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Gemini 4 Argon (batch) API: toman pricing and code

google/gemini-4-argon:batch

Image inputTool callingReasoningJSON outputFile inputAudio 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: 17,311 toman per 1M tokens. Reasoning tokens: 1,731,097 toman per 1M tokens. Per input image: 0.35 toman. Web search: 4,847.07 toman per request.Pricing and top-ups

Gemini 4 Argon (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": "google/gemini-4-argon: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="google/gemini-4-argon: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: "google/gemini-4-argon: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 Gemini 4 Argon (batch) good for?

Google publishes this model; we expose it under the id "google/gemini-4-argon:batch". Its context window is 1,048,576 tokens, roughly 786k English words in one request. A single response can run to 943,718 tokens. On price it sits in the "mid-range" band — cheaper than 108 and dearer than 280 of the other paid models in the catalogue.

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 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.

Pricing is 346,219 toman per 1M input tokens and 1,731,097 per 1M output tokens. A 1,000-word round trip on Gemini 4 Argon (batch) lands near 2,701 toman. It supports cached input: repeated context is billed at 17,311 toman per 1M, which matters a lot if your system prompt is long. Each input image is billed separately at about 0.35 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 Haiku 4.5, and Gemini 4 Argon (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 Gemini 4 Argon (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 1,048,576-token window costs 363,037 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", 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).

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.

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, 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.

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
standardgoogle/gemini-4-argon3,462,1941,048,576

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 (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 "google/gemini-4-argon:batch". Nothing else in your code changes.
What does Gemini 4 Argon (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 Gemini 4 Argon (batch) take?
Up to 1,048,576 tokens per request, roughly 786k words. A single answer can reach 943,718 tokens.
Does Gemini 4 Argon (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.