How do I call GPT-4.1 Nano 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 "openai/gpt-4.1-nano". Nothing else in your code changes.

What does GPT-4.1 Nano cost in toman?

34,622 toman per 1M input tokens and 138,488 toman per 1M output tokens; a 1,000-word request is around 225 toman. You pay for the usage that request actually reported, and a failed request costs nothing.

How much input does GPT-4.1 Nano take?

Up to 1,047,576 tokens per request, roughly 786k words. A single answer can reach 942,818 tokens.

Does GPT-4.1 Nano 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.

GPT-4.1 Nano API: toman pricing and code

openai/gpt-4.1-nano

Image inputTool callingJSON outputFile input

Create a key Try it

Input · per 1M tokens

34,622 toman

Output · per 1M tokens

138,488 toman

One 1,000-word request ≈

225 toman

You are billed for the usage the request actually reported. Prices follow the market. Cached input: 10,387 toman per 1M tokens. Web search: 3,462.19 toman per request.Pricing and top-ups

GPT-4.1 Nano 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.

curlPythonNode.js

# 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": "openai/gpt-4.1-nano",
    "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="openai/gpt-4.1-nano",
    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: "openai/gpt-4.1-nano",
  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 GPT-4.1 Nano good for?

The id for GPT-4.1 Nano in our API is "openai/gpt-4.1-nano", served from OpenAI. Context is 1,047,576 tokens; past that you have to summarise the history yourself. A single response can run to 942,818 tokens. On price it sits in the "very cheap" band — cheaper than 317 and dearer than 74 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 takes files such as PDFs as 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. All of it works through the standard parameters of the official OpenAI SDK — no custom client, no wrapper.

Pricing is 34,622 toman per 1M input tokens and 138,488 per 1M output tokens. A 1,000-word round trip on GPT-4.1 Nano lands near 225 toman. It supports cached input: repeated context is billed at 10,387 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 Seed-2.0-Mini, and GPT-4.1 Nano costs about the same, 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 444 thousand-word requests on GPT-4.1 Nano, and every 1,000 toman is about 4,444 words of round trip. A job with one million input tokens and one million output tokens comes to 173,110 toman in total. Filling this model's 1,047,576-token window costs 36,269 toman on the input side alone, which is the real reason to keep conversation history short.

Its nearest relative in the catalogue is "openai/gpt-4o-mini", and choosing between those two is where most people hesitate. On a thousand-word request this variant works out about 33% cheaper (225 against 338 toman). The context windows differ too: 1,047,576 against 128,000 tokens. Maximum answer length differs as well: 942,818 against 16,384 tokens. On parameters the other takes frequency_penalty, logit_bias, logprobs, max_tokens.

OpenAI has 81 models in our catalogue; the cheapest is gpt-oss-20b (batch) at 61 toman per thousand words and the dearest GPT-5.5 Pro at 94,518. Among the less common parameters it accepts max_completion_tokens — all through the standard request body. It does not support include_reasoning, max_tokens, reasoning, 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 DeepSeek V4 Flash 0423 at 1,048,576 tokens.

Good fits: high-volume work such as classification, tagging and bulk summarising, 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.

Provider's own description

For tasks that demand low latency, GPT‑4.1 nano is the fastest and cheapest model in the GPT-4.1 series. It delivers exceptional performance at a small size with its 1 million...

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	107 toman
Summarising a ten-page document	4,000 in + 600 out	222 toman
Classifying a thousand short rows	120,000 in + 20,000 out	6,924 toman

Job

Tokens

Cost

One chat turn with a medium history

1,500 in + 400 out

107 toman

Summarising a ten-page document

4,000 in + 600 out

222 toman

Classifying a thousand short rows

120,000 in + 20,000 out

6,924 toman

Price history

Date	Input (toman/1M)	Output (toman/1M)
26 Oct 2026	34,622	138,488
7 Sep 2026	34,622	138,488

Every price change for this model is recorded. Toman figures use today's rate.

Frequently asked

How do I call GPT-4.1 Nano 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 "openai/gpt-4.1-nano". Nothing else in your code changes.
What does GPT-4.1 Nano cost in toman?: 34,622 toman per 1M input tokens and 138,488 toman per 1M output tokens; a 1,000-word request is around 225 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does GPT-4.1 Nano take?: Up to 1,047,576 tokens per request, roughly 786k words. A single answer can reach 942,818 tokens.
Does GPT-4.1 Nano 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.