MiniMax M2.5
The first open model to beat Sonnet made for productivity
What it does
Introducing M2.5, an open-source frontier model designed for real-world productivity. SOTA performance at coding (SWE-Bench Verified 80.2%), search (BrowseComp 76.3%), agentic tool-calling (BFCL 76.8%) & office work. Optimized for efficient execution, 37% faster at complex tasks. At $1 per hour with 100 tps, infinite scaling of long-horizon agents now economically possible.
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I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
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