Text to Image
Create AI images from text prompts
Alibaba/qwen-image-3.0-pro
qwen-image-3.0-pro
Free to try the Qwen Image 3.0 Pro API, an AI model for knowledge-rich image generation, dense layouts, fine multilingual text, realistic detail, and creative production.
Text to Image
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Qwen Image 3.0 Pro Pricing
| Parameters | Price | Original Price | Discount |
|---|
Examples
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Professional Qwen Image 3.0 Pro API (Alibaba's Advanced Image Generation)
Qwen Image 3.0 Pro API turns natural-language prompts into polished images for creative, commercial, editorial, and product workflows. The model provides a text-to-image generation path with flexible aspect-ratio controls and seed support, while Flaq AI provides an application-ready interface for repeatable image production.
Key Features of Qwen Image 3.0 Pro API
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Prompt-Based Image Generation: Translate detailed descriptions of subjects, composition, lighting, materials, and style into finished visuals.
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Strong Composition Control: Use structured prompts to define relationships between objects, environments, color systems, and visual hierarchy.
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Flexible Creative Styles: Explore photorealistic, editorial, illustrative, product, and cinematic directions from the same image generation workflow.
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Aspect-Ratio Flexibility: Create images for landscape, portrait, square, editorial, social, and campaign placements through the available ratio settings.
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Seed-Based Reproducibility: Use seed control when a workflow needs repeatable experimentation or controlled visual variations.
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Production-Ready Image Workflow: Generate polished assets through an API-oriented process that fits review, storage, and publishing pipelines.
How to Use Qwen Image 3.0 Pro API for Professional Image Generation on Flaq AI
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Input: A natural-language prompt describing the subject, composition, style, lighting, and required visual details.
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Prompt Structure: Organize the main subject, spatial relationships, camera perspective, materials, and negative requirements clearly.
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Output: Generated images delivered through the Flaq AI image workflow for review, download, or downstream design work.
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Creative Controls: Select the available aspect-ratio and seed settings according to the target layout and iteration strategy.
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Production Handling: Review typography, anatomy, object relationships, and visual artifacts before publishing generated assets.
Best Use Cases for Qwen Image 3.0 Pro API Integration
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Advertising and Brand Content: Create campaign concepts, posters, product scenes, and promotional visual directions.
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Editorial and Social Creative: Produce illustrations, article visuals, thumbnails, and platform-specific image variations.
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Product and E-Commerce Imagery: Explore product contexts, lifestyle scenes, and merchandising concepts before final production.
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Concept and Story Development: Turn scripts, mood descriptions, and art direction into visual references for creative teams.
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Design Prototyping: Generate layout, environment, and visual-style explorations for presentations, interfaces, and marketing work.
Note Prompt specificity and seed selection affect reproducibility. Review generated images for visual accuracy, readable details, and suitability for the intended commercial or editorial use.
Qwen Image 3.0 Pro vs. Competitors: Comparative Analysis
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Qwen Image 3.0 Pro vs. Nano Banana Pro: Nano Banana Pro is strong in instruction-following and multimodal image workflows. Qwen Image 3.0 Pro provides an Alibaba-oriented text-to-image path with flexible composition and style exploration.
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Qwen Image 3.0 Pro vs. GPT Image: GPT Image offers broad creative generation and editing. Qwen Image 3.0 Pro is suited to teams that want to build image production around Qwen and Alibaba integrations.
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Qwen Image 3.0 Pro vs. Seedream: Seedream supports fast commercial image generation and editing. Qwen Image 3.0 Pro offers an alternative workflow for prompt-led visual production with seed and ratio controls.
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Qwen Image 3.0 Pro vs. FLUX: FLUX models are popular for high-quality image generation and creative experimentation. Qwen Image 3.0 Pro provides another API-friendly choice for structured prompt-based image creation.
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Qwen Image 3.0 Pro vs. Midjourney: Midjourney emphasizes a community-driven creative interface. Qwen Image 3.0 Pro is designed for developers who need programmatic generation inside product and content pipelines.
Use the Qwen Image 3.0 Pro Model Directly in Powerful AI Tools
Explore several AI creation tools for quick image and video workflows in your browser, then scale successful ideas with Flaq AI's production-ready model APIs. Flaq AI provides a unified API layer for all models, making it easy to use and scale your workflows.
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API Examples
Submit Example
// 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: 'qwen-image-3.0-pro',
prompt: 'A professional product photo with studio lighting',
width: 16,
height: 9,
resolution: '2k',
seed: 42
})
});
const { data } = await response.json();
const taskId = data.task_id;
Polling Example
// 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, 10000));
}
Submit Example
# 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': 'qwen-image-3.0-pro',
'prompt': 'A professional product photo with studio lighting',
'width': 16,
'height': 9,
'resolution': '2k',
'seed': 42
}
)
result = response.json()
task_id = result['data']['task_id']
Polling Example
# 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)
Submit Example
# 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": "qwen-image-3.0-pro",
"prompt": "A professional product photo with studio lighting",
"width": 16,
"height": 9,
"resolution": "2k",
"seed": 42
}'
Polling Example
# 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"