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MiMo 2.5 Pro Coding Test with OpenCode | Better Agentic Coder Than GLM 5.1 & Kimi K2.6? | 🔴 Live

Venelin Valkov16 June 2026Watch on YouTube

Description

MiMo 2.5 Pro is 1T Mixture of Experts (MoE) model by Xiaomi and it is topping leaderboards of open coding models. How good it is in real-world coding projects? Let's try it out with OpenCode and OpenRouter. Blog: https://mimo.xiaomi.com/mimo-v2-5-pro/ AI Academy: https://mlexpert.io/ Work with me: https://mlexpert.io/consulting LinkedIn: https://www.linkedin.com/in/venelin-valkov/ Follow me on X: https://twitter.com/venelin_valkov Discord: https://discord.gg/UaNPxVD6tv Subscribe: http://bit.ly/venelin-subscribe GitHub repository: https://github.com/curiousily/AI-Bootcamp 👍 Don't Forget to Like, Comment, and Subscribe for More Tutorials! Join this channel to get access to the perks and support my work: https://www.youtube.com/channel/UCoW_WzQNJVAjxo4osNAxd_g/join

What you'll learn

  • MiMo 2.5 Pro is a 1T Mixture of Experts model by Xiaomi that tops leaderboards for open coding models.
  • The model is tested on real-world coding projects using OpenCode and OpenRouter to evaluate practical performance.
  • The video demonstrates how MiMo 2.5 Pro performs for agentic coding tasks compared to other state-of-the-art models like GLM 5.1 and Kimi K2.6.

Frequently asked questions

What is MiMo 2.5 Pro and who developed it?
MiMo 2.5 Pro is a 1T Mixture of Experts model developed by Xiaomi that currently ranks highly on leaderboards for open coding models.
How is MiMo 2.5 Pro tested in this video?
The model is tested live on real-world coding projects using OpenCode and OpenRouter to evaluate its practical performance for agentic coding tasks.
Which other models is MiMo 2.5 Pro compared against?
In this video, MiMo 2.5 Pro is compared with other state-of-the-art models, including GLM 5.1 and Kimi K2.6.
What is a Mixture of Experts architecture?
The video demonstrates that MiMo 2.5 Pro uses a Mixture of Experts (MoE) architecture with 1T parameters, which is a scalable approach for training large language models.

Topics

llm-modelscoding-agentsmoe-architecturemodel-evaluation