You can think of Mercury 2 as roughly in the same intelligence tier as other speed-optimized models (e.g., Haiku 4.5, Grok Fast, GPT-Mini–class systems). The main differentiator is latency — it’s ~5× faster at comparable quality.

We’re not positioning it as competing with the largest models (Opus 4.5, etc.) on hardest-case reasoning. It’s more of a “fast agent” model (like Composer in Cursor, or Haiku 4.5 in some IDEs): strong on common coding and tool-use tasks, and providing very quick iteration loops.

If latency is the differentiator, would you be chasing the edge compute marketplace, e.g. mobile edge compute AI agents?

Are you dogfooding it on simple tasks? If so what do you use it for regularly and what do you avoid?

Is the approach fundamentally limited to smaller models? Or could you theoretically train a model as powerful as the largest models, but much faster?