GPT Image 2.5 Text to Image
Intégrer
openai/gpt-image-2.5/flare/text-to-image
# Run the model; wait up to 20 s for the finished result in the same call
curl -X POST "https://api.doitong.ru/v1/run/openai/gpt-image-2.5/flare/text-to-image?wait=20" \
-H "Authorization: Bearer $DOITONG_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"prompt": "A handwritten recipe card titled \"Apple Pie\" on a wooden kitchen table, neat cursive ingredient list, a slice of pie on a plate beside it, cinnamon sticks, warm afternoon light, photorealistic.",
"quality": "medium"
}'
# Still "queued" or "running"? Poll with the id from the response:
curl "https://api.doitong.ru/v1/requests/REQUEST_ID?wait=20" \
-H "Authorization: Bearer $DOITONG_API_KEY"
const API = "https://api.doitong.ru/v1";
const headers = {
"Authorization": "Bearer " + process.env.DOITONG_API_KEY,
"Content-Type": "application/json",
};
// Run the model; wait up to 20 s for the finished result in the same call
let res = await fetch(API + "/run/openai/gpt-image-2.5/flare/text-to-image?wait=20", {
method: "POST",
headers,
body: JSON.stringify({
"prompt": "A handwritten recipe card titled \"Apple Pie\" on a wooden kitchen table, neat cursive ingredient list, a slice of pie on a plate beside it, cinnamon sticks, warm afternoon light, photorealistic.",
"quality": "medium"
}),
});
let request = await res.json();
if (!res.ok) throw new Error(request.error.code + ": " + request.error.message);
// Not finished yet: long-poll until it is
while (request.status === "queued" || request.status === "running") {
res = await fetch(API + "/requests/" + request.id + "?wait=20", { headers });
request = await res.json();
}
if (request.status !== "succeeded") throw new Error(JSON.stringify(request.error));
console.log(request.outputs.map((o) => o.url), request.cost.credits + " API credits ($" + request.cost.usd + ")");
import os
import requests
API = "https://api.doitong.ru/v1"
headers = {"Authorization": "Bearer " + os.environ["DOITONG_API_KEY"]}
payload = {
"prompt": "A handwritten recipe card titled \"Apple Pie\" on a wooden kitchen table, neat cursive ingredient list, a slice of pie on a plate beside it, cinnamon sticks, warm afternoon light, photorealistic.",
"quality": "medium",
}
# Run the model; wait up to 20 s for the finished result in the same call
r = requests.post(API + "/run/openai/gpt-image-2.5/flare/text-to-image", params={"wait": 20}, json=payload, headers=headers)
request = r.json()
if not r.ok:
raise RuntimeError(request["error"]["code"] + ": " + request["error"]["message"])
# Not finished yet: long-poll until it is
while request["status"] in ("queued", "running"):
request = requests.get(API + "/requests/" + request["id"], params={"wait": 20}, headers=headers).json()
if request["status"] != "succeeded":
raise RuntimeError(request["error"])
print([o["url"] for o in request["outputs"]], request["cost"]["credits"], "API credits", "$" + request["cost"]["usd"])
# 1. Connect once (Claude Code; Cursor, VS Code, Claude Desktop: https://doitong.com/fr-FR/developers/docs/mcp)
claude mcp add --transport http doitong-models https://api.doitong.ru/v1/mcp \
--header "Authorization: Bearer $DOITONG_API_KEY"
# 2. Your agent calls the "run" tool with:
{
"model_id": "openai/gpt-image-2.5/flare/text-to-image",
"input": {
"prompt": "A handwritten recipe card titled \"Apple Pie\" on a wooden kitchen table, neat cursive ingredient list, a slice of pie on a plate beside it, cinnamon sticks, warm afternoon light, photorealistic.",
"quality": "medium"
}
}
Appelez le modèle GPT Image 2.5 Text to Image (openai/gpt-image-2.5/flare/text-to-image) via la Doitong API. Documentation pour les agents : https://doitong.com/fr-FR/developers/models/openai/gpt-image-2.5/flare/text-to-image.md
POST https://api.doitong.ru/v1/run/openai/gpt-image-2.5/flare/text-to-image?wait=20
Authorization: Bearer $DOITONG_API_KEY
Content-Type: application/json
{
"prompt": "A handwritten recipe card titled \"Apple Pie\" on a wooden kitchen table, neat cursive ingredient list, a slice of pie on a plate beside it, cinnamon sticks, warm afternoon light, photorealistic.",
"quality": "medium"
}
Si le statut de la réponse est "queued" ou "running", interrogez GET https://api.doitong.ru/v1/requests/REQUEST_ID?wait=20 jusqu'à ce qu'il soit "succeeded" ; les fichiers se trouvent dans outputs[].url. POST https://api.doitong.ru/v1/quote/openai/gpt-image-2.5/flare/text-to-image avec le même corps renvoie le prix exact avant l'exécution.
Cette requête: 0,013 $ 13 crédits API · ≈ 1,39 ₽ L'exécuter sur cette page
Votre nouvelle clé API. Elle ne s'affiche que maintenant et figure déjà dans le code et dans le champ de clé API ci-dessous. Gardez-la secrète : ne la mettez jamais dans du code exécuté dans un navigateur.
Exemples
Créer une requête
Les contrôles sont générés à partir du schéma du modèle. Le code et le prix se mettent à jour lorsque vous les modifiez.
/run/openai/gpt-image-2.5/flare/text-to-image?wait=20
devis → 0,013 $
# Run the model; wait up to 20 s for the finished result in the same call
curl -X POST "https://api.doitong.ru/v1/run/openai/gpt-image-2.5/flare/text-to-image?wait=20" \
-H "Authorization: Bearer $DOITONG_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"prompt": "A handwritten recipe card titled \"Apple Pie\" on a wooden kitchen table, neat cursive ingredient list, a slice of pie on a plate beside it, cinnamon sticks, warm afternoon light, photorealistic.",
"quality": "medium"
}'
# Still "queued" or "running"? Poll with the id from the response:
curl "https://api.doitong.ru/v1/requests/REQUEST_ID?wait=20" \
-H "Authorization: Bearer $DOITONG_API_KEY"
const API = "https://api.doitong.ru/v1";
const headers = {
"Authorization": "Bearer " + process.env.DOITONG_API_KEY,
"Content-Type": "application/json",
};
// Run the model; wait up to 20 s for the finished result in the same call
let res = await fetch(API + "/run/openai/gpt-image-2.5/flare/text-to-image?wait=20", {
method: "POST",
headers,
body: JSON.stringify({
"prompt": "A handwritten recipe card titled \"Apple Pie\" on a wooden kitchen table, neat cursive ingredient list, a slice of pie on a plate beside it, cinnamon sticks, warm afternoon light, photorealistic.",
"quality": "medium"
}),
});
let request = await res.json();
if (!res.ok) throw new Error(request.error.code + ": " + request.error.message);
// Not finished yet: long-poll until it is
while (request.status === "queued" || request.status === "running") {
res = await fetch(API + "/requests/" + request.id + "?wait=20", { headers });
request = await res.json();
}
if (request.status !== "succeeded") throw new Error(JSON.stringify(request.error));
console.log(request.outputs.map((o) => o.url), request.cost.credits + " API credits ($" + request.cost.usd + ")");
import os
import requests
API = "https://api.doitong.ru/v1"
headers = {"Authorization": "Bearer " + os.environ["DOITONG_API_KEY"]}
payload = {
"prompt": "A handwritten recipe card titled \"Apple Pie\" on a wooden kitchen table, neat cursive ingredient list, a slice of pie on a plate beside it, cinnamon sticks, warm afternoon light, photorealistic.",
"quality": "medium",
}
# Run the model; wait up to 20 s for the finished result in the same call
r = requests.post(API + "/run/openai/gpt-image-2.5/flare/text-to-image", params={"wait": 20}, json=payload, headers=headers)
request = r.json()
if not r.ok:
raise RuntimeError(request["error"]["code"] + ": " + request["error"]["message"])
# Not finished yet: long-poll until it is
while request["status"] in ("queued", "running"):
request = requests.get(API + "/requests/" + request["id"], params={"wait": 20}, headers=headers).json()
if request["status"] != "succeeded":
raise RuntimeError(request["error"])
print([o["url"] for o in request["outputs"]], request["cost"]["credits"], "API credits", "$" + request["cost"]["usd"])
{
"jsonrpc": "2.0",
"id": 1,
"method": "tools/call",
"params": {
"name": "run",
"arguments": {
"model_id": "openai/gpt-image-2.5/flare/text-to-image",
"input": {
"prompt": "A handwritten recipe card titled \"Apple Pie\" on a wooden kitchen table, neat cursive ingredient list, a slice of pie on a plate beside it, cinnamon sticks, warm afternoon light, photorealistic.",
"quality": "medium"
},
"wait_seconds": 20
}
}
}
Exécuter cette requête
Votre clé reste dans ce navigateur : elle est envoyée uniquement à la Doitong API et enregistrée dans localStorage uniquement si vous cochez Mémoriser. Obtenir une clé API
Créé sur Doitong
Créations publiques réalisées avec GPT Image 2.5 par la communauté Doitong. Réutilisez une invite en un clic.
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À propos de GPT Image 2.5 Text to Image
Générateur d’images généraliste d’OpenAI : il suit des instructions longues et précises, met en page proprement le texte et les diagrammes, et peut renvoyer des fichiers PNG ou WebP avec un arrière-plan transparent pour les autocollants, icônes et détourage de produits. Cinq niveaux de qualité arbitrent vitesse et prix contre niveau de détail. Les tailles vont jusqu’à 3840 px sur le bord le plus long (multiples de 16, au plus 3:1) ; jusqu’à dix images par requête.
Paramètres
Envoyez soit le nom canonique, soit le nom natif, mais pas les deux. Noms canoniques
promptobligatoire
image_sizefacultatif
backgroundfacultatif
qualityfacultatif
num_imagesfacultatif
num_outputs
output_formatfacultatif
output_compressionfacultatif
Sortie
{
"id": "req_8k2m4q7x1z9c3v5b6n0p2r4t",
"object": "request",
"model": "openai/gpt-image-2.5/flare/text-to-image",
"status": "succeeded",
"created_at": "2026-10-20T12:00:00Z",
"started_at": "2026-10-20T12:00:02Z",
"completed_at": "2026-10-20T12:00:09Z",
"queue_position": null,
"cost": {
"credits": 13,
"usd": "0.013",
"charged": 13,
"refunded": 0
},
"input": {
"prompt": "A flat vector sticker of a smiling cactus wearing sunglasses, thick white outline, no background",
"image_size": "square_hd",
"quality": "medium",
"background": "transparent"
},
"output": {
"images": [
{
"url": "https://content.doitong.com/dev-api/2026/10/20/req_8k2m4q7x1z9c3v5b6n0p2r4t/0-output.jpeg",
"content_type": "image/jpeg",
"file_name": "0-output.jpeg",
"file_size": 1843200,
"width": 1024,
"height": 768
}
]
},
"outputs": [
{
"type": "image",
"url": "https://content.doitong.com/dev-api/2026/10/20/req_8k2m4q7x1z9c3v5b6n0p2r4t/0-output.jpeg",
"content_type": "image/jpeg",
"file_size": 1843200,
"width": 1024,
"height": 768,
"stored": true
}
],
"error": null,
"metrics": {
"inference_seconds": 6.8
},
"urls": {
"self": "https://api.doitong.ru/v1/requests/req_8k2m4q7x1z9c3v5b6n0p2r4t",
"cancel": "https://api.doitong.ru/v1/requests/req_8k2m4q7x1z9c3v5b6n0p2r4t/cancel"
}
}
Champs de sortie
- images
- array of object {url, content_type, file_name, file_size, width, height}Images produites par le modèle.
Autres variantes GPT Image 2.5
Plus de modèles : Texte vers image
Pour les agents IA : cette page au format Markdown est disponible à l'adresse https://doitong.com/fr-FR/developers/models/openai/gpt-image-2.5/flare/text-to-image.md. Tous les modèles et guides : https://doitong.com/developers/llms.txt







