Mai Image 2.6

MAI-Image-2.6 Flash is Microsoft’s low-latency version of the latest image model MAI-Image-2.6. It supports image generation in multiple aspect ratios and image-guided editing.

MAI-Thinking-1 is Microsoft’s first inference model in the MAI series, built for enterprise-scale workloads. With excellent reasoning, mathematical, and general intelligence capabilities, combined with superior cost-effectiveness, it makes high-throughput, 24/7 AI workloads economically viable.

MAI-Image-2.5 is Microsoft's flagship AI image generation and editing model. With top-tier realism, precise text rendering, and powerful image editing capabilities, it debuted among the top three in AI image generation rankings. It is primarily targeted at commercial design, product photography, and professional creative work.

MAI-Image-2.5 is Microsoft's flagship AI image generation and editing model. With top-tier realism, precise text rendering, and powerful image editing capabilities, it debuted among the top three in AI image generation rankings. It is primarily targeted at commercial design, product photography, and professional creative work.

MAI-Image-2.5 is Microsoft's flagship AI image generation and editing model. With top-tier realism, precise text rendering, and powerful image editing capabilities, it debuted among the top three in AI image generation rankings. It is primarily targeted at commercial design, product photography, and professional creative work.

Phi-4-mini-reasoning is a lightweight open model designed for advanced mathematical reasoning and logic-intensive problem-solving. It is particularly well-suited for tasks such as formal proofs, symbolic computation, and solving multi-step word problems. With its efficient architecture, the model balances high-quality reasoning performance with cost-effective deployment, making it ideal for educational applications, embedded tutoring, and lightweight edge or mobile systems. Phi-4-mini-reasoning supports a 128K token context length, enabling it to process and reason over long mathematical problems and proofs. Built on synthetic and high-quality math datasets, the model leverages advanced fine-tuning techniques such as supervised fine-tuning and preference modeling to enhance reasoning capabilities. Its training incorporates safety and alignment protocols, ensuring robust and reliable performance across supported use cases.