footprint makes it well suited for scaling high-volume OCR workflows, enabling you to process large batches of documents efficiently while maintaining accuracy across layouts like tables, forms, and mixed text-image content. Sample prompt: Extract the following fields from this document and return a structured JSON output: full name, ID number, date of birth, address, document type, and expiration date. Ensure all fields match the document exactly, including formatting. Getting started Whether you are coming straight from the Hugging Face hub or are already in Microsoft Foundry, deploying new open models is getting simpler. You can deploy models on Foundry by browsing the Hugging Face collection in the model catalog or you can choose "Deploy on Microsoft Foundry" on the Hugging Face website, which brings you straight into Foundry with secure, scalable inference already configured. Read the documentation to learn more: Read Hugging Face on Azure docs Learn about one-click deployments from the Hugging Face Hub on Microsoft Foundry Explore models in Microsoft FoundryWhat’s trending on Hugging Face: PubMedBERT Base Embeddings, Paraphrase Multilingual MiniLM, BGE-M3
The embedding model landscape has evolved beyond one-size-fits-all solutions. Today’s developers navigate a set of deliberate trade‑offs: domain specialization to improve accuracy in vertical applications, multilingual capabilities to support global use cases, and retrieval strategies that optimize performance at scale. Once a model demonstrates strong semantic performance, predictable behavior, and broad community support, it often becomes a trusted reference baseline that developers build around and deploy with confidence. This week, we’re not spotlighting models that are new to Microsoft Foundry. Instead, we’re turning our attention to models that have managed to stay relevant in a rapidly expanding sea of options. This week's Model Monday's edition highlights three Hugging Face models including NeuML's PubMedBERT Base Embeddings for domain-specific medical text understanding, Sentence Transformers' Paraphrase Multilingual MiniLM for lightweight cross-lingual semantic similarity, and BAAI's BGE-M3 for multi-functional long-context retrieval across 100+ languages. Models of the week NeuML: PubMedBERT Base Embeddings Model Specs Parameters / size: 109M Context length: 512 tokens Primary task: Embeddings (medical domain) Why it's interesting Domain-specific performance gains: Fine-tuned on PubMed title-abstract pairs, achieving 95.62% average Pearson correlation across medical benchmarks—outperforming general-purpose models like gte-base (95.37%), bge-base-en-v1.5 (93.78%), and all-MiniLM-L6-v2 (93.46%) on medical literature tasks Production-validated for medical RAG: With 141K downloads and deployment in 30+ medical AI applications, this model demonstrates consistent real-world performance for clinical research, drug discovery, and biomedical semantic search pipelines Built on Microsoft's BiomedNLP foundation: Extends BioMed BERT family with sentence-transformers mean pooling, creating 768-dimensional embeddings optimized for medical literature clustering and retrieval Try it Clinical research sample prompt: Industry specific sample prompt: You're building a clinical decision support system for oncology. Deploy PubMedBERT Base Embeddings in Microsoft Foundry to index 50,000 recent cancer research abstracts from PubMed. A physician queries: "What are the cardiotoxicity risks of combining checkpoint inhibitors with anthracycline chemotherapy in elderly patients?" Embed the query, retrieve the top 10 most semantically similar abstracts using cosine similarity, and return citations with PubMed IDs for evidence-based treatment planning. Sentence Transformers: Paraphrase Multilingual MiniLM L12 v2 Model Specs Parameters / size: 117M Context length: 128 tokens Primary task: Embeddings (multilingual, sentence similarity) Why it's interesting Multilingual adoption: Supports 50+ languages including Arabic, Chinese, Hebrew, Hindi, Japanese, Korean, Russian, Thai, and Vietnamese—with 18.4 million downloads last month demonstrating production-scale validation across global deployments Compact architecture for edge deployment: At 117M parameters producing 384-dimensional embeddings, this model balances multilingual coverage with inference efficiency, making it ideal for resource-constrained environments or high-throughput applications Sentence-BERT foundation: Based on the influential Sentence-BERT paper (Reimers & Gurevych, 2019), using siamese BERT networks with mean pooling to create semantically meaningful sentence embeddings for clustering, paraphrase detection, and cross-lingual search Community-proven versatility: With 299 fine-tuned variants and 100+ Spaces implementations, this model serves as a peer reviewed starting point for multilingual semantic similarity tasks, from customer support ticket routing to cross-lingual document retrieval Try it E-commerce sample prompt: You're building a global customer support platform for an e-commerce company operating in 30 countries. Deploy Paraphrase Multilingual MiniLM in Microsoft Foundry to process incoming support tickets in English, Spanish, French, German, Portuguese, Japanese, and Korean. Embed each ticket as a 384-dimensional vector and cluster by semantic similarity to automatically route issues to specialized teams (payment, shipping, returns, technical). Flag duplicate tickets with cosine similarity > 0.85 to prevent redundant responses. BAAI: BGE-M3 Model Specs Parameters / size: ~560M Context length: 8192 tokens Primary task: Embeddings (multi-functional: dense, sparse, multi-vector) Why it's interesting Three retrieval modes in one model: Uniquely supports dense retrieval (1024-dim embeddings), sparse retrieval (lexical matching like BM25), and multi-vector retrieval (ColBERT-style fine-grained matching)—enabling hybrid search pipelines without maintaining separate models or indexes Exceptional long-context capability: 8192-token context window handles full documents, legal contracts, research papers, and lengthy technical content—validated on MLDR (13-language document retrieval) and NarrativeQA (long-form question answering) benchmarks Multilingual dominance: Outperforms OpenAI embeddings on MIRACL multilingual retrieval across 13+ languages and demonstrates strong zero-shot cross-lingual transfer on MKQA. Try it Legal document search sample prompt: You're building a legal document search system for a multinational law firm. Deploy BGE-M3 in Microsoft Foundry to index 5,000 full-length commercial contracts (average 6,000 tokens each) in English, French, German, and Spanish. A lawyer queries: "Find all force majeure clauses that exclude liability for pandemics or global health emergencies." Use hybrid retrieval: (1) dense embeddings for semantic similarity to capture concept variations like "Act of God" or "unforeseen circumstances", (2) sparse retrieval for exact keyword matches on "force majeure", "pandemic", "health emergency". Combine scores with weighted sum (0.6 dense + 0.4 sparse) and return top 15 contract sections with clause numbers and jurisdiction metadata. 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 FoundryNow in Foundry: Cohere Transcribe, Nanbeige 4.1-3B, and Octen Embedding
This week's Model Mondays edition spans three distinct layers of the AI application stack: Cohere's cohere-transcribe, a 2B Automatic Speech Recognition (ASR) model that ranks first on the Open ASR Leaderboard across 14 languages; Nanbeige's Nanbeige4.1-3B, a compact 3B reasoning model that outperforms models ten times its size on coding, math, and deep-search benchmarks; and Octen's Octen-Embedding-0.6B, a lightweight text embedding model that achieves strong retrieval scores across 100+ languages and industry-specific domains. Together, these three models illustrate how developers can build full AI pipelines—from audio ingestion to language reasoning to semantic retrieval—entirely with open-source models deployed through Microsoft Foundry. Each operates in a different modality and fills a distinct architectural role, making this week's selection especially well-suited for teams assembling production-grade systems across speech, text, and search. Models of the week Cohere's cohere-transcribe-03-2026 Model Specs Parameters / size: 2B Primary task: Automatic Speech Recognition (audio-to-text) Why it's interesting Top-ranked on the Open ASR Leaderboard: cohere-transcribe-03-2026 achieves a 5.42% average Word Error Rate (WER) across 8 English benchmark datasets as of March 26, 2026—placing it first among open models. It reaches 1.25% WER on LibriSpeech Clean and 8.15% on AMI (meeting transcription), demonstrating consistent accuracy across both clean speech and real-world, multi-speaker environments. Benchmarks: Open ASR Leaderboard. 14 languages with a dedicated encoder-decoder architecture: The model uses a large Conformer encoder for acoustic representation extraction paired with a lightweight Transformer decoder for token generation, trained from scratch on 14 languages covering European, East Asian (Chinese Mandarin, Japanese, Korean, Vietnamese), and Arabic. Unlike general-purpose models adapted for ASR, this dedicated architecture makes it efficient without sacrificing accuracy. Long-form audio with automatic chunking: Audio longer than 35 seconds is automatically split into overlapping chunks and reassembled into a coherent transcript—no manual preprocessing required. Batched inference, punctuation control, and per-language configuration are all supported through the standard API. Try it Click on the window above, upload an audio file, and watch how quickly the model transcribes it for you. Or click the link to experiment with the Cohere Transcribe Space and record audio directly from your device. Use Case Prompt Pattern Meeting transcription Submit recorded audio with language tag; retrieve timestamped transcript per speaker turn Call center quality review Batch-process customer call recordings, extract transcript, pass to classification model Medical documentation Transcribe clinical encounters; feed transcript into summarization or structured note pipeline Multilingual content indexing Process podcasts or video audio in any of 14 supported languages; store as searchable text Sample prompt for a legal services deployment: You are building a contract negotiation assistant. A client submits a recorded audio of a 45-minute supplier negotiation call. Using the cohere-transcribe-03-2026 endpoint deployed in Microsoft Foundry, transcribe the call with punctuation enabled for the English audio. Once the transcript is available, pass it to a downstream language model with the following instruction: "Identify all pricing commitments, delivery deadlines, and liability clauses mentioned in this negotiation transcript. For each, note the speaker's position (client or supplier) and flag any terms that appear ambiguous or require legal review." Nanbeige's Nanbeige4.1-3B Model Specs Parameters / size: 3B Context length: 131,072 tokens Primary task: Text generation (reasoning, coding, tool use, deep search) Why it's interesting Reasoning performance that exceeds its size class: Nanbeige4.1-3B scores 76.9 on LiveCodeBench-V6, these results suggest that targeted post-training using Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL) on a focused dataset can yield improvements that scale-based approaches cannot replicate at equivalent parameter counts. Read the technical report: https://huggingface.co/papers/2602.13367. Strong preference alignment at the 3B scale: On Arena-Hard-v2, Nanbeige4.1-3B scores 73.2, compared to 56.0 for Qwen3-32B and 60.2 for Qwen3-30B-A3B—both significantly larger models. This indicates that the model's outputs consistently match human preference for response quality and helpfulness, not just accuracy on structured tasks. Deep-search capability previously absent from small general models: On xBench-DeepSearch-2505, Nanbeige4.1-3B scores 75—matching search-specialized small agents. The model can sustain complex agentic tasks involving more than 500 sequential tool invocations, a capability gap that previously required either specialized search agents or significantly larger models. Native tool-use support: The model's chat template and generation pipeline natively support tool call formatting, making it straightforward to connect to external APIs and build multi-step agentic workflows without additional scaffolding. Try it Use Case Prompt Pattern Code review and fix Provide failing test + stack trace; ask model to diagnose root cause and write corrected implementation Competition-style math Submit problem as structured prompt; use temperature 0.6, top-p 0.95 for consistent reasoning steps Agentic task execution Provide tool definitions as JSON + goal; let model plan and execute tool calls sequentially Long-document Q&A Pass full document (up to 131K tokens) with targeted factual questions; extract structured answers Sample prompt for a software engineering deployment: You are automating pull request review for a backend engineering team. Using the Nanbeige4.1-3B endpoint deployed in Microsoft Foundry, provide the model with a unified diff of a proposed code change and the following system instruction: "You are a senior software engineer reviewing a pull request. For each modified function: (1) summarize what the change does, (2) identify any edge cases that are not handled, (3) flag any security or performance regressions relative to the original, and (4) suggest a specific improvement if one is warranted. Format your output as a structured list per function." Octen's Octen-Embedding-0.6B Model Specs Parameters / size: 0.6B Context length: 32,768 tokens Primary task: Text embeddings (semantic search, retrieval, similarity) Why it's interesting Retrieval performance above larger proprietary models at 0.6B: On the RTEB (Retrieval Text Embedding Benchmark) public leaderboard, Octen-Embedding-0.6B achieves a mean task score of 0.7241—above voyage-3.5 (0.7139), Cohere-embed-v4.0 (0.6534), and text-embedding-3-large (0.6110), despite being a fraction of their parameter count. The model is fine-tuned from Qwen3-Embedding-0.6B via Low-Rank Adaptation (LoRA), demonstrating that targeted fine-tuning on retrieval-specific data can close the gap with larger embedding models. Vertical domain coverage across legal, finance, healthcare, and code: Octen-Embedding-0.6B was trained with explicit coverage of domain-specific retrieval scenarios—legal document matching, financial report Q&A, clinical dialogue retrieval, and code search including SQL. This makes it suitable for regulated-industry applications where generic embedding models tend to underperform on specialized terminology. 32,768-token context for long-document retrieval: The extended context window supports encoding entire legal contracts, earnings reports, or clinical case notes as single embeddings—removing the need to chunk long documents and re-aggregate scores at query time, which can introduce ranking errors. 100+ language support with cross-lingual retrieval: The model handles multilingual and cross-lingual retrieval natively, with strong coverage across languages including English, Chinese, and other major languages via its Qwen3-based architecture—practical for global enterprise applications that span multiple languages. Use Case Prompt Pattern Semantic search Encode user query and document corpus; rank documents by cosine similarity to query embedding Legal precedent retrieval Embed case briefs and query with legal question; retrieve most semantically relevant precedents Cross-lingual document search Encode multilingual document set; submit query in any supported language for cross-lingual retrieval Financial Q&A pipeline Embed earnings reports or filings; retrieve relevant passages to ground downstream language model responses Sample prompt for a global enterprise knowledge base deployment: You are building a clinical decision support tool. Using the Octen-Embedding-0.6B endpoint deployed in Microsoft Foundry, embed a corpus of 10,000 clinical case notes at ingestion time and store the resulting 1024-dimensional vectors in a vector database. At query time, encode an incoming patient presentation summary and retrieve the 5 most semantically similar historical cases. Pass the retrieved cases and the current presentation to a language model with the following instruction: "Based on these five similar cases and their documented outcomes, summarize the most common treatment approaches and flag any cases where the outcome differed significantly from the initial prognosis." 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 Foundry
