Nano Banana (Gemini 2.5 Flash Image) API: toman pricing and code
google/gemini-2.5-flash-image
visionreasoningjsonimage outYou are billed for the usage the request actually reported. Prices follow the market. Cached input: 8,704 toman / 1M. Reasoning tokens: 725,313 toman / 1M. Per input image: 0.09 toman. Per generated image: 8.70 toman. Web search: 4,061.75 toman / 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/gemini-2.5-flash-image" in the model field and the rest of your code stays as it is. Nano Banana (Gemini 2.5 Flash Image) keeps 32,768 tokens in view at once, and that number is what caps the conversation history in your product. A single response can run to 8,192 tokens. On price it sits in the "mid-range" band — cheaper than 228 and dearer than 178 of the other paid models in the catalogue.
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 1,056 toman per 1,000-word exchange (87,038 in, 725,313 out, per 1M tokens). It supports cached input: repeated context is billed at 8,704 toman per 1M, which matters a lot if your system prompt is long. Each input image is billed separately at about 0.09 toman. Each generated image costs around 8.70 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 Grok 4.20 Multi-Agent: Nano Banana (Gemini 2.5 Flash Image) works out roughly 1.3× cheaper, 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 95 thousand-word requests on Nano Banana (Gemini 2.5 Flash Image), and every 1,000 toman is about 947 words of round trip. A job with one million input tokens and one million output tokens comes to 812,350 toman in total. Filling this model's 32,768-token window costs 2,852 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-3.1-flash-image-preview", and choosing between those two is where most people hesitate. On a thousand-word request this variant works out 1.3× cheaper (1,056 against 1,320 toman). The context windows differ too: 32,768 against 65,536 tokens. Maximum answer length differs as well: 8,192 against 58,982 tokens. On parameters, this one takes stop while the other takes include_reasoning, reasoning, reasoning_effort.
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. 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 | 421 toman |
| Summarising a ten-page document | 4,000 in + 600 out | 783 toman |
| Classifying a thousand short rows | 120,000 in + 20,000 out | 24,951 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?
- 87,038 toman per 1M input tokens and 725,313 toman per 1M output tokens; a 1,000-word request is around 1,056 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.