Mai Image 2Pricing
Input Modalities
Output Modalities
- Image
Frequently asked questions
More models from Microsoft
See all Microsoft models →- Input: $ 5 /M
- Output: $ 5 /M
- Input Image: $1.6/M tokens
MAI-Image-2.6 is Microsoft's latest image-generation and editing model. It can generate images from text prompts and supports image-guided editing in multiple aspect ratios.
- Input: $ 1.75 /M
- Output: $ 1.75 /M
- Input Image: $1.4286/M tokens
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-DS-R1 is a refined version of DeepSeek-R1 by Microsoft AI, designed to improve responsiveness to previously blocked topics while enhancing safety. It integrates 110k Tulu-3 SFT samples and 350k multilingual safety-alignment examples. The model retains strong reasoning and coding abilities, surpasses R1-1776 in handling sensitive queries, and reduces harmful content leakage. Based on a transformer MoE architecture, it suits general-purpose tasks—excluding legal, medical, or autonomous systems.
Phi-4-Reasoning is a state-of-the-art open-weight reasoning model finetuned from Phi-4 using supervised fine-tuning on a dataset of chain-of-thought traces and reinforcement learning. The supervised fine-tuning dataset includes a blend of synthetic prompts and high-quality filtered data from public domain websites, focused on math, science, and coding skills as well as alignment data for safety and Responsible AI. The goal of this approach was to ensure that small capable models were trained with data focused on high quality and advanced reasoning.
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.
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