Now available: GPT-6 Luna Decisions
Try the modelSCALEDOWN / OPENROUTER
ScaleDown’s structured decision model scores shared state against caller-defined Choice, Score and Noul questions. Use its typed answers and probabilities for routing, moderation, intent detection and domain tagging.
scaledown/classify-1New accounts get 100 credits. The playground and API share one balance.
Context window
1,000,000
tokens · provider-listed maximum
OpenRouter input price
$0.04 / 1M
Per million input tokens · credits use the $0.05 list price
OpenRouter output price
$0 / 1M
output tokens are free upstream
This site's input rate
720
credits per million input tokens · minimum 1 per successful request
JEV / SYSTEM ONE
Send model, state and keyed Choice, Score or Noul questions to /v1/systemone. Text state is forwarded directly; JSON objects and arrays are serialized to text so every field reaches ScaleDown. Answers and token usage use the shared decision response format.
SPAN / NOUL
Span accepts only Noul behavior questions and requires text or a specific input/output conversation object. This model also accepts Choice and Score questions, with text, generic JSON objects, or arrays as state. Use the Jev playground for this broader decision contract.
Choice returns a named option and its probability distribution. Score returns a value on an ordered scale with probabilities. Noul returns the probability of a yes/no condition. Multiple questions share the same state.
Provider context: 1,000,000 tokens. This service accepts text and JSON state, up to 8 questions, 2–255 Choice options and 2–10 ordered Score levels. Text and questions must fit within 32 KiB. Image and PDF input are not enabled in this integration.
Calls OpenRouter’s Decisions endpoint. OpenRouter lists image and file modalities, but ScaleDown requires a provider-specific document state; the shared image content-parts format is rejected. This integration currently exposes text and serialized JSON. Usage is summed across the questions.
OpenRouter’s undiscounted input list price is $0.05 per million tokens; the current 20% promotion lowers upstream input to $0.04. This service sets its rate from the $0.05 list price: 720 credits ($0.072) per million actual input tokens, including all questions. Successful calls round up to at least 1 credit ($0.0001). Output tokens are free; failed calls release the reservation. The promotion does not change this credit rate.
max(1, ceil(input_tokens × 720 / 1,000,000))
A successful request with 1,000 input tokens costs 1 credit; 5,000 input tokens cost 4 credits. Output tokens add no charge. Failed calls release the credit reservation.
Checked October 14, 2026 against OpenRouter and ScaleDown documentation. Live requests returned Choice, Score and Noul answers. Generic JSON and image content parts were rejected upstream; JSON is serialized by this integration. These checks establish API compatibility, not model accuracy.
The Jev workbench below starts with this model selected. Try a recipe, edit the state and questions, or export API code. This page stores its browser draft separately from other model pages and the main playground.
What do you want to decide?
Choose a task to load its input and rules. Loading examples uses no credits.
We reserve this budget before the model runs, then charge actual usage and return the difference. Failed calls are refunded.
New account? Receive 100 credits once. Short requests typically use 1 credit; longer inputs can use more.
Draft saved in this browser · Your draft will be restored after sign-in.
Your current configuration stays in this browser tab for the setup flow.
ILLUSTRATIVE EXAMPLE · NO CREDITS USED
“I was charged twice this morning. Please refund the duplicate payment today.”
Choice
Billing
Route to the billing queue
Yes / no
96%
Probability of an urgent request
Score
2.8 / 3
Urgency on a 0–3 scale
Static illustration of the output format. Values are not a live model response or a measure of accuracy.
Capture A, change the model or questions in the workbench, then capture B. Both versions use identical states.
Capture A from the current workbench model and rules. Change the model or questions, then capture B. Import up to 25 labeled cases and compare both versions. The dataset state replaces each captured state.
This simulates a policy, not accuracy. Noul uses max(P(yes), P(no)); Choice and Score use your selected signal. Validate thresholds on labeled cases.
Total budget across both versions and all cases. Each request reserves its own allowance, then settles actual usage and refunds the remainder. Stopping preserves charges for completed requests.
New account? Receive 100 credits once. Short requests typically use 1 credit; longer inputs can use more.
Each successful comparison request uses credits. Review the total budget before starting. Requests run sequentially; stopping keeps charges for completed requests. Export includes rules, cases, results and errors. Closing this page stops the comparison.
Send model, state, and questions to Decisions API's /v1/systemone endpoint with an API key created here. Set model to scaledown/classify-1; answers are in data.result.answers and token usage is in data.result.usage.
// Server-side JavaScript (Node 20+ or Bun). Set DECISIONS_API_KEY.
const response = await fetch("https://decisions-api.dev/v1/systemone", {
method: "POST",
headers: {
"Authorization": `Bearer ${process.env.DECISIONS_API_KEY}`,
"Content-Type": "application/json",
},
body: JSON.stringify({
"model": "scaledown/classify-1",
"state": "I was charged twice for order A-4471. Please refund the duplicate payment.",
"questions": {
"route": {
"type": "choice",
"instructions": "Which team should handle this ticket? Use other when no option fits.",
"criteria": {
"billing": "Payments, charges and refunds",
"technical": "Bugs and product errors",
"account": "Login and account access",
"other": "None of these teams"
}
}
}
}),
});
const body = await response.json();
if (!response.ok || body.code !== 0) throw new Error(body.message);
console.log(body.data.result.answers);
console.log(body.data.creditsUsed);