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Gemini 2.5 Pro API: toman pricing and code

google/gemini-2.5-pro

Image inputTool callingReasoningJSON outputFile inputAudio input
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Input · per 1M tokens
432,774 toman
Output · per 1M tokens
3,462,194 toman
One 1,000-word request ≈
5,063 toman

You are billed for the usage the request actually reported. Prices follow the market. Cached input: 43,277 toman per 1M tokens. Reasoning tokens: 3,462,194 toman per 1M tokens. Per input image: 0.43 toman. Web search: 4,847.07 toman per request.Pricing and top-ups

Gemini 2.5 Pro 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-2.5-pro",
    "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-2.5-pro",
    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-2.5-pro",
  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 2.5 Pro good for?

Gemini 2.5 Pro comes from Google; in uttapen you reach it with the model id "google/gemini-2.5-pro". It accepts up to 1,048,576 tokens of input per call, so a long document or several source files fit in a single request. A single response can run to 65,536 tokens. On price it sits in the "expensive" band — cheaper than 52 and dearer than 320 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 432,774 toman per 1M tokens and output at 3,462,194 — output costs 8× input, so trimming the answer saves more than trimming the prompt. It supports cached input: repeated context is billed at 43,277 toman per 1M, which matters a lot if your system prompt is long. Each input image is billed separately at about 0.43 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): Gemini 2.5 Pro works out about 9% cheaper, 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 20 thousand-word requests on Gemini 2.5 Pro, and every 1,000 toman is about 198 words of round trip. A job with one million input tokens and one million output tokens comes to 3,894,968 toman in total. Filling this model's 1,048,576-token window costs 453,797 toman on the input side alone, which is the real reason to keep conversation history short.

Its nearest relative in the catalogue is "google/gemini-2.5-pro-preview", and choosing between those two is where most people hesitate. Both land at nearly the same price on a thousand-word request (5,063 and 5,063 toman). On parameters the other takes stop.

Google has 43 models in our catalogue; the cheapest is Gemma 4 26B A4B at 45 toman per thousand words and the dearest Gemini 3.1 Pro Preview Custom Tools at 6,301. By context size the nearest option from another provider is DeepSeek V4 Flash 0423 at 1,048,576 tokens.

Where it makes sense: work where the quality of the answer matters more than its cost, 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 2.5 Pro is Google’s state-of-the-art AI model designed for advanced reasoning, coding, mathematics, and scientific tasks. It employs “thinking” capabilities, enabling it to reason through responses with enhanced accuracy...

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 out2,034 toman
Summarising a ten-page document4,000 in + 600 out3,808 toman
Classifying a thousand short rows120,000 in + 20,000 out121,177 toman

Frequently asked

How do I call Gemini 2.5 Pro 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-2.5-pro". Nothing else in your code changes.
What does Gemini 2.5 Pro cost in toman?
432,774 toman per 1M input tokens and 3,462,194 toman per 1M output tokens; a 1,000-word request is around 5,063 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does Gemini 2.5 Pro take?
Up to 1,048,576 tokens per request, roughly 786k words. A single answer can reach 65,536 tokens.
Does Gemini 2.5 Pro 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.