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GPT-4o (2024-05-13) API: toman pricing and code

openai/gpt-4o-2024-05-13

Image inputTool callingJSON outputFile input
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Input · per 1M tokens
1,731,097 toman
Output · per 1M tokens
5,193,290 toman
One 1,000-word request ≈
9,002 toman

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

GPT-4o (2024-05-13) 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-2024-05-13",
    "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-2024-05-13",
    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-2024-05-13",
  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 (2024-05-13) good for?

OpenAI publishes this model; we expose it under the id "openai/gpt-4o-2024-05-13". Its context window is 128,000 tokens, roughly 96k English words in one request. A single response can run to 4,096 tokens. On price it sits in the "expensive" band — cheaper than 31 and dearer than 358 of the other paid models in the catalogue.

What you get on top of text in, text out: 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.

Input runs at 1,731,097 toman per 1M tokens and output at 5,193,290 — output costs 3× input, so trimming the answer saves more than trimming the prompt. 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 Claude Sonnet 4.5: GPT-4o (2024-05-13) works out about 10% more expensive, 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 11 thousand-word requests on GPT-4o (2024-05-13), and every 1,000 toman is about 111 words of round trip. A job with one million input tokens and one million output tokens comes to 6,924,387 toman in total. Filling this model's 128,000-token window costs 221,580 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", and choosing between those two is where most people hesitate. On a thousand-word request this variant works out about 60% more expensive (9,002 against 5,626 toman). Maximum answer length differs as well: 4,096 against 16,384 tokens. On parameters the other takes max_tokens, prediction.

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 logit_bias, logprobs, max_completion_tokens, presence_penalty, top_logprobs, web_search_options — 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 Nova Micro 1.0 at 128,000 tokens.

Where it makes sense: work where the quality of the answer matters more than its cost, 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 ("o" for "omni") is OpenAI's latest AI model, supporting both text and image inputs with text outputs. It maintains the intelligence level of [GPT-4 Turbo](/models/openai/gpt-4-turbo) while being twice as...

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 out4,674 toman
Summarising a ten-page document4,000 in + 600 out10,040 toman
Classifying a thousand short rows120,000 in + 20,000 out311,597 toman

Frequently asked

How do I call GPT-4o (2024-05-13) 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-2024-05-13". Nothing else in your code changes.
What does GPT-4o (2024-05-13) cost in toman?
1,731,097 toman per 1M input tokens and 5,193,290 toman per 1M output tokens; a 1,000-word request is around 9,002 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does GPT-4o (2024-05-13) take?
Up to 128,000 tokens per request, roughly 96k words. A single answer can reach 4,096 tokens.
Does GPT-4o (2024-05-13) 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.