How do I call GPT-5.6 Luna 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". Nothing else in your code changes.

What does GPT-5.6 Luna cost in toman?

69,244 toman per 1M input tokens and 415,463 toman per 1M output tokens; a 1,000-word request is around 630 toman. You pay for the usage that request actually reported, and a failed request costs nothing.

How much input does GPT-5.6 Luna 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 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.

GPT-5.6 Luna API: toman pricing and code

openai/gpt-5.6-luna

Image inputTool callingReasoningJSON outputFile input

Create a key Try it

Input · per 1M tokens

69,244 toman

Output · per 1M tokens

415,463 toman

One 1,000-word request ≈

630 toman

You are billed for the usage the request actually reported. Prices follow the market. Cached input: 6,924 toman per 1M tokens. Web search: 3,462.19 toman per request.Pricing and top-ups

GPT-5.6 Luna 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.

curlPythonNode.js

# 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",
    "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",
    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",
  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 good for?

GPT-5.6 Luna comes from OpenAI; in uttapen you reach it with the model id "openai/gpt-5.6-luna". It accepts up to 1,050,000 tokens of input per call, so a long document or several source files fit in a single request. A single response can run to 128,000 tokens. On price it sits in the "cheap" band — cheaper than 232 and dearer than 157 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 630 toman per 1,000-word exchange (69,244 in, 415,463 out, per 1M tokens). It supports cached input: repeated context is billed at 6,924 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 Muse Glimmer 30B: GPT-5.6 Luna works out about 9% 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 159 thousand-word requests on GPT-5.6 Luna, and every 1,000 toman is about 1,587 words of round trip. A job with one million input tokens and one million output tokens comes to 484,707 toman in total. Filling this model's 1,050,000-token window costs 72,706 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: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 about 2× more expensive (630 against 315 toman). On parameters, this one takes max_completion_tokens.

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. Among the less common parameters it accepts max_completion_tokens, reasoning_effort, verbosity — 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-Pro 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.

Provider's own description

GPT-5.6 Luna is a fast, cost-efficient model in OpenAI's GPT-5.6 series. It is suited for high-volume, latency-sensitive tasks such as chat, classification, and lightweight agentic workflows, providing capable reasoning for...

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	270 toman
Summarising a ten-page document	4,000 in + 600 out	526 toman
Classifying a thousand short rows	120,000 in + 20,000 out	16,619 toman

Job

Tokens

Cost

One chat turn with a medium history

1,500 in + 400 out

270 toman

Summarising a ten-page document

4,000 in + 600 out

526 toman

Classifying a thousand short rows

120,000 in + 20,000 out

16,619 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	openai/gpt-5.6-luna:batch	207,732	1,050,000

Variant

Model id

Output / 1M

Context

batch

openai/gpt-5.6-luna:batch

207,732

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 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". Nothing else in your code changes.
What does GPT-5.6 Luna cost in toman?: 69,244 toman per 1M input tokens and 415,463 toman per 1M output tokens; a 1,000-word request is around 630 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does GPT-5.6 Luna 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 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.