GPT-5 Image Mini API: toman pricing and code
openai/gpt-5-image-mini
You are billed for the usage the request actually reported. Prices follow the market. Cached input: 86,555 toman per 1M tokens. Per generated image: 2.77 toman. Web search: 3,462.19 toman per request.Pricing and top-ups
GPT-5 Image Mini example: image generation
The example is picked from this model's own capabilities. Drop in your key and it runs as is.
curl https://api.uttapen.ir/v1/chat/completions \
-H "Authorization: Bearer sk-up-…" \
-H "Content-Type: application/json" \
-d '{
"model": "openai/gpt-5-image-mini",
"messages": [{"role": "user", "content": "A minimal photo of a coffee cup on a wooden table, natural light"}],
"modalities": ["image", "text"]
}'import base64
from openai import OpenAI
client = OpenAI(base_url="https://api.uttapen.ir/v1", api_key="sk-up-…")
resp = client.chat.completions.create(
model="openai/gpt-5-image-mini",
messages=[{"role": "user", "content": "A minimal photo of a coffee cup on a wooden table, natural light"}],
extra_body={"modalities": ["image", "text"]},
)
# images are billed on image output tokens — check the balance before generating in bulk
for img in resp.choices[0].message.images or []:
data = img["image_url"]["url"].split(",", 1)[1]
open("out.png", "wb").write(base64.b64decode(data))
print("saved out.png")import OpenAI from "openai";
import { writeFileSync } from "node:fs";
const client = new OpenAI({ baseURL: "https://api.uttapen.ir/v1", apiKey: "sk-up-…" });
const resp = await client.chat.completions.create({
model: "openai/gpt-5-image-mini",
messages: [{ role: "user", content: "A minimal photo of a coffee cup on a wooden table, natural light" }],
modalities: ["image", "text"],
});
for (const img of resp.choices[0].message.images ?? []) {
const b64 = img.image_url.url.split(",", 2)[1];
writeFileSync("out.png", Buffer.from(b64, "base64"));
}What is GPT-5 Image Mini good for?
GPT-5 Image Mini is one of OpenAI's models. Put "openai/
What it can do beyond plain text: it reads images directly, which makes it a real option for invoices, forms and screenshots; it takes files such as PDFs as input; 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; it can return generated images. 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.
Pricing is 865,548 toman per 1M input tokens and 692,439 per 1M output tokens. A 1,000-word round trip on GPT-5 Image Mini lands near 2,025 toman. It supports cached input: repeated context is billed at 86,555 toman per 1M, which matters a lot if your system prompt is long. Each generated image costs around 2.77 toman. 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 Nano Banana (Gemini 2.5 Flash Image): GPT-5 Image Mini works out about 60% more expensive, 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 49 thousand-word requests on GPT-5 Image Mini, and every 1,000 toman is about 494 words of round trip. A job with one million input tokens and one million output tokens comes to 1,557,987 toman in total. Filling this model's 400,000-token window costs 346,219 toman on the input side alone, which is the real reason to keep conversation history short.
Its nearest relative in the catalogue is "openai/
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 logprobs, top_logprobs, verbosity — all through the standard request body. It does not support tool_choice, tools, 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.
Good fits: product chatbots and internal assistants, where cost and quality have to balance, logic puzzles and code review, pulling text and fields out of images, extracting data against a fixed schema, analysing a long document or codebase in one request.
GPT-5 Image Mini combines OpenAI's advanced language capabilities, powered by GPT-5 Mini, with GPT Image 1 Mini for efficient image generation. This natively multimodal model features superior instruction following, text...
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 | 1,575 toman |
| Summarising a ten-page document | 4,000 in + 600 out | 3,878 toman |
| Classifying a thousand short rows | 120,000 in + 20,000 out | 117,715 toman |
Frequently asked
- How do I call GPT-5 Image 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-image-mini". Nothing else in your code changes. - What does GPT-5 Image Mini cost in toman?
- 865,548 toman per 1M input tokens and 692,439 toman per 1M output tokens; a 1,000-word request is around 2,025 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
- How much input does GPT-5 Image Mini take?
- Up to 400,000 tokens per request, roughly 300k words. A single answer can reach 128,000 tokens.
- Does GPT-5 Image Mini support streaming and image input?
- Streaming (stream=true) works on every model here. Image input is accepted through image_url, as a data URI or a public URL. Structured output through response_format works too.