MiniMax-M1

Why we included this project

MiniMax-M1 is an open-weight reasoning model built around a hybrid-attention design: it pairs standard attention with a linear "lightning" attention mechanism, and the accompanying paper shows this lets it scale test-time compute efficiently, which matters when you're paying for long chain-of-thought inference at volume. Because the weights are out in the open, you can self-host it, run it through your own serving stack, or fine-tune it toward a domain instead of being locked into a single closed API. That makes it a genuine alternative for teams weighing open reasoning models against commercial ones. The repo is the straightforward landing point for the release, with the architecture, benchmark results, and the deployment notes a small team would need to get started.