GPT Image 2.5 Text to Image
Integriruj
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/rom/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"
}
}
Kall o GPT Image 2.5 Text to Image modelo (openai/gpt-image-2.5/flare/text-to-image) vas o Doitong API. Dokumentacia vaš agentura: https://doitong.com/rom/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"
}
Te o response status si "queued" vaj "running", ker polling GET https://api.doitong.ru/v1/requests/REQUEST_ID?wait=20 dži kaj te avel "succeeded"; e fajlo si andro outputs[].url. POST https://api.doitong.ru/v1/quote/openai/gpt-image-2.5/flare/text-to-image sa jekhe body-esa del o precizno molipen angl-o phiripen.
Kava mangipen: $0.013 13 API krediti · ≈ ₽1.40 Phir les kate
Tiro nevo API klidin. Dikhavel pes akana numa thaj si angleder ando kodo thaj ando API klidin kotor tele. Garav les: na thovel les ande kodo savo keren andre jekh browser.
Primerura
Ker jekh mangipen
O kontrolura kerde si katar e schemaske. O kodo thaj e cena menjinen sar tu len menjines.
/run/openai/gpt-image-2.5/flare/text-to-image?wait=20
hesab → $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
}
}
}
Phandel kava mangipen
Tiro kluč ačhel kadale browsereste: bičhalel pes numa ko Doitong API thaj šuvav pes (localStorage) numa te tiknes Remember. Le jekh API ključ
Kerdo pe Doitong
Publikane khera kerde GPT Image 2.5-sa e Doitong komunitetostar. Pheripen jekh prompto ande jekh klik.
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Grungy analog photo of Daphne Blake (from Serial scooby doo) watching her own movie on a 90s CRT TV in a dimly lit bedroom. The TV clearly shows animated scene from scooby doo, with a cartoon-style Daphne in her classic clothes on screen, smiling. Daphne Blake is sitting cross-legged on the floor...
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Use facial feature of attached photo. A realistic portrait of a woman with black hair loosely tied back, sitting outside a whitewashed stone house with a conical roof in a quiet Italian village. She wears a soft yellow floral summer dress with puff sleeves and white strappy heels. A wicker basket...
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Art style: Кинематографичный, реалистичный стиль. Резкая смена кадра. Девушка с ужасом смотрит на себя в зеркало. Маска снята, но ее лицо гротескно искажено: глаза слишком большие, улыбка растянута неестественно. Освещение стало холодным и резким. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ FINAL...
Art style: Кинематографичный, реалистичный стиль. Резкая смена кадра. Девушка с ужасом смотрит на себя в зеркало. Маска снята, но ее лицо гротескно искажено: глаза слишком большие, улыбка растянута неестественно. Освещение стало холодным и резким. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ FINAL...
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Palal GPT Image 2.5 Text to Image
OpenAI-ki fotografijaki generatori sajekhane dija: sledil lunge thaj specifikane instrukcien, šukar aranžiril teksto thaj diagramura, thaj šaj te del PNG vaj WebP transparenno fondosa vaś stickerura, ikonura thaj produktura. Pandž kalite lavel trgovinil siglost thaj cena vaš detalura. Barimata džan dži 3840 px ko lungo krajo (multiplikacija 16-esa, maksimalno 3:1); dži deš fotografije ko jekh mangipen.
Parametra
Bičhalav vaj e kanonikano nav vaj e native nav, na duj. Kanonikane nava
prompttrubul
image_sizeopcionalno
backgroundopcionalno
qualityopcionalno
num_imagesopcionalno
num_outputs
output_formatopcionalno
output_compressionopcionalno
Rezultato
{
"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"
}
}
Outputoske fieldura
- images
- array of object {url, content_type, file_name, file_size, width, height}Kipo kerdine e modeloça.
Aver variantura e GPT Image 2.5
Bute modeli: Teksto ko Kip
AI agentenge: kadija rig sar Markdown si kate https://doitong.com/rom/developers/models/openai/gpt-image-2.5/flare/text-to-image.md. Sa e modelura thaj e gidura: https://doitong.com/developers/llms.txt







