o4 Mini API: toman pricing and code
openai/o4-mini
visiontoolsreasoningjsonfilesYou are billed for the usage the request actually reported. Prices follow the market. Cached input: 79,784 toman / 1M. Web search: 2,901.25 toman / request.Pricing and top-ups
o4 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/o4-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/o4-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/o4-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 o4 Mini good for?
o4 Mini is one of OpenAI's models. Put "openai/o4-mini" in the model field and the rest of your code stays as it is. o4 Mini keeps 200,000 tokens in view at once, and that number is what caps the conversation history in your product. A single response can run to 100,000 tokens. On price it sits in the "mid-range" band — cheaper than 278 and dearer than 128 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; it has a reasoning mode that pays off on multi-step problems, maths and debugging. All of it works through the standard parameters of the official OpenAI SDK — no custom client, no wrapper. Keep in mind that reasoning tokens are output tokens and do appear on the bill.
Pricing is 319,138 toman per 1M input tokens and 1,276,550 per 1M output tokens. A 1,000-word round trip on o4 Mini lands near 2,074 toman. It supports cached input: repeated context is billed at 79,784 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 Gemini 3.5 Flash (batch): o4 Mini 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 48 thousand-word requests on o4 Mini, and every 1,000 toman is about 482 words of round trip. A job with one million input tokens and one million output tokens comes to 1,595,688 toman in total. Filling this model's 200,000-token window costs 63,828 toman on the input side alone, which is the real reason to keep conversation history short.
This id gets confused with "openai/o4-mini:batch", because the underlying model is the same. The difference is the suffix: no suffix (the standard variant) against "batch". On a thousand-word request this variant works out 2× dearer (2,074 against 1,037 toman).
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. It does not support temperature, top_p, 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 Claude 3 Haiku at 200,000 tokens.
Good fits: product chatbots and internal assistants, where cost and quality have to balance, logic puzzles and code review, 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.
OpenAI o4-mini is a compact reasoning model in the o-series, optimized for fast, cost-efficient performance while retaining strong multimodal and agentic capabilities. It supports tool use and demonstrates competitive reasoning...
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 | 989 toman |
| Summarising a ten-page document | 4,000 in + 600 out | 2,042 toman |
| Classifying a thousand short rows | 120,000 in + 20,000 out | 63,828 toman |
Other variants of this model
Same core model, different execution terms and different price. This page is the standard variant.
| Variant | Model id | Output / 1M | Context |
|---|---|---|---|
| batch (cheaper, slower) | openai/o4-mini:batch | 638,275 | 200,000 |
Put the variant's id verbatim in the model field; nothing else in your code changes.
Frequently asked
- How do I call o4 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/o4-mini". Nothing else in your code changes.
- What does o4 Mini cost in toman?
- 319,138 toman per 1M input tokens and 1,276,550 toman per 1M output tokens; a 1,000-word request is around 2,074 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
- How much input does o4 Mini take?
- Up to 200,000 tokens per request, roughly 150k words. A single answer can reach 100,000 tokens.
- Does o4 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.