Use GPT Image 2 Client API for high-precision image generation with strong prompt accuracy, clean layout control, quality options, and stable output.
This model is currently in preview and may be less stable than standard versions.
Try the AI Image Generator now
GPT Image 2 Client API from OpenAI delivers high-quality, very affordable AI image generation for developers and creative teams. This professional text-to-image API integration helps you turn natural-language prompts into polished visuals with strong instruction following, flexible quality controls, and reliable output consistency. Built on OpenAI's latest GPT image generation stack, GPT Image 2 Client combines semantic prompt understanding with production-ready API integration for scalable creative 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 Client vs. GPT Image 2
GPT Image 2 and GPT Image 2 Client share the same core generation strengths in prompt adherence, text rendering, and production usability. GPT Image 2 Client is positioned as a very affordable option for teams that want the same overall workflow with stronger cost efficiency.
GPT Image 2 Client vs. Runway Gen-4 Image
Runway Gen-4 Image focuses on cinematic creative direction and reference-driven visuals. GPT Image 2 Client API stands out with strong text rendering, dependable instruction following, and a very affordable positioning for scalable text-to-image production.
GPT Image 2 Client vs. Stable Diffusion 3.5
Stable Diffusion 3.5 provides open-model flexibility and deeper customization potential. GPT Image 2 Client provides hassle-free API integration, better out-of-the-box prompt adherence, and a very affordable production path for teams that want production-ready image generation immediately.
GPT Image 2 Client vs. Qwen Image 2.0
Qwen Image 2.0 is strong for multilingual and value-focused image generation. GPT Image 2 Client API differentiates with OpenAI's polished instruction following, reliable text-in-image rendering, and a very affordable professional workflow for global creative teams.
GPT Image 2 Client vs. Nano Banana 2
Nano Banana 2 emphasizes Gemini Flash speed and high-throughput generation. GPT Image 2 Client offers a compelling alternative with strong prompt control, clean typography handling, and very affordable OpenAI API integration for practical production use.
// 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-client',
prompt: 'A minimalist product shot of a ceramic mug on marble, soft studio light',
width: 1,
height: 1,
resolution: '1k',
quality: 'medium'
})
});
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-client',
'prompt': 'A minimalist product shot of a ceramic mug on marble, soft studio light',
'width': 1,
'height': 1,
'resolution': '1k',
'quality': 'medium'
}
)
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-client",
"prompt": "A minimalist product shot of a ceramic mug on marble, soft studio light",
"width": 1,
"height": 1,
"resolution": "1k",
"quality": "medium"
}'
# 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)