Nano Banana Pro (Gemini 3 Pro Image) API: toman pricing and code
google/gemini-3-pro-image
visiontoolsreasoningjsonimage outYou are billed for the usage the request actually reported. Prices follow the market. Cached input: 58,025 toman / 1M. Reasoning tokens: 3,481,500 toman / 1M. Per input image: 0.58 toman. Per generated image: 34.82 toman. Web search: 4,061.75 toman / 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 341 and dearer than 65 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 580,250 toman per 1M input tokens and 3,481,500 per 1M output tokens. A 1,000-word round trip on Nano Banana Pro (Gemini 3 Pro Image) lands near 5,280 toman. It supports cached input: repeated context is billed at 58,025 toman per 1M, which matters a lot if your system prompt is long. Each input image is billed separately at about 0.58 toman. Each generated image costs around 34.82 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: Nano Banana Pro (Gemini 3 Pro Image) works out roughly 1× more expensive, 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 19 thousand-word requests on Nano Banana Pro (Gemini 3 Pro Image), and every 1,000 toman is about 189 words of round trip. A job with one million input tokens and one million output tokens comes to 4,061,750 toman in total. Filling this model's 131,072-token window costs 76,055 toman on the input side alone, which is the real reason to keep conversation history short.
Google has 45 models in our catalogue; the cheapest is Gemma 3 4B at 57 toman per thousand words and the dearest Google Gemini Pro Latest at 5,280. 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.
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.
| Job | Tokens | Cost |
|---|---|---|
| One chat turn with a medium history | 1,500 in + 400 out | 2,263 toman |
| Summarising a ten-page document | 4,000 in + 600 out | 4,410 toman |
| Classifying a thousand short rows | 120,000 in + 20,000 out | 139,260 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?
- 580,250 toman per 1M input tokens and 3,481,500 toman per 1M output tokens; a 1,000-word request is around 5,280 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.