Pareto 26.9 API: toman pricing and code
unbiased/pareto-26.9
You are billed for the usage the request actually reported. Prices follow the market. Cached input: 86,555 toman per 1M tokens.Pricing and top-ups
Pareto 26.9 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": "unbiased/pareto-26.9",
"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="unbiased/pareto-26.9",
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: "unbiased/pareto-26.9",
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 Pareto 26.9 good for?
The id for Pareto 26.9 in our API is "unbiased/
Capabilities available on this id: it reads images directly, which makes it a real option for invoices, forms and screenshots; it supports tool calling, so it can invoke your own functions with valid arguments; it returns schema-valid JSON through response_format, ready to hand to your code. All of it works through the standard parameters of the official OpenAI SDK — no custom client, no wrapper.
For a back-of-envelope figure: about 4,501 toman per 1,000-word exchange (865,548 in, 2,596,645 out, per 1M tokens). It supports cached input: repeated context is billed at 86,555 toman per 1M, which matters a lot if your system prompt is long. 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 Nano Banana 2.1: Pareto 26.9 works out about 10% more expensive, and its context window is larger. 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 22 thousand-word requests on Pareto 26.9, and every 1,000 toman is about 222 words of round trip. A job with one million input tokens and one million output tokens comes to 3,462,194 toman in total. Filling this model's 262,144-token window costs 226,898 toman on the input side alone, which is the real reason to keep conversation history short.
Unbiased has 3 models in our catalogue; the cheapest is Pareto 26.10 at 1,800 toman per thousand words and the dearest Pareto 26.9 at 4,501. It does not support include_reasoning, reasoning, seed, structured_outputs, 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 Aion 3.5 at 262,144 tokens.
What to use it for: product chatbots and internal assistants, where cost and quality have to balance, pulling text and fields out of images, agents that reach out to APIs and databases, extracting data against a fixed schema, analysing a long document or codebase in one request.
Pareto is a multimodal composite model built for research, coding, and agentic workflows, while delivering frontier-level performance across a broad range of general-purpose tasks.
Three real jobs, priced on this model
Each figure is derived from the prices above and moves when they do.
| Job | Tokens | Cost |
|---|---|---|
| One chat turn with a medium history | 1,500 in + 400 out | 2,337 toman |
| Summarising a ten-page document | 4,000 in + 600 out | 5,020 toman |
| Classifying a thousand short rows | 120,000 in + 20,000 out | 155,799 toman |
Frequently asked
- How do I call Pareto 26.9 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 "unbiased/ pareto-26.9". Nothing else in your code changes. - What does Pareto 26.9 cost in toman?
- 865,548 toman per 1M input tokens and 2,596,645 toman per 1M output tokens; a 1,000-word request is around 4,501 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
- How much input does Pareto 26.9 take?
- Up to 262,144 tokens per request, roughly 197k words. A single answer can reach 131,072 tokens.
- Does Pareto 26.9 support streaming and tool calling?
- Streaming (stream=true) works on every model here. This one supports tool calling in the standard OpenAI shape. Image input is accepted through image_url, as a data URI or a public URL. Structured output through response_format works too.