Edit images with OpenAI GPT Image 2 Edit API. Use prompt-guided changes, multi-image input, flexible quality controls, and stable creative output.
Try the AI Image Generator now
GPT Image 2 Edit API from OpenAI delivers cost-effective, production-grade AI image editing for developers and creative teams. This professional image editing API integration helps you transform existing visuals through natural-language instructions while preserving scene coherence, style consistency, and design intent. Built on OpenAI's latest GPT image editing stack, GPT Image 2 Edit combines semantic multimodal reasoning with flexible quality controls and multi-image input support for scalable editing workflows on Best Image AI.
Note Please ensure your prompts comply with OpenAI's usage policies. If an error occurs, review your prompt for restricted content, adjust it, and try again.
GPT Image 2 Edit vs. GPT Image 2 Edit Client
GPT Image 2 Edit Client offers the same core image editing capabilities with a very affordable access path for teams optimizing around cost efficiency. GPT Image 2 Edit maintains the same strong semantic editing, multi-image workflow support, and production-ready editing fit, making both variants suitable for professional editing pipelines depending on budget priorities.
GPT Image 2 Edit vs. Runway Gen-4 References
Runway Gen-4 References is strong for cinematic, reference-driven visual workflows. GPT Image 2 Edit API differentiates with flexible multi-image editing, dependable instruction following, and efficient API-based editing for practical production use.
GPT Image 2 Edit vs. Stable Image Edit
Stable Image Edit offers customization flexibility and open ecosystem appeal. GPT Image 2 Edit provides lower-friction API integration, polished semantic editing behavior, and strong out-of-the-box quality for teams that want reliable editing without infrastructure overhead.
GPT Image 2 Edit vs. Qwen Image 2.0 Edit
Qwen Image 2.0 Edit is a strong value-oriented editing model with multilingual strengths. GPT Image 2 Edit API stands out with OpenAI's refined instruction following, robust multi-image workflow support, and dependable design-oriented editing control.
GPT Image 2 Edit vs. Nano Banana 2 Edit
Nano Banana 2 Edit emphasizes Gemini Flash speed and affordable editing throughput. GPT Image 2 Edit focuses on polished semantic editing, flexible multi-image composition, and strong OpenAI workflow integration for professional editing pipelines.
// 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: 'gpt-image-2-edit',
width: 1,
height: 1,
resolution: '1k',
prompt: 'Change the jacket color to deep navy, keep lighting consistent',
quality: 'medium',
image_url_list: ['https://example.com/source.jpg']
})
});
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[].);
;
}
(status === ) {
.(pollResultData..);
;
}
( (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': 'gpt-image-2-edit',
'width': 1,
'height': 1,
'resolution': '1k',
'prompt': 'Change the jacket color to deep navy, keep lighting consistent',
'quality': 'medium',
'image_url_list': ['https://example.com/source.jpg']
}
)
task_id = response.json()['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": "gpt-image-2-edit",
"width": 1,
"height": 1,
"resolution": "1k",
"prompt": "Change the jacket color to deep navy, keep lighting consistent",
"quality": "medium",
"image_url_list": ["https://example.com/source.jpg"]
}'
# 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)