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GPT-4o-mini API: toman pricing and code

openai/gpt-4o-mini

Image inputTool callingJSON outputFile input
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Input · per 1M tokens
51,933 toman
Output · per 1M tokens
207,732 toman
One 1,000-word request ≈
338 toman

You are billed for the usage the request actually reported. Prices follow the market. Cached input: 25,966 toman per 1M tokens.Pricing and top-ups

GPT-4o-mini 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": "openai/gpt-4o-mini",
    "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-4o-mini",
    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-4o-mini",
  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-4o-mini good for?

OpenAI publishes this model; we expose it under the id "openai/gpt-4o-mini". Its context window is 128,000 tokens, roughly 96k English words in one request. A single response can run to 16,384 tokens. On price it sits in the "cheap" band — cheaper than 281 and dearer than 105 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 51,933 toman per 1M input tokens and 207,732 per 1M output tokens. A 1,000-word round trip on GPT-4o-mini lands near 338 toman. It supports cached input: repeated context is billed at 25,966 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 Mistral Small 4, and GPT-4o-mini costs about the same, 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 296 thousand-word requests on GPT-4o-mini, and every 1,000 toman is about 2,959 words of round trip. A job with one million input tokens and one million output tokens comes to 259,665 toman in total. Filling this model's 128,000-token window costs 6,647 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-2024-07-18", and choosing between those two is where most people hesitate. Both land at nearly the same price on a thousand-word request (338 and 338 toman). On parameters, this one takes max_completion_tokens.

OpenAI has 81 models in our catalogue; the cheapest is gpt-oss-20b at 49 toman per thousand words and the dearest GPT-5.5 Pro at 94,518. Among the less common parameters it accepts logit_bias, logprobs, max_completion_tokens, prediction, presence_penalty, top_logprobs — all through the standard request body. It does not support include_reasoning, 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 Nova Micro 1.0 at 128,000 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.

Provider's own description

GPT-4o mini is OpenAI's newest model after [GPT-4 Omni](/models/openai/gpt-4o), supporting both text and image inputs with text outputs. As their most advanced small model, it is many multiples more affordable...

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 out161 toman
Summarising a ten-page document4,000 in + 600 out332 toman
Classifying a thousand short rows120,000 in + 20,000 out10,387 toman

Frequently asked

How do I call GPT-4o-mini 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-4o-mini". Nothing else in your code changes.
What does GPT-4o-mini cost in toman?
51,933 toman per 1M input tokens and 207,732 toman per 1M output tokens; a 1,000-word request is around 338 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does GPT-4o-mini take?
Up to 128,000 tokens per request, roughly 96k words. A single answer can reach 16,384 tokens.
Does GPT-4o-mini 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.