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o3 API: toman pricing and code

openai/o3

Image inputTool callingReasoningJSON outputFile input
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Input · per 1M tokens
692,439 toman
Output · per 1M tokens
2,769,755 toman
One 1,000-word request ≈
4,501 toman

You are billed for the usage the request actually reported. Prices follow the market. Cached input: 173,110 toman per 1M tokens. Web search: 3,462.19 toman per request.Pricing and top-ups

o3 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/o3",
    "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/o3",
    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/o3",
  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 o3 good for?

The id for o3 in our API is "openai/o3", 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 "mid-range" band — cheaper than 83 and dearer than 311 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 692,439 toman per 1M input tokens and 2,769,755 per 1M output tokens. A 1,000-word round trip on o3 lands near 4,501 toman. It supports cached input: repeated context is billed at 173,110 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 Nano Banana 2.1: o3 works out about 10% more expensive, and its context window is larger. 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 22 thousand-word requests on o3, and every 1,000 toman is about 222 words of round trip. A job with one million input tokens and one million output tokens comes to 3,462,194 toman in total. Filling this model's 200,000-token window costs 138,488 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-5", and choosing between those two is where most people hesitate. Both land at nearly the same price on a thousand-word request (4,501 and 5,063 toman). The context windows differ too: 200,000 against 400,000 tokens. Maximum answer length differs as well: 100,000 against 128,000 tokens. On parameters the other takes max_completion_tokens, reasoning_effort, verbosity.

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. 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 Haiku 4.5 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.

Provider's own description

o3 is a well-rounded and powerful model across domains. It sets a new standard for math, science, coding, and visual reasoning tasks. It also excels at technical writing and instruction-following....

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 out2,147 toman
Summarising a ten-page document4,000 in + 600 out4,432 toman
Classifying a thousand short rows120,000 in + 20,000 out138,488 toman

Frequently asked

How do I call o3 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/o3". Nothing else in your code changes.
What does o3 cost in toman?
692,439 toman per 1M input tokens and 2,769,755 toman per 1M output tokens; a 1,000-word request is around 4,501 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does o3 take?
Up to 200,000 tokens per request, roughly 150k words. A single answer can reach 100,000 tokens.
Does o3 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.