
A decision model for your agent harness.
An agent makes hundreds of small decisions during a task. We built Mouse-1 to handle these decisions, saving you time and money. Its 163x cheaper, and 65x faster than GPT-6 Sol. It works by adding the model directly into your agent harness alongside your current stack. Mouse-1 takes the agent's state and options and returns a probability. This lets the harness handle all the small decisions while the main frontier model takes care of the reasoning or code.
Mouse-1 is a decision model designed to optimize agent performance by managing numerous small decisions, resulting in significant cost and speed advantages over GPT-6 Sol. It integrates directly into existing agent harnesses, providing probability outputs based on the agent's state and options.
A source that found nothing is a measurement. A source that has not run is a gap. Neither means the launch lacks the thing.