Retrieve a complete list of all available AI models
https://api.genvrresearch.com/api/modelscurl -X GET https://api.genvrresearch.com/api/modelsconst response = await fetch('https://api.genvrresearch.com/api/models');
const data = await response.json();
console.log(`Found ${data.count} models`);
data.data.forEach(model => {
console.log(`${model.name} - ${model.category}/${model.subcategory}`);
});import requests
response = requests.get('https://api.genvrresearch.com/api/models')
data = response.json()
print(f"Found {data['count']} models")
for model in data['data']:
print(f"{model['name']} - {model['category']}/{model['subcategory']}"){
"success": true,
"count": 308,
"data": [
{
"name": "Flux Dev",
"category": "imagegen",
"subcategory": "flux_dev",
"description": "High-quality image generation",
"endpoint": {
"generate": "/api/v1/generate",
"status": "/api/v1/status",
"response": "/api/v1/response"
},
"documentation": "https://api.genvrresearch.com/imagegen/flux_dev",
"preview": "https://app.genvrresearch.com/imagegen/flux_dev"
},
// ... 307 more models
],
"message": "Models retrieved successfully"
}| Field | Type | Description |
|---|---|---|
| success | boolean | Request success status |
| count | integer | Total number of models |
| data | array | Array of model objects |
| data[].name | string | Model display name |
| data[].category | string | Model category identifier |
| data[].subcategory | string | Model subcategory identifier |
| data[].description | string | Model description |
| data[].documentation | string | Link to full documentation |
| data[].preview | string | Link to live demo |
Dynamically populate dropdowns or selection menus with available models.
Allow users to browse and discover new models programmatically.
Automatically generate documentation pages for each available model.
Fetch model list to test integrations across all available models.