Google's Gemini 3.0 Pro (Gemini 3.0 Pro Image) Edit enables image editing and text-to-image generation with 4K-capable output for mobile devices. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.
Idle

$0.14per run·~71 / $10

Change the logo on Spider-Man's chest to the img 2 logo. And keep it white and adapt for his suit.

Change the manga style to Classic American comic book style and change the word to English

Color this page of manga.

Change the drink to image 2 and the hat in image 3 and sun glasses in image 4

Make the photo look like a newspaper front page headline.
Gemini 3.0 Pro Image Edit (Gemini 3.0 Pro Image) is Google’s advanced AI-powered image editing and generation model, designed to make visual transformation as intuitive as describing it in words. Built on Google’s cutting-edge computer vision and generative research, it combines precision, flexibility, and semantic awareness for professional-grade editing.
Input: existing image + text prompt
Output: edited image (JPEG/PNG)
Size: 1:1, 4:3, 16:9, 21:9, and so on.
Supports style transfer, relighting, background replacement, and object modification
Works with natural prompts like:
“Replace the cloudy sky with a clear sunset.”
“Add soft studio lighting and a modern background.”
“Turn the model’s outfit into a formal business suit.”
| Resolution | Cost per image |
|---|---|
| 1k | $0.14 |
| 2k | $0.14 |
| 4k | $0.24 |
Please ensure your prompts comply with Google’s Safety Guidelines. If an error occurs, review your prompt for restricted content, adjust it, and try again.
Compare gemini-3-pro-image Edit with:
FLUX.1 [dev] – Nano Banana Pro Edit focuses on semantic understanding and layout-aware editing via Gemini 3’s reasoning, making it ideal for complex, text-driven transformations without manual masking. FLUX.1 [dev] emphasizes maximum resolution control and fine detail preservation for highly technical workflows.
Stable Diffusion 3.5 – Nano Banana Pro Edit leverages Google’s production-scale multimodal training and advanced reasoning for context-aware edits that understand object relationships and composition. Stable Diffusion 3.5 offers open-source flexibility for custom fine-tuning and local deployment in highly specialized pipelines.
Gpt-Image-1 (OpenAI) – Nano Banana Pro Edit emphasizes layout control, multilingual on-image text, and tightly directed edits for design and marketing workflows, while openai/gpt-image-1 shines as a general-purpose creative generator with strong style variety and fast, natural-language image synthesis for broad consumer and developer use.
Original Nano Banana (Gemini 2.5 Flash Image) – Nano Banana Pro Edit trades pure speed for quality, delivering better reasoning, sharper text, improved character consistency, and richer camera controls at a higher unit cost. Original Nano Banana remains ideal for rapid, low-latency iterations and lightweight edits.
Seedream – Nano Banana Pro Edit is tuned for reliable typography, photo-real edits, and mixed media layouts, while SeeDream excels at fast, stylized T2I generation with strong anime and illustration aesthetics, making it a good choice for heavily stylized concept art.
Qwen Image 2509 – Nano Banana Pro Edit focuses on high-fidelity 4K outputs and multilingual on-image design control, whereas Qwen Image shines in open-source ecosystems and document-style rendering, offering flexible integration for developer-centric and research workflows.
Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/google/gemini-3-pro-image/edit 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 Gemini 3 Pro Image Edit below.
set -euo pipefail
: "${WAVESPEED_API_KEY:?Set WAVESPEED_API_KEY}"
REQUEST_BODY=$(cat <<'JSON'
{
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"images": [
"https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg"
],
"aspect_ratio": "1:1",
"resolution": "1k",
"output_format": "png"
}
JSON
)
# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
-X POST "https://api.wavespeed.ai/api/v3/google/gemini-3-pro-image/edit" \
-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/gemini-3-pro-image/edit";
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 ${apiKey}`,
"Content-Type": "application/json",
},
body: JSON.stringify({
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"images": [
"https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg"
],
"aspect_ratio": "1:1",
"resolution": "1k",
"output_format": "png"
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
`https://api.wavespeed.ai/api/v3/predictions/${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": "1:1",
"resolution": "1k",
"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/gemini-3-pro-image/edit", 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)Gemini 3 Pro Image Edit is a Google model for image editing, exposed as a REST API on WaveSpeedAI. Google's Gemini 3.0 Pro (Gemini 3.0 Pro Image) Edit enables image editing and text-to-image generation with 4K-capable output for mobile devices. 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-gemini-3-pro-image-edit.
Gemini 3 Pro Image Edit starts at $0.14 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`, `resolution`, `enable_base64_output`, `enable_sync_mode`. 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-gemini-3-pro-image-edit.
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.