by ScaledownReleased 13 October 2026
Classify-1 reads text, pictures or whole documents and answers questions you set: pick one option from several, yes or no, or rate on an ordered scale. It returns a probability for each answer rather than written text, which suits sorting requests, moderation, spotting what a customer wants and tagging by topic. It reads about 1,000 pages at once. To pull specific values out of a document use ScaleDown Extract, and to shorten it use ScaleDown Summarize.
You pay for what each request uses. No subscription, no minimum.
20 % offScaleDown has cut its price. Every figure here is the new one.
One case, the ordinary use
Every rate
| Meter | ↑ you send | ↓ comes back |
|---|---|---|
| Text tokens | 13,31 DA |
per 1M tokens
One company runs this model. OpenDunes sends every request to it. DA per 1M tokens: ↑ what you send.
| Provider | ↑ Sent | Latency | Uptime |
|---|---|---|---|
| ScaleDown20 % off | … | … |
You pay the price of the provider that served your request. Prices read 15:18 · speed over the last 30 minutes · uptime over 3 days.
One HTTP call, with your questions named as you like.
import os, requests
BASE = "https://opendunes.com"
KEY = os.environ["OPENDUNES_API_KEY"]
H = {"Authorization": f"Bearer {KEY}"}
resp = requests.post(f"{BASE}/alpha/decisions", headers=H, json={
"model": "scaledown/classify-1",
"state": "The courier came two days late and the box was open.",
"questions": {
"is_complaint": {
"type": "noul",
"instructions": "Is it a complaint?",
"criteria": {
"true": "The customer is unhappy with the service.",
"false": "The customer is asking a question or saying thanks.",
},
},
},
})
print(resp.json())Runs this request once on your balance.
About 0,013 DA per case. Was 0,016 DA. ↑ 13,31 DA per 1M tokens.
No. You pay per use from your balance, with no subscription. Run it once from Call it in code, at the same price.
Classify-1 reads text, pictures or whole documents and answers questions you set: pick one option from several, yes or no, or rate on an ordered scale. It returns a probability for each answer rather than written text, which suits sorting requests, moderation, spotting what a customer wants and tagging by topic. It reads about 1,000 pages at once. To pull specific values out of a document use ScaleDown Extract, and to shorten it use ScaleDown Summarize.
Deposit dinars with your Edahabia or CIB card. Every model, Classify-1 included, is paid from that balance. No foreign card, no conversion.
Scaledown names no languages for Classify-1. Arabic, French and Darja: not stated.
Yes or no, one choice from your list, or a score on your scale. Each answer comes with its probability.
Scaledown. Released 13 October 2026.
ScaleDown Summarize shortens long text and documents by rewriting them faithfully in new words, not by copying sentences out. It reads about 1,000 pages at once and writes back about 4 pages at most. Use it for reviewing documents, digesting transcripts, or trimming retrieved material before another model sees it, steering layout, focus, language and length with instructions.
ScaleDown Extract pulls the details you name out of text, pictures and PDFs. You list the fields you want in plain English, and it returns each match with a confidence score, its position in the document and the text around it. It suits contracts, invoices and emails, plus transcripts, and it reads scanned pages itself.
From what its maker and two benchmark sites publish, and where it stands on OpenDunes. Read 7 Nov 2026.
Reads a long document and answers each yes/no question you set with a probability
Takes an image and rates it on your ordered scale from no to yes
Sorts incoming text into the choice options you define and reports the odds