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GPT-5.6 Sol Pro API: toman pricing and code

openai/gpt-5.6-sol-pro

Image inputTool callingReasoningJSON outputFile input
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Input · per 1M tokens
1,384,877 toman
Output · per 1M tokens
6,924,387 toman
One 1,000-word request ≈
10,802 toman

You are billed for the usage the request actually reported. Prices follow the market. Cached input: 138,488 toman per 1M tokens. Web search: 3,462.19 toman per request.Pricing and top-ups

GPT-5.6 Sol 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-sol-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-sol-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-sol-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 Sol Pro good for?

The id for GPT-5.6 Sol Pro in our API is "openai/gpt-5.6-sol-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 "expensive" band — cheaper than 25 and dearer than 368 of the other paid models in the catalogue.

What you get on top of text in, text out: 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.

Input runs at 1,384,877 toman per 1M tokens and output at 6,924,387 — output costs 5× input, so trimming the answer saves more than trimming the prompt. It supports cached input: repeated context is billed at 138,488 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 Claude Opus 5.5, and GPT-5.6 Sol Pro costs about the same, 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 9 thousand-word requests on GPT-5.6 Sol Pro, and every 1,000 toman is about 93 words of round trip. A job with one million input tokens and one million output tokens comes to 8,309,264 toman in total. Filling this model's 1,050,000-token window costs 1,454,121 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-sol-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 about 2× more expensive (10,802 against 5,401 toman). On parameters, this one takes max_completion_tokens.

OpenAI has 81 models in our catalogue; the cheapest is gpt-oss-20b at 49 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.

Where it makes sense: work where the quality of the answer matters more than its cost, 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 Sol Pro is the same underlying model as GPT-5.6 Sol, served with `reasoning.mode` set to `pro` for higher-quality responses on complex tasks. **Cost note:** pro mode spends far more...

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 out4,847 toman
Summarising a ten-page document4,000 in + 600 out9,694 toman
Classifying a thousand short rows120,000 in + 20,000 out304,673 toman

Other variants of this model

Same core model, different execution terms and different price. This page is the standard variant.

VariantModel idOutput / 1MContext
batchopenai/gpt-5.6-sol-pro:batch3,462,1941,050,000

Put the variant's id verbatim in the model field; nothing else in your code changes.

Price history

DateInput (toman/1M)Output (toman/1M)
14 Oct 20261,384,8776,924,387
5 Oct 2026692,4393,462,194
28 Sep 20261,384,8776,924,387
7 Sep 2026692,4393,462,194

Every price change for this model is recorded. Toman figures use today's rate.

Frequently asked

How do I call GPT-5.6 Sol 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-sol-pro". Nothing else in your code changes.
What does GPT-5.6 Sol Pro cost in toman?
1,384,877 toman per 1M input tokens and 6,924,387 toman per 1M output tokens; a 1,000-word request is around 10,802 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does GPT-5.6 Sol 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 Sol 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.