The video introduces the Xiaomi MiMo V2.5 Pro, a powerful 1 trillion parameter open-weight AI model released by Xiaomi, often referred to as the “Apple of China.” The presenter highlights the model’s impressive capabilities, especially in coding, mathematics, and logic, noting that it ranks among the top open models alongside others like Kim K 2.6 and GLM 5.1. Despite its strengths, the model is challenging to quantize due to its size and complexity, requiring distributed computing resources to run effectively. The presenter demonstrates running the model on a Mac Studio M3 Ultra with distributed compute, generating tens of thousands of tokens, though sometimes the model overthinks and struggles to complete complex tasks like creating a high-fidelity interactive webpage.
One of the standout features of the MiMo V2.5 Pro is its MIT license, which allows users full freedom to use and modify the model without restrictive conditions, unlike some other models that require attribution or have commercial use limitations. The presenter also showcases the model’s ability to run games like Snake and 3D Tetris, noting that enabling “thinking mode” significantly improves output quality. However, the model sometimes fails on more complex prompts, such as advanced 3D Tetris or intricate coding tasks, indicating that while it is powerful, it still has limitations in handling highly complex or multi-modal tasks.
The standard MiMo V2.5 (non-Pro) edition is highlighted as an omni-modal model capable of processing audio and vision inputs, although the presenter has only tested its language capabilities locally. This version performs well on benchmarks and can handle logical and mathematical problems efficiently, producing fast token generation rates and requiring less memory than the Pro version. The presenter demonstrates the model solving a classic logic puzzle about dividing oranges and running a 3D Tetris game, emphasizing the model’s versatility and speed, especially when thinking mode is enabled.
In terms of mathematical problem-solving, the presenter compares the Pro and non-Pro versions on an International Math Olympiad question. The non-Pro version struggles and eventually fails to provide an answer, while the Pro version arrives at the correct solution much faster, though it tends to overthink and generate excessive tokens by double- and triple-checking its answers. This behavior suggests that while the Pro model is more capable, it still requires optimization to improve efficiency and stop conditions during reasoning tasks.
Overall, the Xiaomi MiMo V2.5 series represents a significant advancement in open-weight AI models, combining high parameter counts, multi-modal capabilities, and an open MIT license that encourages community use and development. The presenter expresses enthusiasm for the model’s potential and encourages community efforts to develop distributed computing setups to handle the demanding resource requirements of the Pro version. Despite some current limitations in inference and task completion, the MiMo V2.5 models show promise in coding, logic, and multi-modal AI applications, making them exciting tools for developers and researchers alike.
