GPT-4o (2024-05-13) API: toman pricing and code
openai/gpt-4o-2024-05-13
visiontoolsjsonfilesYou 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 359 and dearer than 47 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 1,450,625 toman per 1M tokens and output at 4,351,875 — 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: GPT-4o (2024-05-13) works out roughly 1.1× 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 13 thousand-word requests on GPT-4o (2024-05-13), and every 1,000 toman is about 133 words of round trip. A job with one million input tokens and one million output tokens comes to 5,802,500 toman in total. Filling this model's 128,000-token window costs 185,680 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-4-turbo:batch", and choosing between those two is where most people hesitate. Both land at nearly the same price on a thousand-word request (7,543 and 7,543 toman). On parameters, this one takes max_completion_tokens, prediction, web_search_options.
OpenAI has 95 models in our catalogue; the cheapest is gpt-oss-20b at 60 toman per thousand words and the dearest o1-pro at 282,872. Among the less common parameters it accepts logit_bias, logprobs, max_completion_tokens, prediction, top_logprobs, web_search_options — 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.
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.
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.
| Job | Tokens | Cost |
|---|---|---|
| One chat turn with a medium history | 1,500 in + 400 out | 3,917 toman |
| Summarising a ten-page document | 4,000 in + 600 out | 8,414 toman |
| Classifying a thousand short rows | 120,000 in + 20,000 out | 261,113 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?
- 1,450,625 toman per 1M input tokens and 4,351,875 toman per 1M output tokens; a 1,000-word request is around 7,543 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.