Mercury Coder is a coding-focused model built on diffusion-based architecture rather than the autoregressive approach used by most language models. This gives it a distinctly different generation pattern — it refines outputs in parallel rather than token-by-token, which translates to notably fast inference speeds. The trade-off is that it operates in a less familiar paradigm, and its strengths are concentrated specifically on code tasks.