Mage (2 minute read)

TLDR AI 07/29/26, 12:00 AM Models

Summary

Microsoft unveils Mage, a family of compact 4B-parameter multimodal models for visual understanding and generation, designed for easy training and deployment on modest hardware while staying competitive with larger open models.

Mage is a lightweight, research-friendly multimodal model family. It was built with a fixed 4B-parameter budget. The models are compact enough to train, fine-tune, and deploy onto modest hardware while remaining competitive with much larger open systems. Mage-VL is an efficient codec-native streaming multimodal foundation model, and Mage-Flow is an efficient native-resolution foundation model for image generation and editing.

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# Mage — A Lightweight, Research-Friendly Multimodal Model Family Source: [https://microsoft.github.io/Mage/](https://microsoft.github.io/Mage/) ![Mage family](https://microsoft.github.io/Mage/static/imgs/mage-model-cover.png) **Mage**is a family of lightweight, research\-friendly multimodal models built at a fixed**4B\-parameter**budget, sharing a*codec\-aligned efficiency*philosophy —*spend representation capacity where the signal is*— across both visual**understanding**and**generation**\. Both models are compact enough to train, fine\-tune, and deploy on modest hardware, yet remain competitive with much larger open systems\.

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