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Perceptron Mk1.5 API: toman pricing and code

perceptron/perceptron-mk1.5

Image inputTool callingReasoningJSON outputAudio input
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Input · per 1M tokens
51,933 toman
Output · per 1M tokens
519,329 toman
One 1,000-word request ≈
743 toman

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

Perceptron Mk1.5 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-mk1.5",
    "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-mk1.5",
    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-mk1.5",
  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 Mk1.5 good for?

Perceptron Mk1.5 is one of Perceptron's models. Put "perceptron/perceptron-mk1.5" in the model field and the rest of your code stays as it is. Perceptron Mk1.5 keeps 36,864 tokens in view at once, and that number is what caps the conversation history in your product. A single response can run to 8,192 tokens. On price it sits in the "cheap" band — cheaper than 213 and dearer than 178 of the other paid models in the catalogue.

Capabilities available on this id: it reads images directly, which makes it a real option for invoices, forms and screenshots; it accepts audio input; 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; 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.

For a back-of-envelope figure: about 743 toman per 1,000-word exchange (51,933 in, 519,329 out, per 1M tokens). 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 Command A+: Perceptron Mk1.5 works out about 9% 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 135 thousand-word requests on Perceptron Mk1.5, and every 1,000 toman is about 1,346 words of round trip. A job with one million input tokens and one million output tokens comes to 571,262 toman in total. Filling this model's 36,864-token window costs 1,914 toman on the input side alone, which is the real reason to keep conversation history short.

Perceptron has 2 models in our catalogue; the cheapest is Perceptron Mk1 at 743 toman per thousand words and the dearest Perceptron Mk1.5 at 743. Among the less common parameters it accepts presence_penalty, reasoning_effort, top_k — all through the standard request body. It does not support response_format, 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.

What to use it for: high-volume work such as classification, tagging and bulk summarising, logic puzzles and code review, pulling text and fields out of images, agents that reach out to APIs and databases, extracting data against a fixed schema.

Provider's own description

Perceptron Mk1.5 is Perceptron's embodied reasoning model for physical agents. It accepts text, image, video, and audio input, and answers with text plus optional structured annotations: points, boxes, polygons, tracks,...

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 out286 toman
Summarising a ten-page document4,000 in + 600 out519 toman
Classifying a thousand short rows120,000 in + 20,000 out16,619 toman

Frequently asked

How do I call Perceptron Mk1.5 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-mk1.5". Nothing else in your code changes.
What does Perceptron Mk1.5 cost in toman?
51,933 toman per 1M input tokens and 519,329 toman per 1M output tokens; a 1,000-word request is around 743 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does Perceptron Mk1.5 take?
Up to 36,864 tokens per request, roughly 28k words. A single answer can reach 8,192 tokens.
Does Perceptron Mk1.5 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.