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GPT-6 Sol Pro (batch) API: toman pricing and code

openai/gpt-6-sol-pro:batch

Image inputTool callingReasoningJSON outputFile input
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Input · per 1M tokens
346,219 toman
Output · per 1M tokens
1,731,097 toman
One 1,000-word request ≈
2,701 toman

You are billed for the usage the request actually reported. Prices follow the market. Cached input: 34,622 toman per 1M tokens. Web search: 3,462.19 toman per request.Pricing and top-ups

GPT-6 Sol Pro (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/gpt-6-sol-pro: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/gpt-6-sol-pro: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/gpt-6-sol-pro: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 GPT-6 Sol Pro (batch) good for?

GPT-6 Sol Pro (batch) is one of OpenAI's models. Put "openai/gpt-6-sol-pro:batch" in the model field and the rest of your code stays as it is. GPT-6 Sol Pro (batch) keeps 1,050,000 tokens in view at once, and that number is what caps the conversation history in your product. A single response can run to 128,000 tokens. On price it sits in the "mid-range" band — cheaper than 108 and dearer than 278 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 2,701 toman per 1,000-word exchange (346,219 in, 1,731,097 out, per 1M tokens). It supports cached input: repeated context is billed at 34,622 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 Haiku 4.5, and GPT-6 Sol Pro (batch) 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 37 thousand-word requests on GPT-6 Sol Pro (batch), and every 1,000 toman is about 370 words of round trip. A job with one million input tokens and one million output tokens comes to 2,077,316 toman in total. Filling this model's 1,050,000-token window costs 363,530 toman on the input side alone, which is the real reason to keep conversation history short.

This id gets confused with "openai/gpt-6-sol-pro", 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 about 2× cheaper (2,701 against 5,401 toman). On parameters the other 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 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: 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.

Provider's own description

GPT-6 Sol Pro is the same underlying model as GPT-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 out1,212 toman
Summarising a ten-page document4,000 in + 600 out2,424 toman
Classifying a thousand short rows120,000 in + 20,000 out76,168 toman

Other variants of this model

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

VariantModel idOutput / 1MContext
standardopenai/gpt-6-sol-pro3,462,1941,050,000

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

Frequently asked

How do I call GPT-6 Sol Pro (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/gpt-6-sol-pro:batch". Nothing else in your code changes.
What does GPT-6 Sol Pro (batch) cost in toman?
346,219 toman per 1M input tokens and 1,731,097 toman per 1M output tokens; a 1,000-word request is around 2,701 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does GPT-6 Sol Pro (batch) take?
Up to 1,050,000 tokens per request, roughly 788k words. A single answer can reach 128,000 tokens.
Does GPT-6 Sol Pro (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.