GPT Image 1.5 — Text to Image
curl --request POST \
--url https://api.muvi.video/v1/jobs/submit \
--header 'Authorization: Bearer <token>' \
--header 'Content-Type: application/json' \
--data '
{
"model": "<string>",
"input": {
"prompt": "<string>",
"aspect_ratio": "<string>"
}
}
'import requests
url = "https://api.muvi.video/v1/jobs/submit"
payload = {
"model": "<string>",
"input": {
"prompt": "<string>",
"aspect_ratio": "<string>"
}
}
headers = {
"Authorization": "Bearer <token>",
"Content-Type": "application/json"
}
response = requests.post(url, json=payload, headers=headers)
print(response.text)const options = {
method: 'POST',
headers: {Authorization: 'Bearer <token>', 'Content-Type': 'application/json'},
body: JSON.stringify({model: '<string>', input: {prompt: '<string>', aspect_ratio: '<string>'}})
};
fetch('https://api.muvi.video/v1/jobs/submit', options)
.then(res => res.json())
.then(res => console.log(res))
.catch(err => console.error(err));{
"jobId": "<string>",
"status": "<string>",
"estimatedCompletionTime": "<string>",
"costMicroCents": 123
}GPT Image 1.5
GPT Image 1.5 — Text to Image
Generate images from text prompts using OpenAI’s GPT Image 1.5 model
POST
/
v1
/
jobs
/
submit
GPT Image 1.5 — Text to Image
curl --request POST \
--url https://api.muvi.video/v1/jobs/submit \
--header 'Authorization: Bearer <token>' \
--header 'Content-Type: application/json' \
--data '
{
"model": "<string>",
"input": {
"prompt": "<string>",
"aspect_ratio": "<string>"
}
}
'import requests
url = "https://api.muvi.video/v1/jobs/submit"
payload = {
"model": "<string>",
"input": {
"prompt": "<string>",
"aspect_ratio": "<string>"
}
}
headers = {
"Authorization": "Bearer <token>",
"Content-Type": "application/json"
}
response = requests.post(url, json=payload, headers=headers)
print(response.text)const options = {
method: 'POST',
headers: {Authorization: 'Bearer <token>', 'Content-Type': 'application/json'},
body: JSON.stringify({model: '<string>', input: {prompt: '<string>', aspect_ratio: '<string>'}})
};
fetch('https://api.muvi.video/v1/jobs/submit', options)
.then(res => res.json())
.then(res => console.log(res))
.catch(err => console.error(err));{
"jobId": "<string>",
"status": "<string>",
"estimatedCompletionTime": "<string>",
"costMicroCents": 123
}Generate images from text descriptions with OpenAI’s GPT Image 1.5 model. Supports text-to-image generation with multiple aspect ratios.
| Property | Value |
|---|---|
| Provider | OpenAI |
| Model | GPT Image 1.5 |
| Capability | Text to Image |
| Base Cost | 34,000 micro-cents/request ($0.034) |
| Processing Time | ~30 seconds |
Request Body
string
required
Model slug. Use
openai/gpt-image-1.5/text-to-image for text-to-image generation.object
required
string
HTTPS URL to receive a webhook notification when the job completes or fails.
Pricing
Base cost: 34,000 micro-cents per request ($0.034)Fixed cost: 34,000 micro-cents ($0.034) per request. No dynamic pricing factors.
Response
string
Unique identifier for the submitted job.
string
Initial job status. Always
"pending" on successful submission.string
ISO 8601 timestamp of the estimated completion time.
number
The cost of the job in micro-cents.
Code Examples
curl -X POST https://api.muvi.video/v1/jobs/submit \
-H "Authorization: Bearer $PIXELBYTE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "openai/gpt-image-1.5/text-to-image",
"input": {
"prompt": "A futuristic cityscape at sunset with flying cars",
"aspect_ratio": "3:2"
}
}'
import requests
response = requests.post(
"https://api.muvi.video/v1/jobs/submit",
headers={
"Authorization": "Bearer YOUR_API_KEY",
"Content-Type": "application/json"
},
json={
"model": "openai/gpt-image-1.5/text-to-image",
"input": {
"prompt": "A futuristic cityscape at sunset with flying cars",
"aspect_ratio": "3:2"
}
}
)
data = response.json()
print(data)
const response = await fetch("https://api.muvi.video/v1/jobs/submit", {
method: "POST",
headers: {
"Authorization": "Bearer YOUR_API_KEY",
"Content-Type": "application/json"
},
body: JSON.stringify({
model: "openai/gpt-image-1.5/text-to-image",
input: {
prompt: "A futuristic cityscape at sunset with flying cars",
aspect_ratio: "3:2"
}
})
});
const data = await response.json();
console.log(data);