Google's Nano Banana Pro (Gemini 3.0 Pro Image) Edit is a next-generation image editing model capable of generating multiple high-quality edited images in a single run. Extremely low cost — only $0.07 per image. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.
Idle


$0.07per run·~14 / $1

Replace the man in the first image with the woman from the second image, matching his pose and perspective. Keep the original background intact, and blend the woman naturally into the scene with consistent lighting, shadows, and overall photorealistic style.

Photo-realistic edit of the reference photo. Keep the original West Sea Grand Canyon background, stone path, railings, autumn trees and tourists in the distance. Replace the elderly man on the bench with a handsome young male celebrity-looking man in his late 20s, stylish haircut, clear skin, natural smile, casual but fashionable outdoor outfit (light jacket, shirt, jeans, sneakers), holding a backpack beside him in a relaxed pose. Maintain the same camera angle and composition, soft daylight, realistic colors, high resolution travel photography style.

Replace the necklace in Figure 2 with that in Figure 1

Photo-realistic edits of the reference couple portrait. Keep the same man and woman, faces, expressions, pose, camera angle and framing. Generate TWO versions of the image: Summer version: change the background to a bright summer scene with lush green trees and clear blue sky. No red autumn leaves. Dress the woman in a light summer outfit (short-sleeved blouse or thin dress) and the man in a T-shirt or light shirt without a jacket. Warm daylight, fresh colors, relaxed vacation atmosphere. Winter version: change the background to a cold winter scene with bare trees, snow on the ground and rooftops, and a pale winter sky. Dress the woman in a thick wool coat, scarf and gloves, and the man in a padded winter jacket with visible layers. Cooler color temperature, soft winter daylight. For both versions, keep the couple’s identity and pose consistent, and blend them naturally into the new background with realistic lighting and shadows.

Stylized edits of the reference portrait photo. Keep the same man, face, expression, hairstyle, pose, camera angle and café background. Generate TWO versions in different art styles: A soft painterly oil-portrait style with a warm color palette, visible brushstrokes, gentle lighting and a slightly textured canvas look, like a classic indoor painting. A clean modern 2D cartoon / anime illustration style with simplified shapes, smooth line art, flat shading with subtle gradients, brighter colors and slightly exaggerated facial features. For both versions, preserve the man’s likeness and overall framing while completely changing the visual rendering style.
Nano Banana Pro Edit Multi (Gemini 3.0 Pro Image) is Google's next-generation multi-image editing model. Instead of generating a single edited image, this endpoint allows you to upload one or more input images and produce multiple edited outputs in one run.
On WaveSpeedAI, Edit Multi delivers exceptional scale efficiency at a flat $0.07 per image, making it the most cost-effective multi-edit pipeline for design, creative production, and batch asset updates.
Generate several edited versions of your uploaded image(s) in a single request using num_images—no loops, no repeated API calls.
All variants follow the same instruction but differ naturally in composition, lighting, pose, or mood—ideal for A/B testing and creative exploration.
Pay only $0.07 per edited image, regardless of batch size. Perfect for workflows needing dozens or hundreds of variations.
Handles object replacement, style changes, background editing, lighting adjustments, composition tweaks, and more.
Powered by WaveSpeedAI’s optimized runtime for low latency and consistent performance.
Use Google Nano Banana Pro Edit Multi when:
Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/google/nano-banana-pro/edit-multi with your input as JSON. The endpoint returns a prediction id. Start polling the result endpoint around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. On completed, read output values from data.outputs. Examples for Nano Banana Pro Edit Multi below.
set -euo pipefail
: "${WAVESPEED_API_KEY:?Set WAVESPEED_API_KEY}"
REQUEST_BODY=$(cat <<'JSON'
{
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"images": [
"https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg"
],
"aspect_ratio": "3:2",
"num_images": 2,
"output_format": "png"
}
JSON
)
# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
-X POST "https://api.wavespeed.ai/api/v3/google/nano-banana-pro/edit-multi" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $WAVESPEED_API_KEY" \
-d "$REQUEST_BODY")
TASK=$(printf '%s' "$SUBMIT_RESPONSE" | jq 'if has("data") then .data else . end')
PREDICTION_ID=$(printf '%s' "$TASK" | jq -r '.id')
if [ -z "$PREDICTION_ID" ] || [ "$PREDICTION_ID" = "null" ]; then
printf 'Submission response did not contain a prediction id
' >&2
exit 1
fi
RESULT_URL=$(printf '%s' "$TASK" | jq -r '.urls.get // empty')
if [ -z "$RESULT_URL" ]; then
RESULT_URL="https://api.wavespeed.ai/api/v3/predictions/$PREDICTION_ID/result"
fi
# 2. Poll until the prediction finishes.
while true; do
RESPONSE=$(curl --silent --show-error --fail-with-body "$RESULT_URL" \
-H "Authorization: Bearer $WAVESPEED_API_KEY")
RESULT=$(printf '%s' "$RESPONSE" | jq 'if has("data") then .data else . end')
STATUS=$(printf '%s' "$RESULT" | jq -r '.status')
case "$STATUS" in
completed) printf '%s\n' "$RESULT" | jq '.outputs'; break ;;
failed|cancelled|timeout) printf '%s\n' "$RESULT" | jq . >&2; exit 1 ;;
created|processing) sleep 2 ;;
*) printf 'Unexpected status: %s
' "$STATUS" >&2; exit 1 ;;
esac
doneconst submitUrl = "https://api.wavespeed.ai/api/v3/google/nano-banana-pro/edit-multi";
const apiKey = process.env.WAVESPEED_API_KEY;
if (!apiKey) throw new Error('Set WAVESPEED_API_KEY');
async function requestJson(url, options = {}) {
const response = await fetch(url, options);
if (!response.ok) throw new Error(await response.text());
return response.json();
}
// 1. Submit the prediction.
const body = await requestJson(submitUrl, {
method: "POST",
headers: {
"Authorization": `Bearer ${apiKey}`,
"Content-Type": "application/json",
},
body: JSON.stringify({
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"images": [
"https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg"
],
"aspect_ratio": "3:2",
"num_images": 2,
"output_format": "png"
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
`https://api.wavespeed.ai/api/v3/predictions/${task.id}/result`;
// 2. Poll until the prediction finishes.
while (true) {
const resultBody = await requestJson(resultUrl, {
headers: { "Authorization": `Bearer ${apiKey}` },
});
const result = resultBody.data ?? resultBody;
if (result.status === "completed") {
console.log(result.outputs);
break;
}
if (["failed", "cancelled", "timeout"].includes(result.status)) throw new Error(JSON.stringify(result));
if (!["created", "processing"].includes(result.status)) throw new Error("Unexpected status: " + result.status);
await new Promise(resolve => setTimeout(resolve, 2000));
}import json
import os
import time
from urllib.request import Request, urlopen
api_key = os.environ["WAVESPEED_API_KEY"]
headers = {"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"}
payload = {
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"images": [
"https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg"
],
"aspect_ratio": "3:2",
"num_images": 2,
"output_format": "png"
}
def request_json(url, data=None):
request = Request(url, data=data, headers=headers, method="POST" if data else "GET")
with urlopen(request) as response:
return json.load(response)
# 1. Submit the prediction.
body = request_json("https://api.wavespeed.ai/api/v3/google/nano-banana-pro/edit-multi", json.dumps(payload).encode())
task = body.get("data", body)
if not task.get("id"):
raise RuntimeError("Submission response did not contain a prediction id")
result_url = task.get("urls", {}).get("get") or f"https://api.wavespeed.ai/api/v3/predictions/{task['id']}/result"
# 2. Poll until the prediction finishes.
while True:
result_body = request_json(result_url)
result = result_body.get("data", result_body)
status = result.get("status")
if status == "completed":
print(result.get("outputs", []))
break
if status in {"failed", "cancelled", "timeout"}:
raise RuntimeError(result)
if status not in {"created", "processing"}:
raise RuntimeError(f"Unexpected status: {status}")
time.sleep(2)Nano Banana Pro Edit Multi is a Google model for image editing, exposed as a REST API on WaveSpeedAI. Google's Nano Banana Pro (Gemini 3.0 Pro Image) Edit is a next-generation image editing model capable of generating multiple high-quality edited images in a single run. Extremely low cost — only $0.07 per image. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing. You can call it programmatically or try it from the playground above.
POST your input parameters to the model's REST endpoint (shown in the API tab of this playground) with your WaveSpeedAI API key in the Authorization header. Submission returns a prediction ID. Poll the result endpoint starting around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. The playground generates production-oriented Python, JavaScript, and cURL examples with timeouts, transient-error handling, and safe GET retries. Full request/response shape is documented at https://wavespeed.ai/docs/docs-api/google/google-nano-banana-pro-edit-multi.
Nano Banana Pro Edit Multi starts at $0.070 per run. That figure is the base price — the final charge scales with the parameters you set in the form (output size, length, count, references, or whatever knobs this model exposes), so a higher-quality or larger output costs more than a minimal one. The exact cost for your current input is shown live next to the Generate button before you submit, and the actual per-call charge is recorded on the prediction afterwards.
Key inputs: `prompt`, `images`, `aspect_ratio`, `enable_base64_output`, `enable_sync_mode`, `num_images`. The full JSON schema (types, defaults, allowed values) is rendered above the Generate button and mirrored in the API reference at https://wavespeed.ai/docs/docs-api/google/google-nano-banana-pro-edit-multi.
Sign up for a free WaveSpeedAI account to claim starter credits, copy your API key from /accesskey, then call the endpoint shown in the API tab of the playground. The playground also auto-generates a code sample in Python, JavaScript, or cURL for the parameters you've set.
Commercial usage rights depend on the model's license, set by its provider (Google). The license summary appears on the model card above; see WaveSpeedAI's Terms of Service for platform-level conditions.