MiniMax: AI Company Developing Multimodal Foundation Models for AGI
By
Zac Zuo
Slightly gummy. The crust never quite set.
Summary
MiniMax is an AI technology company founded in 2022 with the mission to 'co-create intelligence with everyone' and advance toward Artificial General Intelligence (AGI). The company has developed multimodal foundation models capable of processing text, audio, image, video, and music, featuring powerful code and Agent capabilities with ultra-long context processing. The article specifically mentions MMX-CLI, MiniMax's official command-line interface for AI agents and terminals that exposes various AI capabilities.
Key quotes
· 3 pulledMiniMax is driven by the mission to 'co-create intelligence with everyone,' dedicated to advancing the frontiers of AI and achieving Artificial General Intelligence (AGI).
MiniMax has independently developed a series of multimodal foundation models with powerful code and Agent capabilities, as well as ultra-long context processing.
MMX-CLI is MiniMax's official CLI for AI agents and terminals. It exposes text, image, video, speech, music, vision, and s
You might also wanna read
MiniMax Releases M2.1 AI Model with Enhanced Multi-Language Programming Capabilities
MiniMax has released M2.1, a significant upgrade to their AI model that focuses on enhanced multi-language programming capabilities. Unlike
MiniMax Launches M2.5 AI Model with Enhanced Performance in Coding and Real-World Tasks
MiniMax introduces its latest AI model, M2.5, which has been extensively trained with reinforcement learning in complex real-world environme
MiniMax M2.7 Is Now Open Source
Introducing MiniMax-M1: The World's First Open-Weight Hybrid-Attention Reasoning Model
Introducing MiniMax-M1, the world's first open-weight, large-scale hybrid-attention reasoning model powered by a hybrid Mixture-of-Experts a
Hands-on evaluation of MiniMax M2.7 via API on ML and coding workflows
The author evaluates MiniMax M2.7 by using it through Claude Code on three real-world ML and coding workflows: scaffolding a Kaggle competit
