o1-pro API: toman pricing and code
openai/o1-pro
visionreasoningjsonfilesYou are billed for the usage the request actually reported. Prices follow the market. Web search: 2,901.25 toman / request.Pricing and top-ups
o1-pro 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/o1-pro",
"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/o1-pro",
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/o1-pro",
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 o1-pro good for?
The id for o1-pro in our API is "openai/o1-pro", served from OpenAI. Context is 200,000 tokens; past that you have to summarise the history yourself. A single response can run to 100,000 tokens. On price it sits in the "expensive" band — cheaper than 405 and dearer than 1 of the other paid models in the catalogue.
Capabilities available on this id: it reads images directly, which makes it a real option for invoices, forms and screenshots; it takes files such as PDFs as input; 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.
For a back-of-envelope figure: about 282,872 toman per 1,000-word exchange (43,518,750 in, 174,075,000 out, per 1M tokens). 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 Sonar Pro: o1-pro works out roughly 41.7× more expensive, and its context window is the same size. 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 0 thousand-word requests on o1-pro, and every 1,000 toman is about 4 words of round trip. A job with one million input tokens and one million output tokens comes to 217,593,750 toman in total. Filling this model's 200,000-token window costs 8,703,750 toman on the input side alone, which is the real reason to keep conversation history short.
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, tool_choice, tools, 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.
What to use it for: work where the quality of the answer matters more than its cost, logic puzzles and code review, pulling text and fields out of images, extracting data against a fixed schema, analysing a long document or codebase in one request.
The o1 series of models are trained with reinforcement learning to think before they answer and perform complex reasoning. The o1-pro model uses more compute to think harder and provide...
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 | 134,908 toman |
| Summarising a ten-page document | 4,000 in + 600 out | 278,520 toman |
| Classifying a thousand short rows | 120,000 in + 20,000 out | 8,703,750 toman |
Frequently asked
- How do I call o1-pro 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/o1-pro". Nothing else in your code changes.
- What does o1-pro cost in toman?
- 43,518,750 toman per 1M input tokens and 174,075,000 toman per 1M output tokens; a 1,000-word request is around 282,872 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
- How much input does o1-pro take?
- Up to 200,000 tokens per request, roughly 150k words. A single answer can reach 100,000 tokens.
- Does o1-pro support streaming and image input?
- Streaming (stream=true) works on every model here. Image input is accepted through image_url, as a data URI or a public URL. Structured output through response_format works too.