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.
Hey i'm Pete, I'm the guy that built Mouse.
Adding a model like Mouse-1 to your agent harness will significantly improve performance, and cost savings.
In our evals adding Mouse to an agent harness was made the agent ~190× cheaper and 13× faster compared to Opus 5.5 with the same exact output on testing.
I optimized this model specifically for agent harnesses to perform fast decisions, classification, routing and more.
Mouse-1 is more accurate and cheaper than Jev, and can be tuned and calibrated specifically for your agent harness, so it gets better the more you use it right from a single API endpoint.
We pass through LoRa fine tuning from the h100 provider with zero mark up.
The API is Zero Data Retention for all users by default.
It's live and available to try today.
We went down this road for our own chat. a small model making the routing call before the big one does the work and what we learned is that the small call's speed matters more than its price. If it adds a couple of seconds before the first word, people feel it.
Here's a side by side of an agent harness with Mouse, and without vs GPT-6 Sol
set to the sweet sounds of Wu-Tang and Aesop Bach.
Mouse-1 launched on Product Hunt on October 21st, 2026 and earned 98 upvotes and 4 comments, placing #11 on the daily leaderboard. 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 was featured in API (98.8k followers), Developer Tools (521.5k followers) and Artificial Intelligence (481.6k followers) on Product Hunt. Together, these topics include over 232.6k products, making this a competitive space to launch in.
Who hunted Mouse-1?
Mouse-1 was hunted by Pete McGrath. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
Want to see how Mouse-1 stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Pete McGrath