GPT-5.6 Luna Pro API: toman pricing and code
openai/gpt-5.6-luna-pro
visiontoolsreasoningjsonfilesYou are billed for the usage the request actually reported. Prices follow the market. Cached input: 5,803 toman / 1M. Web search: 2,901.25 toman / request.Pricing and top-ups
GPT-5.6 Luna 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/gpt-5.6-luna-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/gpt-5.6-luna-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/gpt-5.6-luna-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 GPT-5.6 Luna Pro good for?
The id for GPT-5.6 Luna Pro in our API is "openai/gpt-5.6-luna-pro", served from OpenAI. Context is 1,050,000 tokens; past that you have to summarise the history yourself. A single response can run to 128,000 tokens. On price it sits in the "cheap" band — cheaper than 151 and dearer than 255 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 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 528 toman per 1,000-word exchange (58,025 in, 348,150 out, per 1M tokens). It supports cached input: repeated context is billed at 5,803 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 MiniMax M3: GPT-5.6 Luna Pro works out roughly 1.1× cheaper, 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 189 thousand-word requests on GPT-5.6 Luna Pro, and every 1,000 toman is about 1,894 words of round trip. A job with one million input tokens and one million output tokens comes to 406,175 toman in total. Filling this model's 1,050,000-token window costs 60,926 toman on the input side alone, which is the real reason to keep conversation history short.
This id gets confused with "openai/gpt-5.6-luna-pro: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 (528 against 264 toman). On parameters, this one takes max_completion_tokens.
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 max_completion_tokens, 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 MiMo-V2.5 at 1,050,000 tokens.
What to use it for: high-volume work such as classification, tagging and bulk summarising, 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.
GPT-5.6 Luna Pro is the same underlying model as [GPT-5.6 Luna](https://openrouter.ai/openai/gpt-5.6-luna), served with `reasoning.mode` set to `pro` for higher-quality responses on complex tasks. Learn more in OpenAI's docs: https://developers.openai.com/api/docs/guides/reasoning#reasoning-mode
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 | 226 toman |
| Summarising a ten-page document | 4,000 in + 600 out | 441 toman |
| Classifying a thousand short rows | 120,000 in + 20,000 out | 13,926 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/gpt-5.6-luna-pro:batch | 174,075 | 1,050,000 |
Put the variant's id verbatim in the model field; nothing else in your code changes.
Frequently asked
- How do I call GPT-5.6 Luna 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/gpt-5.6-luna-pro". Nothing else in your code changes.
- What does GPT-5.6 Luna Pro cost in toman?
- 58,025 toman per 1M input tokens and 348,150 toman per 1M output tokens; a 1,000-word request is around 528 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
- How much input does GPT-5.6 Luna Pro take?
- Up to 1,050,000 tokens per request, roughly 788k words. A single answer can reach 128,000 tokens.
- Does GPT-5.6 Luna Pro 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.