Mouse-1
A lightweight decision model designed for AI agent frameworks that separates decision-making from reasoning. It takes agent state and available options as input and outputs a probability distribution, enabling fast action selection.
Timeline (last 90 days)
Mouse-1 is a lightweight decision model designed specifically for Agent frameworks, developed by Pete McGrath.
Mouse-1 is reportedly 163 times cheaper and 65 times faster than GPT-6 Sol.
Mouse-1 received 86 upvotes on Product Hunt, ranking 7th for the day.
Mouse-1's core idea is to separate decision-making from reasoning: it does not handle complex logical reasoning or code generation, but instead quickly returns probability values based on the Agent's state and the available options.
Mouse-1 is offered via API access and is categorized as a developer tool and artificial intelligence.
Mouse-1 outputs a probability distribution rather than natural language text, and its output layer is constrained to the predefined action space of Agent frameworks.
Mouse-1's "separation of decision and reasoning" belongs to the same engineering approach of "using small models for high-frequency subtasks" as mixture-of-experts (MoE) routing and speculative decoding.
For Agents where decisions account for more than 80% of a task, moving decisions to a dedicated small model can bring order-of-magnitude cost reductions; but if the task is mainly long-text generation or complex code reasoning, the savings ratio narrows significantly.
Mouse-1 recommends starting with decisions on non-critical paths as a pilot and gradually validating the results.
Mouse-1 was developed by Pete McGrath and released on Product Hunt, and currently has only 1 comment.
All Facts (10)
Mouse-1 is a lightweight decision model designed specifically for Agent frameworks, developed by Pete McGrath.
50%UnverifiedMouse-1 is reportedly 163 times cheaper and 65 times faster than GPT-6 Sol.
50%UnverifiedFor Agents where decisions account for more than 80% of a task, moving decisions to a dedicated small model can bring order-of-magnitude cost reductions; but if the task is mainly long-text generation or complex code reasoning, the savings ratio narrows significantly.
50%UnverifiedMouse-1 recommends starting with decisions on non-critical paths as a pilot and gradually validating the results.
50%UnverifiedMouse-1's core idea is to separate decision-making from reasoning: it does not handle complex logical reasoning or code generation, but instead quickly returns probability values based on the Agent's state and the available options.
50%UnverifiedMouse-1 is offered via API access and is categorized as a developer tool and artificial intelligence.
50%UnverifiedMouse-1 outputs a probability distribution rather than natural language text, and its output layer is constrained to the predefined action space of Agent frameworks.
50%UnverifiedMouse-1's "separation of decision and reasoning" belongs to the same engineering approach of "using small models for high-frequency subtasks" as mixture-of-experts (MoE) routing and speculative decoding.
50%UnverifiedMouse-1 received 86 upvotes on Product Hunt, ranking 7th for the day.
50%UnverifiedMouse-1 was developed by Pete McGrath and released on Product Hunt, and currently has only 1 comment.
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