# Mouse-1

> Source: <https://mouse.dev/blog/mouse-decision-model/>
> Published: 2026-10-16 21:08:38+00:00

Today we’re introducing `mouse-1`, a general purpose decision model specifically built for AI agent harnesses.

It integrates into your existing harness through a single API and returns decisions in about 100 milliseconds of model time. It’s designed to help agents complete more work independently, with fewer errors and unnecessary interruptions.

In our tests, Mouse-1 was 163× faster than GPT-6 Sol, returning a decision before Sol produced its first token.

GPT-6 Sol

We evaluated `mouse-1` on unfamiliar tools and repositories to measure how well it generalizes beyond its training data. It achieved 89.1% average decision accuracy, compared with 85.8% for d3, 84.2% for Clef, 82.5% for OpenAI Decisions, 81.4% for d3-flash, and 79.1% for Jev.

Unfamiliar tools and repositories (%) · Higher is better

| Average decision accuracy, Unfamiliar tools and repositories (%) |  | 
|---|---|
| Mouse-1 | 89.1% | 
|---|---|
| d3 | 85.8% | 
| Clef | 84.2% | 
| OpenAI | 82.5% | 
| d3-flash | 81.4% | 
| Jev | 79.1% | 

Average of BFCL, API-Bank, When2Call and PhishNChips (%) — distinct from the 37-task HF Decision Index · Higher is better

| Agent Tool & Security Suite, Average of BFCL, API-Bank, When2Call and PhishNChips (%) — distinct from the 37-task HF Decision Index |  | 
|---|---|
| d3 | 86.0% | 
|---|---|
| Clef | 85.6% | 
| Mouse-1 | 84.9% | 
| Jev | 79.8% | 

Tool selection (%) · Higher is better

| BFCL, Tool selection (%) |  | 
|---|---|
| Clef | 98.5% | 
|---|---|
| Mouse-1 | 97.6% | 
| d3 | 97.3% | 
| Jev | 96.0% | 

Tool selection (%) · Higher is better

| API-Bank, Tool selection (%) |  | 
|---|---|
| Clef | 91.9% | 
|---|---|
| d3 | 86.4% | 
| Mouse-1 | 83.7% | 
| Jev | 82.9% | 

When to call a tool (%) · Higher is better

| When2Call, When to call a tool (%) |  | 
|---|---|
| d3 | 86.4% | 
|---|---|
| Mouse-1 | 85.8% | 
| Jev | 78.5% | 
| Clef | 72.5% | 

Phishing decisions (%) · Higher is better

| PhishNChips, Phishing decisions (%) |  | 
|---|---|
| Clef | 79.4% | 
|---|---|
| d3 | 73.9% | 
| Mouse-1 | 72.3% | 
| Jev | 61.9% | 

## Prompt Injection

We hardened `mouse-1`'s resistance to prompt injection, which was a common early complaint from the initial Jev model results. We tested this in an evaluation using planted instructions, which resulted in 0 change of `mouse-1` decisions, meaning it performs significantly better at squashing these types of attacks.

Decisions changed by a planted instruction (%) · Lower is better

| Decision flip rate, Decisions changed by a planted instruction (%) |  | 
|---|---|
| Mouse-1 | 0% | 
|---|---|
| d3-flash | 27.6% | 
| Clef | 37.4% | 
| d3 | 40.4% | 
| OpenAI | 53.9% | 
| Jev | 65.4% | 

## Images

`mouse-1` reads images too. Attach them to the same request and every question sees them, with the same probabilities and the same price per token. These are real answers from the production model on the images shown, one `choice` question each.

Image input is in beta: we have checked it on cases like these, not on a published image benchmark yet. [Read the guide](https://mouse.dev/docs/guides/images).

## Built for improving agent harness cost and speed.

Agents make many decisions as they operate. These decisions compound over time and are becoming more important as agents now work for hours and even days at a time.

We built `mouse-1` to support these choices through a common interface. The harness supplies the task context and candidate options and our model returns a probability for each option. Developers can decide how those scores affect the execution and even delegate to which decisions require human review.

| Cost per 1,000 decisions across models |  | 
|---|---|
| TypeSafe Jev | $0.042 ($0.042 / 1M in · $0 out) | 
|---|---|
| Mouse-1 | $0.10 ($0.10 / 1M in · $0 out) | 
| OpenAI Decisions | $0.10 ($0.10 / 1M in · $0 out) | 
| Cloudflare Clef | $0.24 ($0.24 / 1M in · $0 out) | 
| Claude Haiku 4.5 | $1.25 ($1 / 1M in · $5 / 1M out) | 
| DeepSeek V4 | $1.52 ($1.32 / 1M in · $3.96 / 1M out) | 
| Gemini 3.7 Flash | $1.88 ($1.50 / 1M in · $7.50 / 1M out) | 
| Claude Sonnet 5 | $3.75 ($3 / 1M in · $15 / 1M out) | 
| Claude Opus 5 | $6.25 ($5 / 1M in · $25 / 1M out) | 
| GPT-6 Sol | $6.50 ($5 / 1M in · $30 / 1M out) | 

1,000 input tokens per decision. Generative models add 50 output tokens.

We’ve found that adding `mouse-1` to our own coding agent harness significantly improves its latency.

The model is available as an API at $0.10 per 1M input tokens with free output. [Get a key](https://mouse.dev/api) and send a state plus the options you want scored. $5 in credits, no card.