GPT-4o-mini (batch) API: toman pricing and code
openai/gpt-4o-mini:batch
visiontoolsjsonfilesYou are billed for the usage the request actually reported. Prices follow the market. Cached input: 10,880 toman / 1M. Web search: 2,901.25 toman / request.Pricing and top-ups
GPT-4o-mini (batch) 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:batch",
"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:batch",
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:batch",
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 (batch) good for?
GPT-4o-mini (batch) is one of OpenAI's models. Put "openai/gpt-4o-mini:batch" in the model field and the rest of your code stays as it is. GPT-4o-mini (batch) keeps 128,000 tokens in view at once, and that number is what caps the conversation history in your product. A single response can run to 16,384 tokens. On price it sits in the "very cheap" band — cheaper than 61 and dearer than 345 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 21,759 toman per 1M input tokens and 87,038 per 1M output tokens. A 1,000-word round trip on GPT-4o-mini (batch) lands near 141 toman. It supports cached input: repeated context is billed at 10,880 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 1.6 Flash: GPT-4o-mini (batch) works out roughly 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 709 thousand-word requests on GPT-4o-mini (batch), and every 1,000 toman is about 7,092 words of round trip. A job with one million input tokens and one million output tokens comes to 108,797 toman in total. Filling this model's 128,000-token window costs 2,785 toman on the input side alone, which is the real reason to keep conversation history short.
This id gets confused with "openai/gpt-4o-mini", because the underlying model is the same. The difference is the suffix: "batch" against the standard variant. On a thousand-word request this variant works out 2× cheaper (141 against 283 toman). On parameters the other takes max_completion_tokens.
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, 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.
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.
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.
| Job | Tokens | Cost |
|---|---|---|
| One chat turn with a medium history | 1,500 in + 400 out | 67 toman |
| Summarising a ten-page document | 4,000 in + 600 out | 139 toman |
| Classifying a thousand short rows | 120,000 in + 20,000 out | 4,352 toman |
Other variants of this model
Same core model, different execution terms and different price. This page is the batch (cheaper, slower) variant.
| Variant | Model id | Output / 1M | Context |
|---|---|---|---|
| standard | openai/gpt-4o-mini | 174,075 | 128,000 |
Put the variant's id verbatim in the model field; nothing else in your code changes.
Frequently asked
- How do I call GPT-4o-mini (batch) 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:batch". Nothing else in your code changes.
- What does GPT-4o-mini (batch) cost in toman?
- 21,759 toman per 1M input tokens and 87,038 toman per 1M output tokens; a 1,000-word request is around 141 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
- How much input does GPT-4o-mini (batch) take?
- Up to 128,000 tokens per request, roughly 96k words. A single answer can reach 16,384 tokens.
- Does GPT-4o-mini (batch) 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.