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GPT-5.1-Codex-Mini API: toman pricing and code

openai/gpt-5.1-codex-mini

Image inputTool callingReasoningJSON output
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Input · per 1M tokens
86,555 toman
Output · per 1M tokens
692,439 toman
One 1,000-word request ≈
1,013 toman

You are billed for the usage the request actually reported. Prices follow the market. Cached input: 10,387 toman per 1M tokens. Web search: 3,462.19 toman per request.Pricing and top-ups

GPT-5.1-Codex-Mini 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-5.1-codex-mini",
    "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-5.1-codex-mini",
    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-5.1-codex-mini",
  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-5.1-Codex-Mini good for?

GPT-5.1-Codex-Mini comes from OpenAI; in uttapen you reach it with the model id "openai/gpt-5.1-codex-mini". It accepts up to 400,000 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 "mid-range" band — cheaper than 185 and dearer than 199 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 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 86,555 toman per 1M tokens and output at 692,439 — output costs 8× input, so trimming the answer saves more than trimming the prompt. It supports cached input: repeated context is billed at 10,387 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 Seed 1.6, and GPT-5.1-Codex-Mini 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 99 thousand-word requests on GPT-5.1-Codex-Mini, and every 1,000 toman is about 987 words of round trip. A job with one million input tokens and one million output tokens comes to 778,994 toman in total. Filling this model's 400,000-token window costs 34,622 toman on the input side alone, which is the real reason to keep conversation history short.

Its nearest relative in the catalogue is "openai/gpt-5-mini", and choosing between those two is where most people hesitate. Both land at nearly the same price on a thousand-word request (1,013 and 1,013 toman). On parameters the other takes max_tokens.

OpenAI has 81 models in our catalogue; the cheapest is gpt-oss-120b at 26 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 max_tokens, 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 Llama 4 Scout at 327,680 tokens.

Where it makes sense: 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-5.1-Codex-Mini is a smaller and faster version of GPT-5.1-Codex

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 out407 toman
Summarising a ten-page document4,000 in + 600 out762 toman
Classifying a thousand short rows120,000 in + 20,000 out24,235 toman

Frequently asked

How do I call GPT-5.1-Codex-Mini 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-5.1-codex-mini". Nothing else in your code changes.
What does GPT-5.1-Codex-Mini cost in toman?
86,555 toman per 1M input tokens and 692,439 toman per 1M output tokens; a 1,000-word request is around 1,013 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does GPT-5.1-Codex-Mini take?
Up to 400,000 tokens per request, roughly 300k words. A single answer can reach 128,000 tokens.
Does GPT-5.1-Codex-Mini 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.