OpenAI's gpt-image-1 enables image generation and image editing via OpenAI's image API, ideal for creating and refining images. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.
Idle

$0.042per run·~23 / $1

Become a comic style.

Converted to comic style.

Change the black sweater to a white sweater.

Remove the background.

Change the tiger's fur color to orange.

remove the moon.

Change the background to grass.

Change the background to a starry sky.

Add the same pillow as on the left.

The image becomes a watercolor style.

Change hair color to red.
Edit and transform images with natural language using OpenAI's GPT-Image-1. This versatile model understands your instructions to apply style changes, modifications, and creative transformations — with optional mask support for precise regional editing and multiple quality tiers to match your needs and budget.
| Parameter | Required | Description |
|---|---|---|
| prompt | Yes | Text instruction describing the edit or transformation you want. |
| image | Yes | Source image to edit (upload or public URL). |
| quality | No | Output quality: low, medium, or high. Default: medium. |
| mask_image | No | Optional mask to specify edit regions (upload or URL). |
| size | No | Output dimensions: 1024x1024, 1024x1536, or 1536x1024. |
| enable_sync_mode | No | API only: Waits for result and returns it directly. |
| enable_base64_output | No | API only: Returns base64 string instead of URL. |
Pricing varies by quality level and output size.
| Quality | Cost |
|---|---|
| Low | $0.011 |
| Medium | $0.042 |
| High | $0.167 |
| Quality | Cost |
|---|---|
| Low | $0.016 |
| Medium | $0.063 |
| High | $0.250 |
| Quality | 1024x1024 | 1024x1536 / 1536x1024 |
|---|---|---|
| Low | $0.011 | $0.016 |
| Medium | $0.042 | $0.063 |
| High | $0.167 | $0.250 |
| Quality | Best For | Trade-off |
|---|---|---|
| Low | Quick previews, testing concepts, high-volume processing | Fastest, most affordable, lower detail |
| Medium | General use, social media, balanced needs | Good quality/cost balance |
| High | Professional work, final deliverables, maximum detail | Highest quality, premium price |
Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/openai/gpt-image-1 with your input as JSON. The endpoint returns a prediction id. Start polling the result endpoint around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. On completed, read output values from data.outputs. Examples for Gpt Image 1 below.
set -euo pipefail
: "${WAVESPEED_API_KEY:?Set WAVESPEED_API_KEY}"
REQUEST_BODY=$(cat <<'JSON'
{
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
"quality": "medium",
"size": "auto"
}
JSON
)
# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
-X POST "https://api.wavespeed.ai/api/v3/openai/gpt-image-1" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $WAVESPEED_API_KEY" \
-d "$REQUEST_BODY")
TASK=$(printf '%s' "$SUBMIT_RESPONSE" | jq 'if has("data") then .data else . end')
PREDICTION_ID=$(printf '%s' "$TASK" | jq -r '.id')
if [ -z "$PREDICTION_ID" ] || [ "$PREDICTION_ID" = "null" ]; then
printf 'Submission response did not contain a prediction id
' >&2
exit 1
fi
RESULT_URL=$(printf '%s' "$TASK" | jq -r '.urls.get // empty')
if [ -z "$RESULT_URL" ]; then
RESULT_URL="https://api.wavespeed.ai/api/v3/predictions/$PREDICTION_ID/result"
fi
# 2. Poll until the prediction finishes.
while true; do
RESPONSE=$(curl --silent --show-error --fail-with-body "$RESULT_URL" \
-H "Authorization: Bearer $WAVESPEED_API_KEY")
RESULT=$(printf '%s' "$RESPONSE" | jq 'if has("data") then .data else . end')
STATUS=$(printf '%s' "$RESULT" | jq -r '.status')
case "$STATUS" in
completed) printf '%s\n' "$RESULT" | jq '.outputs'; break ;;
failed|cancelled|timeout) printf '%s\n' "$RESULT" | jq . >&2; exit 1 ;;
created|processing) sleep 2 ;;
*) printf 'Unexpected status: %s
' "$STATUS" >&2; exit 1 ;;
esac
doneconst submitUrl = "https://api.wavespeed.ai/api/v3/openai/gpt-image-1";
const apiKey = process.env.WAVESPEED_API_KEY;
if (!apiKey) throw new Error('Set WAVESPEED_API_KEY');
async function requestJson(url, options = {}) {
const response = await fetch(url, options);
if (!response.ok) throw new Error(await response.text());
return response.json();
}
// 1. Submit the prediction.
const body = await requestJson(submitUrl, {
method: "POST",
headers: {
"Authorization": `Bearer ${apiKey}`,
"Content-Type": "application/json",
},
body: JSON.stringify({
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
"quality": "medium",
"size": "auto"
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
`https://api.wavespeed.ai/api/v3/predictions/${task.id}/result`;
// 2. Poll until the prediction finishes.
while (true) {
const resultBody = await requestJson(resultUrl, {
headers: { "Authorization": `Bearer ${apiKey}` },
});
const result = resultBody.data ?? resultBody;
if (result.status === "completed") {
console.log(result.outputs);
break;
}
if (["failed", "cancelled", "timeout"].includes(result.status)) throw new Error(JSON.stringify(result));
if (!["created", "processing"].includes(result.status)) throw new Error("Unexpected status: " + result.status);
await new Promise(resolve => setTimeout(resolve, 2000));
}import json
import os
import time
from urllib.request import Request, urlopen
api_key = os.environ["WAVESPEED_API_KEY"]
headers = {"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"}
payload = {
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
"quality": "medium",
"size": "auto"
}
def request_json(url, data=None):
request = Request(url, data=data, headers=headers, method="POST" if data else "GET")
with urlopen(request) as response:
return json.load(response)
# 1. Submit the prediction.
body = request_json("https://api.wavespeed.ai/api/v3/openai/gpt-image-1", json.dumps(payload).encode())
task = body.get("data", body)
if not task.get("id"):
raise RuntimeError("Submission response did not contain a prediction id")
result_url = task.get("urls", {}).get("get") or f"https://api.wavespeed.ai/api/v3/predictions/{task['id']}/result"
# 2. Poll until the prediction finishes.
while True:
result_body = request_json(result_url)
result = result_body.get("data", result_body)
status = result.get("status")
if status == "completed":
print(result.get("outputs", []))
break
if status in {"failed", "cancelled", "timeout"}:
raise RuntimeError(result)
if status not in {"created", "processing"}:
raise RuntimeError(f"Unexpected status: {status}")
time.sleep(2)Gpt Image 1 is a OpenAI model for image editing, exposed as a REST API on WaveSpeedAI. OpenAI's gpt-image-1 enables image generation and image editing via OpenAI's image API, ideal for creating and refining images. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing. You can call it programmatically or try it from the playground above.
POST your input parameters to the model's REST endpoint (shown in the API tab of this playground) with your WaveSpeedAI API key in the Authorization header. Submission returns a prediction ID. Poll the result endpoint starting around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. The playground generates production-oriented Python, JavaScript, and cURL examples with timeouts, transient-error handling, and safe GET retries. Full request/response shape is documented at https://wavespeed.ai/docs/docs-api/openai/openai-gpt-image-1.
Gpt Image 1 starts at $0.042 per run. That figure is the base price — the final charge scales with the parameters you set in the form (output size, length, count, references, or whatever knobs this model exposes), so a higher-quality or larger output costs more than a minimal one. The exact cost for your current input is shown live next to the Generate button before you submit, and the actual per-call charge is recorded on the prediction afterwards.
Key inputs: `prompt`, `image`, `size`, `enable_base64_output`, `enable_sync_mode`, `mask_image`. The full JSON schema (types, defaults, allowed values) is rendered above the Generate button and mirrored in the API reference at https://wavespeed.ai/docs/docs-api/openai/openai-gpt-image-1.
Median end-to-end generation time on WaveSpeedAI is around 42 seconds per request, based on recent successful runs. Queue time varies with global demand; live status is visible in the prediction record.
Commercial usage rights depend on the model's license, set by its provider (OpenAI). The license summary appears on the model card above; see WaveSpeedAI's Terms of Service for platform-level conditions.