How do I call Gemini 4 Argon 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 "google/gemini-4-argon". Nothing else in your code changes.

What does Gemini 4 Argon cost in toman?

692,439 toman per 1M input tokens and 3,462,194 toman per 1M output tokens; a 1,000-word request is around 5,401 toman. You pay for the usage that request actually reported, and a failed request costs nothing.

How much input does Gemini 4 Argon take?

Up to 1,048,576 tokens per request, roughly 786k words. A single answer can reach 943,718 tokens.

Does Gemini 4 Argon 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.

Gemini 4 Argon API: toman pricing and code

google/gemini-4-argon

Image inputTool callingReasoningJSON outputFile inputAudio input

Create a key Try it

Input · per 1M tokens

692,439 toman

Output · per 1M tokens

3,462,194 toman

One 1,000-word request ≈

5,401 toman

You are billed for the usage the request actually reported. Prices follow the market. Cached input: 34,622 toman per 1M tokens. Reasoning tokens: 3,462,194 toman per 1M tokens. Per input image: 0.69 toman. Web search: 4,847.07 toman per request.Pricing and top-ups

Gemini 4 Argon 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": "google/gemini-4-argon",
    "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="google/gemini-4-argon",
    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: "google/gemini-4-argon",
  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 Gemini 4 Argon good for?

Gemini 4 Argon is one of Google's models. Put "google/gemini-4-argon" in the model field and the rest of your code stays as it is. Gemini 4 Argon keeps 1,048,576 tokens in view at once, and that number is what caps the conversation history in your product. A single response can run to 943,718 tokens. On price it sits in the "mid-range" band — cheaper than 53 and dearer than 319 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 accepts audio 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 692,439 toman per 1M tokens and output at 3,462,194 — output costs 5× input, so trimming the answer saves more than trimming the prompt. It supports cached input: repeated context is billed at 34,622 toman per 1M, which matters a lot if your system prompt is long. Each input image is billed separately at about 0.69 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 Claude Opus 5.5 (batch), and Gemini 4 Argon 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 19 thousand-word requests on Gemini 4 Argon, and every 1,000 toman is about 185 words of round trip. A job with one million input tokens and one million output tokens comes to 4,154,632 toman in total. Filling this model's 1,048,576-token window costs 726,075 toman on the input side alone, which is the real reason to keep conversation history short.

This id gets confused with "google/gemini-4-argon: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 (5,401 against 2,701 toman).

Google has 43 models in our catalogue; the cheapest is Gemma 4 26B A4B at 67 toman per thousand words and the dearest Gemini 3.1 Pro Preview Custom Tools at 6,301. Among the less common parameters it accepts reasoning_effort — all through the standard request body. By context size the nearest option from another provider is DeepSeek V4 Flash 0423 at 1,048,576 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

Gemini 4 Argon is Google's flagship reasoning model, built for complex software engineering, deep mathematical and scientific problem solving, and autonomous multi-step agents. Google trained it to keep context across...

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	2,424 toman
Summarising a ten-page document	4,000 in + 600 out	4,847 toman
Classifying a thousand short rows	120,000 in + 20,000 out	152,337 toman

Job

Tokens

Cost

One chat turn with a medium history

1,500 in + 400 out

2,424 toman

Summarising a ten-page document

4,000 in + 600 out

4,847 toman

Classifying a thousand short rows

120,000 in + 20,000 out

152,337 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	google/gemini-4-argon:batch	1,731,097	1,048,576

Variant

Model id

Output / 1M

Context

batch

google/gemini-4-argon:batch

1,731,097

1,048,576

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

Frequently asked

How do I call Gemini 4 Argon 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 "google/gemini-4-argon". Nothing else in your code changes.
What does Gemini 4 Argon cost in toman?: 692,439 toman per 1M input tokens and 3,462,194 toman per 1M output tokens; a 1,000-word request is around 5,401 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does Gemini 4 Argon take?: Up to 1,048,576 tokens per request, roughly 786k words. A single answer can reach 943,718 tokens.
Does Gemini 4 Argon 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.