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Extract API: toman pricing and code

scaledown/extract

Image inputFile input
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Input · per 1M tokens
13,849 toman
Output · per 1M tokens
0 toman
One 1,000-word request ≈
18 toman

You are billed for the usage the request actually reported. Prices follow the market.Pricing and top-ups

Extract example: reading an image into JSON

The example is picked from this model's own capabilities. Drop in your key and it runs as is.

# tip: a data URI works too — base64 the file and prefix it with data:image/jpeg;base64,
curl https://api.uttapen.ir/v1/chat/completions \
  -H "Authorization: Bearer sk-up-…" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "scaledown/extract",
    "messages": [{
      "role": "user",
      "content": [
        {"type": "text", "text": "Return only the invoice number and the total, as JSON."},
        {"type": "image_url", "image_url": {"url": "https://example.com/factor.jpg"}}
      ]
    }]
  }'
import base64, json
from openai import OpenAI

client = OpenAI(base_url="https://api.uttapen.ir/v1", api_key="sk-up-…")

img = base64.b64encode(open("factor.jpg", "rb").read()).decode()

resp = client.chat.completions.create(
    model="scaledown/extract",
    messages=[{
        "role": "user",
        "content": [
            {"type": "text", "text": "Return the invoice number, the date and the total as JSON."},
            {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{img}"}},
        ],
    }],
    response_format={"type": "json_object"},
)
print(json.loads(resp.choices[0].message.content))
import OpenAI from "openai";
import { readFileSync } from "node:fs";

const client = new OpenAI({ baseURL: "https://api.uttapen.ir/v1", apiKey: "sk-up-…" });
const img = readFileSync("factor.jpg").toString("base64");

const resp = await client.chat.completions.create({
  model: "scaledown/extract",
  messages: [{
    role: "user",
    content: [
      { type: "text", text: "Return the invoice number, the date and the total as JSON." },
      { type: "image_url", image_url: { url: `data:image/jpeg;base64,${img}` } },
    ],
  }],
  response_format: { type: "json_object" },
});
console.log(JSON.parse(resp.choices[0].message.content));

What is Extract good for?

Scaledown publishes this model; we expose it under the id "scaledown/extract". Its context window is 1,000,000 tokens, roughly 750k English words in one request. A single response can run to 900,000 tokens. On price it sits in the "very cheap" band, cheaper than all 394 other paid models in the catalogue.

What it can do beyond plain text: it reads images directly, which makes it a real option for invoices, forms and screenshots; it takes files such as PDFs as input. All of it works through the standard parameters of the official OpenAI SDK — no custom client, no wrapper.

Pricing is 13,849 toman per 1M input tokens and 0 per 1M output tokens. A 1,000-word round trip on Extract lands near 18 toman. You are always charged for the usage the request actually reported, never for the estimate, and a failed request costs nothing.

The closest alternative with the same capabilities from a different provider is Lyria 3 Clip Preview, and its context window is smaller. Both run on the same key and the same code, so trying the other one is a single string change.

To make the figure concrete: 100,000 toman of credit buys roughly 5,556 thousand-word requests on Extract, and every 1,000 toman is about 55,556 words of round trip. A job with one million input tokens and one million output tokens comes to 13,849 toman in total. Filling this model's 1,000,000-token window costs 13,849 toman on the input side alone, which is the real reason to keep conversation history short.

Its nearest relative in the catalogue is "scaledown/summarize", and choosing between those two is where most people hesitate. Both land at nearly the same price on a thousand-word request (18 and 18 toman). Maximum answer length differs as well: 900,000 against 4,000 tokens.

Scaledown has 2 models in our catalogue; the cheapest is Extract at 18 toman per thousand words and the dearest Summarize at 18. It does not support include_reasoning, reasoning, response_format, temperature, which most models here do, so test before switching if your code relies on them. By context size the nearest option from another provider is Nova 2 Lite at 1,000,000 tokens.

Good fits: high-volume work such as classification, tagging and bulk summarising, pulling text and fields out of images, analysing a long document or codebase in one request.

Provider's own description

Extract is a description-driven entity extraction model from ScaleDown. Instead of a fixed set of entity types, callers describe the fields they want in plain English through `provider.options.scaledown.entities`, and the...

Three real jobs, priced on this model

Each figure is derived from the prices above and moves when they do.

JobTokensCost
One chat turn with a medium history1,500 in + 400 out21 toman
Summarising a ten-page document4,000 in + 600 out55 toman
Classifying a thousand short rows120,000 in + 20,000 out1,662 toman

Frequently asked

How do I call Extract from Iran?
Sign up with your mobile number, top the wallet up in toman, create an API key, then in the official OpenAI SDK point base_url at https://api.uttapen.ir/v1 and set model to "scaledown/extract". Nothing else in your code changes.
What does Extract cost in toman?
13,849 toman per 1M input tokens and 0 toman per 1M output tokens; a 1,000-word request is around 18 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does Extract take?
Up to 1,000,000 tokens per request, roughly 750k words. A single answer can reach 900,000 tokens.
Does Extract support streaming and image input?
Streaming (stream=true) works on every model here. Image input is accepted through image_url, as a data URI or a public URL.