Use Google Nano Banana Pro API for high-fidelity image generation with Gemini 3.0 Pro, strong prompt accuracy, and native 2K/4K output.
Try the AI Image Generator now
Google Nano Banana Pro API (powered by Gemini 3.0 Pro Image) delivers cost-effective, production-grade AI image generation for developers. This affordable Gemini image API integration enables you to transform complex creative visions into high-resolution output through intuitive text-to-image generation. Built on Google’s cutting-edge computer vision research, it combines deep semantic reasoning with flexible API integration for professional-grade output.
Note 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.
Nano Banana Pro vs. FLUX.1 [dev]
While FLUX.1 [dev] emphasizes maximum resolution control and fine detail preservation for technical workflows, Nano Banana Pro focuses on semantic understanding and layout-aware creation. Powered by Gemini 3’s reasoning, it delivers cost-effective text-to-image generation ideal for complex, text-driven visual storytelling.
Nano Banana Pro vs. GPT-Image-1 (OpenAI)
GPT-Image-1 shines as a general-purpose creative generator with broad style variety. In contrast, Nano Banana Pro emphasizes precise layout control, multilingual on-image text, and tightly directed output, making it the superior choice for affordable, production-grade professional design and marketing.
Nano Banana Pro vs. Original Nano Banana
The original Nano Banana remains the go-to for rapid, low-latency iterations. Nano Banana Pro trades pure speed for premium quality, delivering significantly better reasoning, sharper text, improved character consistency, and richer camera-style controls through cost-effective Gemini 3 Pro Image integration.
Nano Banana Pro vs. Seedream
Seedream excels at fast, stylized generation with strong anime and illustration aesthetics. Nano Banana Pro is specifically tuned for reliable typography, photo-realism, and professional mixed-media layouts with 4K output quality.
Nano Banana Pro vs. Qwen Image 2509
Qwen Image is a strong contender in open-source ecosystems and document-style rendering. Nano Banana Pro distinguishes itself by focusing on high-fidelity 4K outputs and sophisticated multilingual design control for global productions through production-grade Gemini API integration.
// Step 1: Submit generation request
const response = await fetch('https://api.flaq.ai/api/v1/image/task', {
method: 'POST',
headers: {
'Content-Type': 'application/json',
'Authorization': 'Bearer YOUR_API_KEY'
},
body: JSON.stringify({
model_name: 'nano-banana-pro',
prompt: 'A beautiful sunset over mountains',
width: 16,
height: 9,
resolution: '2k'
})
});
const { data } = await response.json();
const taskId = data.task_id;
// Step 2: Poll for results
const taskId = data.task_id;
const pollResult = async (taskId) => {
const res = await fetch(`https://api.flaq.ai/api/v1/image/${taskId}`, {
headers: { 'Authorization': 'Bearer YOUR_API_KEY' }
});
return res.json();
};
while (true) {
const pollResultData = await pollResult(taskId);
const status = pollResultData.data.task_status;
if (status === 'succeed') {
console.log(pollResultData.data.task_result.images[0].url);
break;
}
if (status === 'failed') {
console.error(pollResultData.data.task_status_msg);
break;
}
await new Promise(resolve => setTimeout(resolve, ));
}
# Step 1: Submit generation request
import requests
response = requests.post(
'https://api.flaq.ai/api/v1/image/task',
headers={
'Content-Type': 'application/json',
'Authorization': 'Bearer YOUR_API_KEY'
},
json={
'model_name': 'nano-banana-pro',
'prompt': 'A beautiful sunset over mountains',
'width': 16,
'height': 9,
'resolution': '2k'
}
)
result = response.json()
task_id = result['data']['task_id']
# Step 1: Submit generation request
curl -X POST https://api.flaq.ai/api/v1/image/task \
-H "Content-Type: application/json" \
-H "Authorization: Bearer YOUR_API_KEY" \
-d '{
"model_name": "nano-banana-pro",
"prompt": "A beautiful sunset over mountains",
"width": 16,
"height": 9,
"resolution": "2k"
}'
# Step 2: Poll for results
# Replace {task_id} with the task_id returned from the submit response
curl -X GET "https://api.flaq.ai/api/v1/image/{task_id}" \
-H "Authorization: Bearer YOUR_API_KEY"
# Step 2: Poll for results
task_id = response.json()['data']['task_id']
poll_url = f"https://api.flaq.ai/api/v1/image/{task_id}"
while True:
poll_result = requests.get(poll_url, headers={'Authorization': 'Bearer YOUR_API_KEY'}).json()
status = poll_result['data']['task_status']
if status == 'succeed':
print(poll_result['data']['task_result']['images'][0]['url'])
break
if status == 'failed':
print(poll_result['data']['task_status_msg'])
break
time.sleep(10)