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o3 Mini High API: toman pricing and code

openai/o3-mini-high

toolsreasoningjsonfiles
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Input · per 1M tokens
319,138 toman
Output · per 1M tokens
1,276,550 toman
One 1,000-word request ≈
2,074 toman

You are billed for the usage the request actually reported. Prices follow the market. Cached input: 159,569 toman / 1M. Web search: 2,901.25 toman / request.Pricing and top-ups

o3 Mini High example: tool calling

The example is picked from this model's own capabilities. Drop in your key and it runs as is.

from openai import OpenAI
import json

client = OpenAI(base_url="https://api.uttapen.ir/v1", api_key="sk-up-…")

tools = [{
    "type": "function",
    "function": {
        "name": "check_stock",
        "description": "Returns the stock level of a product",
        "parameters": {
            "type": "object",
            "properties": {"sku": {"type": "string"}},
            "required": ["sku"],
        },
    },
}]

messages = [{"role": "user", "content": "How many of the Nike NK-42 shoe are in stock?"}]
first = client.chat.completions.create(model="openai/o3-mini-high", messages=messages, tools=tools)
call = first.choices[0].message.tool_calls[0]

# you run the function yourself — the model never touches the database
args = json.loads(call.function.arguments)
result = {"sku": args["sku"], "qty": 7}

messages += [first.choices[0].message, {"role": "tool", "tool_call_id": call.id, "content": json.dumps(result)}]
final = client.chat.completions.create(model="openai/o3-mini-high", messages=messages, tools=tools)
print(final.choices[0].message.content)

What is o3 Mini High good for?

o3 Mini High is one of OpenAI's models. Put "openai/o3-mini-high" in the model field and the rest of your code stays as it is. o3 Mini High 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.

Capabilities available on this id: 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.

For a back-of-envelope figure: about 2,074 toman per 1,000-word exchange (319,138 in, 1,276,550 out, per 1M tokens). It supports cached input: repeated context is billed at 159,569 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 GLM 5.3: o3 Mini High works out roughly 1.1× cheaper, 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 o3 Mini High, 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.

Its nearest relative in the catalogue is "openai/o3-mini", and choosing between those two is where most people hesitate. Both land at nearly the same price on a thousand-word request (2,074 and 2,074 toman). On parameters, this one takes reasoning_effort.

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 reasoning_effort — all through the standard request body. 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.

What to use it for: product chatbots and internal assistants, where cost and quality have to balance, logic puzzles and code review, 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

OpenAI o3-mini-high is the same model as [o3-mini](/openai/o3-mini) with reasoning_effort set to high. o3-mini is a cost-efficient language model optimized for STEM reasoning tasks, particularly excelling in science, mathematics, and...

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 out989 toman
Summarising a ten-page document4,000 in + 600 out2,042 toman
Classifying a thousand short rows120,000 in + 20,000 out63,828 toman

Frequently asked

How do I call o3 Mini High 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-mini-high". Nothing else in your code changes.
What does o3 Mini High 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 o3 Mini High take?
Up to 200,000 tokens per request, roughly 150k words. A single answer can reach 100,000 tokens.
Does o3 Mini High support streaming and tool calling?
Streaming (stream=true) works on every model here. This one supports tool calling in the standard OpenAI shape. Structured output through response_format works too.