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Nano Banana Pro (Gemini 3 Pro Image) API: toman pricing and code

google/gemini-3-pro-image

Image inputTool callingReasoningJSON outputImage output
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Input · per 1M tokens
692,439 toman
Output · per 1M tokens
4,154,632 toman
One 1,000-word request ≈
6,301 toman

You are billed for the usage the request actually reported. Prices follow the market. Cached input: 69,244 toman per 1M tokens. Reasoning tokens: 4,154,632 toman per 1M tokens. Per input image: 0.69 toman. Per generated image: 41.55 toman. Web search: 4,847.07 toman per request.Pricing and top-ups

Nano Banana Pro (Gemini 3 Pro Image) 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": "google/gemini-3-pro-image",
    "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="google/gemini-3-pro-image",
    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: "google/gemini-3-pro-image",
  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 Nano Banana Pro (Gemini 3 Pro Image) good for?

The id for Nano Banana Pro (Gemini 3 Pro Image) in our API is "google/gemini-3-pro-image", served from Google. Context is 131,072 tokens; past that you have to summarise the history yourself. A single response can run to 32,768 tokens. On price it sits in the "expensive" band — cheaper than 44 and dearer than 348 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 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; 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 692,439 toman per 1M input tokens and 4,154,632 per 1M output tokens. A 1,000-word round trip on Nano Banana Pro (Gemini 3 Pro Image) lands near 6,301 toman. It supports cached input: repeated context is billed at 69,244 toman per 1M, which matters a lot if your system prompt is long. Each input image is billed separately at about 0.69 toman. Each generated image costs around 41.55 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 GPT-5.6 Terra, and Nano Banana Pro (Gemini 3 Pro Image) costs about the same, and its context window is smaller. 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 16 thousand-word requests on Nano Banana Pro (Gemini 3 Pro Image), and every 1,000 toman is about 159 words of round trip. A job with one million input tokens and one million output tokens comes to 4,847,071 toman in total. Filling this model's 131,072-token window costs 90,759 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-nano-banana-2.1", and choosing between those two is where most people hesitate. On a thousand-word request this variant works out about 60% more expensive (6,301 against 4,051 toman). The context windows differ too: 131,072 against 65,536 tokens. Maximum answer length differs as well: 32,768 against 58,982 tokens. On parameters, this one takes stop while the other takes reasoning_effort.

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. By context size the nearest option from another provider is Aion-2.0 at 131,072 tokens.

Good fits: 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.

Provider's own description

Nano Banana Pro is Google’s most advanced image-generation and editing model, built on Gemini 3 Pro. It extends the original Nano Banana with significantly improved multimodal reasoning, real-world grounding, and...

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 out2,701 toman
Summarising a ten-page document4,000 in + 600 out5,263 toman
Classifying a thousand short rows120,000 in + 20,000 out166,185 toman

Frequently asked

How do I call Nano Banana Pro (Gemini 3 Pro Image) 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-3-pro-image". Nothing else in your code changes.
What does Nano Banana Pro (Gemini 3 Pro Image) cost in toman?
692,439 toman per 1M input tokens and 4,154,632 toman per 1M output tokens; a 1,000-word request is around 6,301 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does Nano Banana Pro (Gemini 3 Pro Image) take?
Up to 131,072 tokens per request, roughly 98k words. A single answer can reach 32,768 tokens.
Does Nano Banana Pro (Gemini 3 Pro Image) 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.