Perceptron Mk1 API: toman pricing and code
perceptron/perceptron-mk1
visionreasoningjsonYou are billed for the usage the request actually reported. Prices follow the market.Pricing and top-ups
Perceptron Mk1 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": "perceptron/perceptron-mk1",
"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="perceptron/perceptron-mk1",
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: "perceptron/perceptron-mk1",
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 Perceptron Mk1 good for?
Perceptron Mk1 comes from Perceptron; in uttapen you reach it with the model id "perceptron/perceptron-mk1". It accepts up to 32,768 tokens of input per call, so a long document or several source files fit in a single request. A single response can run to 8,192 tokens. On price it sits in the "cheap" band — cheaper than 173 and dearer than 233 of the 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 returns schema-valid JSON through response_format, ready to hand to your code; it has a reasoning mode that pays off on multi-step problems, maths and debugging. All of it works through the standard parameters of the official OpenAI SDK — no custom client, no wrapper. Keep in mind that reasoning tokens are output tokens and do appear on the bill.
Pricing is 43,519 toman per 1M input tokens and 435,188 per 1M output tokens. A 1,000-word round trip on Perceptron Mk1 lands near 622 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 Nano Banana 2 Lite (Gemini 3.1 Flash Lite Image): Perceptron Mk1 works out roughly 1.1× cheaper, 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 161 thousand-word requests on Perceptron Mk1, and every 1,000 toman is about 1,608 words of round trip. A job with one million input tokens and one million output tokens comes to 478,706 toman in total. Filling this model's 32,768-token window costs 1,426 toman on the input side alone, which is the real reason to keep conversation history short.
Among the less common parameters it accepts top_k — all through the standard request body. It does not support response_format, tool_choice, tools, seed, 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-RP 1.0 (8B) at 32,768 tokens.
Good fits: high-volume work such as classification, tagging and bulk summarising, logic puzzles and code review, pulling text and fields out of images, extracting data against a fixed schema.
Perceptron Mk1 (Mark One) is Perceptron's highest-quality vision-language model for video and embodied reasoning.** It accepts image and video inputs paired with natural language queries, and produces detailed visual understanding...
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 | 239 toman |
| Summarising a ten-page document | 4,000 in + 600 out | 435 toman |
| Classifying a thousand short rows | 120,000 in + 20,000 out | 13,926 toman |
Frequently asked
- How do I call Perceptron Mk1 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 "perceptron/perceptron-mk1". Nothing else in your code changes.
- What does Perceptron Mk1 cost in toman?
- 43,519 toman per 1M input tokens and 435,188 toman per 1M output tokens; a 1,000-word request is around 622 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
- How much input does Perceptron Mk1 take?
- Up to 32,768 tokens per request, roughly 25k words. A single answer can reach 8,192 tokens.
- Does Perceptron Mk1 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. Structured output through response_format works too.