Best AI Models for Microsoft Azure
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Compare the Top AI Models that integrate with Microsoft Azure as of October 2025
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This a list of AI Models that integrate with Microsoft Azure. Use the filters on the left to add additional filters for products that have integrations with Microsoft Azure. View the products that work with Microsoft Azure in the table below.
What are AI Models for Microsoft Azure?
AI models are systems designed to simulate human intelligence by learning from data and solving complex tasks. They include specialized types like Large Language Models (LLMs) for text generation, image models for visual recognition and editing, and video models for processing and analyzing dynamic content. These models power applications such as chatbots, facial recognition, video summarization, and personalized recommendations. Their capabilities rely on advanced algorithms, extensive training datasets, and robust computational resources. AI models are transforming industries by automating processes, enhancing decision-making, and enabling creative innovations. Compare and read user reviews of the best AI Models for Microsoft Azure currently available using the table below. This list is updated regularly.
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1
Azure OpenAI Service
Microsoft
Apply advanced coding and language models to a variety of use cases. Leverage large-scale, generative AI models with deep understandings of language and code to enable new reasoning and comprehension capabilities for building cutting-edge applications. Apply these coding and language models to a variety of use cases, such as writing assistance, code generation, and reasoning over data. Detect and mitigate harmful use with built-in responsible AI and access enterprise-grade Azure security. Gain access to generative models that have been pretrained with trillions of words. Apply them to new scenarios including language, code, reasoning, inferencing, and comprehension. Customize generative models with labeled data for your specific scenario using a simple REST API. Fine-tune your model's hyperparameters to increase accuracy of outputs. Use the few-shot learning capability to provide the API with examples and achieve more relevant results.Starting Price: $0.0004 per 1000 tokens -
2
Medical LLM
John Snow Labs
John Snow Labs' Medical LLM is an advanced, domain-specific large language model (LLM) designed to revolutionize the way healthcare organizations harness the power of artificial intelligence. This innovative platform is tailored specifically for the healthcare industry, combining cutting-edge natural language processing (NLP) capabilities with a deep understanding of medical terminology, clinical workflows, and regulatory requirements. The result is a powerful tool that enables healthcare providers, researchers, and administrators to unlock new insights, improve patient outcomes, and drive operational efficiency. At the heart of the Healthcare LLM is its comprehensive training on vast amounts of healthcare data, including clinical notes, research papers, and regulatory documents. This specialized training allows the model to accurately interpret and generate medical text, making it an invaluable asset for tasks such as clinical documentation, automated coding, and medical research. -
3
Phi-2
Microsoft
We are now releasing Phi-2, a 2.7 billion-parameter language model that demonstrates outstanding reasoning and language understanding capabilities, showcasing state-of-the-art performance among base language models with less than 13 billion parameters. On complex benchmarks Phi-2 matches or outperforms models up to 25x larger, thanks to new innovations in model scaling and training data curation. With its compact size, Phi-2 is an ideal playground for researchers, including for exploration around mechanistic interpretability, safety improvements, or fine-tuning experimentation on a variety of tasks. We have made Phi-2 available in the Azure AI Studio model catalog to foster research and development on language models. -
4
Hyperplane
Hyperplane
Better audiences from the richness of transaction data. Create nuanced personas and effective marketing campaigns based on financial behaviors and consumer interests. Increase user limits, without worrying about default. Leverage user income estimates that are precise and always up-to-date. The Hyperplane platform enables financial institutions to launch personalized consumer experiences through specialized foundation models (LLMs). Upgrade your feature sets with embeddings for credit, collections, and lookalike modeling. Segment users based on various criteria, enabling you to target specific audience groups for personalized marketing campaigns, content delivery, and user analysis. Segmentation is achieved through facets, which are key attributes or characteristics used to categorize users, Hyperplane offers the capability to enrich user segmentation by employing additional attributes to fine-tune the filtering of responses from certain audience segmentation endpoints. -
5
Magma
Microsoft
Magma is a cutting-edge multimodal foundation model developed by Microsoft, designed to understand and act in both digital and physical environments. The model excels at interpreting visual and textual inputs, allowing it to perform tasks such as interacting with user interfaces or manipulating real-world objects. Magma builds on the foundation models paradigm by leveraging diverse datasets to improve its ability to generalize to new tasks and environments. It represents a significant leap toward developing AI agents capable of handling a broad range of general-purpose tasks, bridging the gap between digital and physical actions. -
6
Phi-4-reasoning
Microsoft
Phi-4-reasoning is a 14-billion parameter transformer-based language model optimized for complex reasoning tasks, including math, coding, algorithmic problem solving, and planning. Trained via supervised fine-tuning of Phi-4 on carefully curated "teachable" prompts and reasoning demonstrations generated using o3-mini, it generates detailed reasoning chains that effectively leverage inference-time compute. Phi-4-reasoning incorporates outcome-based reinforcement learning to produce longer reasoning traces. It outperforms significantly larger open-weight models such as DeepSeek-R1-Distill-Llama-70B and approaches the performance levels of the full DeepSeek-R1 model across a wide range of reasoning tasks. Phi-4-reasoning is designed for environments with constrained computing or latency. Fine-tuned with synthetic data generated by DeepSeek-R1, it provides high-quality, step-by-step problem solving. -
7
Phi-4-reasoning-plus
Microsoft
Phi-4-reasoning-plus is a 14-billion parameter open-weight reasoning model that builds upon Phi-4-reasoning capabilities. It is further trained with reinforcement learning to utilize more inference-time compute, using 1.5x more tokens than Phi-4-reasoning, to deliver higher accuracy. Despite its significantly smaller size, Phi-4-reasoning-plus achieves better performance than OpenAI o1-mini and DeepSeek-R1 at most benchmarks, including mathematical reasoning and Ph.D. level science questions. It surpasses the full DeepSeek-R1 model (with 671 billion parameters) on the AIME 2025 test, the 2025 qualifier for the USA Math Olympiad. Phi-4-reasoning-plus is available on Azure AI Foundry and HuggingFace. -
8
Phi-4-mini-reasoning
Microsoft
Phi-4-mini-reasoning is a 3.8-billion parameter transformer-based language model optimized for mathematical reasoning and step-by-step problem solving in environments with constrained computing or latency. Fine-tuned with synthetic data generated by the DeepSeek-R1 model, it balances efficiency with advanced reasoning ability. Trained on over one million diverse math problems spanning multiple levels of difficulty from middle school to Ph.D. level, Phi-4-mini-reasoning outperforms its base model on long sentence generation across various evaluations and surpasses larger models like OpenThinker-7B, Llama-3.2-3B-instruct, and DeepSeek-R1. It features a 128K-token context window and supports function calling, enabling integration with external tools and APIs. Phi-4-mini-reasoning can be quantized using Microsoft Olive or Apple MLX Framework for deployment on edge devices such as IoT, laptops, and mobile devices. -
9
Tune AI
NimbleBox
Leverage the power of custom models to build your competitive advantage. With our enterprise Gen AI stack, go beyond your imagination and offload manual tasks to powerful assistants instantly – the sky is the limit. For enterprises where data security is paramount, fine-tune and deploy generative AI models on your own cloud, securely.
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