Nano Banana (Gemini 2.5 Flash Image) API: toman pricing and code
google/gemini-2.5-flash-image
You are billed for the usage the request actually reported. Prices follow the market. Cached input: 10,387 toman per 1M tokens. Reasoning tokens: 865,548 toman per 1M tokens. Per input image: 0.10 toman. Per generated image: 10.39 toman. Web search: 4,847.07 toman per request.Pricing and top-ups
Nano Banana (Gemini 2.5 Flash 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-2.5-flash-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-2.5-flash-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-2.5-flash-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 (Gemini 2.5 Flash Image) good for?
Nano Banana (Gemini 2.5 Flash Image) is one of Google's models. Put "google/
Capabilities available on this id: it reads images directly, which makes it a real option for invoices, forms and screenshots; 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.
For a back-of-envelope figure: about 1,260 toman per 1,000-word exchange (103,866 in, 865,548 out, per 1M tokens). It supports cached input: repeated context is billed at 10,387 toman per 1M, which matters a lot if your system prompt is long. Each input image is billed separately at about 0.10 toman. Each generated image costs around 10.39 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 Moondream 3 Preview, and Nano Banana (Gemini 2.5 Flash Image) 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 79 thousand-word requests on Nano Banana (Gemini 2.5 Flash Image), and every 1,000 toman is about 794 words of round trip. A job with one million input tokens and one million output tokens comes to 969,414 toman in total. Filling this model's 32,768-token window costs 3,403 toman on the input side alone, which is the real reason to keep conversation history short.
Its nearest relative in the catalogue is "google/
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. It does not support include_reasoning, reasoning, tool_choice, tools, which most models here do, so test before switching if your code relies on them. By context size the nearest option from another provider is Aion-RP 1.0 (8B) at 32,768 tokens.
What to use it for: product chatbots and internal assistants, where cost and quality have to balance, logic puzzles and code review, pulling text and fields out of images, extracting data against a fixed schema.
Gemini 2.5 Flash Image, a.k.a. "Nano Banana," is now generally available. It is a state of the art image generation model with contextual understanding. It is capable of image generation,...
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 | 502 toman |
| Summarising a ten-page document | 4,000 in + 600 out | 935 toman |
| Classifying a thousand short rows | 120,000 in + 20,000 out | 29,775 toman |
Frequently asked
- How do I call Nano Banana (Gemini 2.5 Flash 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-2.5-flash-image". Nothing else in your code changes. - What does Nano Banana (Gemini 2.5 Flash Image) cost in toman?
- 103,866 toman per 1M input tokens and 865,548 toman per 1M output tokens; a 1,000-word request is around 1,260 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
- How much input does Nano Banana (Gemini 2.5 Flash Image) take?
- Up to 32,768 tokens per request, roughly 25k words. A single answer can reach 8,192 tokens.
- Does Nano Banana (Gemini 2.5 Flash Image) support streaming and image input?
- Streaming (stream=true) works on every model here. Image input is accepted through image_url, as a data URI or a public URL. Structured output through response_format works too.