o3 (batch) API: toman pricing and code
openai/o3:batch
visiontoolsreasoningjsonfilesYou are billed for the usage the request actually reported. Prices follow the market. Cached input: 72,531 toman / 1M. Web search: 2,901.25 toman / request.Pricing and top-ups
o3 (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/o3: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/o3: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/o3: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 o3 (batch) good for?
o3 (batch) comes from OpenAI; in uttapen you reach it with the model id "openai/o3:batch". It accepts up to 200,000 tokens of input per call, so a long document or several source files fit in a single request. A single response can run to 100,000 tokens. On price it sits in the "mid-range" band — cheaper than 265 and dearer than 141 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 290,125 toman per 1M input tokens and 1,160,500 per 1M output tokens. A 1,000-word round trip on o3 (batch) lands near 1,886 toman. It supports cached input: repeated context is billed at 72,531 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 Kimi K2.6: o3 (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 53 thousand-word requests on o3 (batch), and every 1,000 toman is about 530 words of round trip. A job with one million input tokens and one million output tokens comes to 1,450,625 toman in total. Filling this model's 200,000-token window costs 58,025 toman on the input side alone, which is the real reason to keep conversation history short.
This id gets confused with "openai/o3", 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 (1,886 against 3,772 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.
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.
| Job | Tokens | Cost |
|---|---|---|
| One chat turn with a medium history | 1,500 in + 400 out | 899 toman |
| Summarising a ten-page document | 4,000 in + 600 out | 1,857 toman |
| Classifying a thousand short rows | 120,000 in + 20,000 out | 58,025 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/o3 | 2,321,000 | 200,000 |
Put the variant's id verbatim in the model field; nothing else in your code changes.
Frequently asked
- How do I call o3 (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/o3:batch". Nothing else in your code changes.
- What does o3 (batch) cost in toman?
- 290,125 toman per 1M input tokens and 1,160,500 toman per 1M output tokens; a 1,000-word request is around 1,886 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
- How much input does o3 (batch) take?
- Up to 200,000 tokens per request, roughly 150k words. A single answer can reach 100,000 tokens.
- Does o3 (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.