Mamba-3 operates on a fundamentally different architecture than most models — it uses selective state space models (SSMs) instead of transformers, which means it handles long sequences without the quadratic memory cost that slows transformer-based peers. It tends to be efficient and fast, particularly on tasks involving long documents or streaming inputs. The trade-off is that it may lag behind transformer models on tasks requiring complex reasoning or nuanced instruction following.