GPT Image 2.5 Flare is an OpenAI model for creating images. According to the manufacturer, its purpose is fast everyday generation of high-quality visual material. A prompt can describe the subject, style, detail placement and desired frame shape; the result is an image.
In Ailoot, this card presents generation with text input. OpenAI also describes work with source images, references and edits. These methods require image-input support; they do not mean that the text method already accepts such material.
The levels low, medium, high, xhigh, max and automatic selection with auto are supported. Quality affects the amount of work performed when forming the result. It is specified separately from frame size and compression of the finished file.
Square, landscape and portrait size options, automatic selection and custom sizes are provided. Aspect ratios can be selected to suit a composition while meeting all manufacturer limits.
Each dimension must be a multiple of 16 pixels and no longer than 3840 pixels. The ratio of the longer dimension to the shorter must not exceed 3:1. Permitted area is between 655,360 and 8,294,400 pixels. The limits apply together: a permitted aspect ratio does not remove the area limit.
Resolutions above 2560x1440 are designated as experimental. When selecting a large frame, the composition itself matters: more pixels do not guarantee correct placement of every element.
The model provides PNG, JPEG and WebP, with PNG as the standard format. Compression can be controlled for JPEG and WebP. A transparent background requires PNG or WebP. The manufacturer also supports an opaque background and automatic selection. Transparency allows an image to be used in a later composition without a specified backdrop.
Manufacturer settings describe model capabilities; execution depends on parameter support in the selected method. A separate generation creates a new result, so an identical description does not guarantee that images match completely.
According to OpenAI, a complex prompt may take up to two minutes to process. This describes generation with the manufacturer, not a guarantee of total result-delivery time in a particular application.
Text, precise object placement and preservation of one appearance across images need checking. A mask in a separately supported editing operation guides the change rather than imposing a hard boundary. For text-to-image generation, it is useful to describe the composition precisely and inspect the file itself: visible details, readability and conformity to the specified subject.
", "aboutText": "### Everyday generation from text descriptions\n\nGPT Image 2.5 Flare is an OpenAI model for creating images. According to the manufacturer, its purpose is fast everyday generation of high-quality visual material. A prompt can describe the subject, style, detail placement and desired frame shape; the result is an image.\n\nIn Ailoot, this card presents generation with text input. OpenAI also describes work with source images, references and edits. These methods require image-input support; they do not mean that the text method already accepts such material.\n\n### Quality from a draft to a detailed image\n\nThe levels `low`, `medium`, `high`, `xhigh`, `max` and automatic selection with `auto` are supported. Quality affects the amount of work performed when forming the result. It is specified separately from frame size and compression of the finished file.\n\nSquare, landscape and portrait size options, automatic selection and custom sizes are provided. Aspect ratios can be selected to suit a composition while meeting all manufacturer limits.\n\n### Custom sizes\n\nEach dimension must be a multiple of 16 pixels and no longer than 3840 pixels. The ratio of the longer dimension to the shorter must not exceed 3:1. Permitted area is between 655,360 and 8,294,400 pixels. The limits apply together: a permitted aspect ratio does not remove the area limit.\n\nResolutions above `2560x1440` are designated as experimental. When selecting a large frame, the composition itself matters: more pixels do not guarantee correct placement of every element.\n\n### Output file and transparency\n\nThe model provides PNG, JPEG and WebP, with PNG as the standard format. Compression can be controlled for JPEG and WebP. A transparent background requires PNG or WebP. The manufacturer also supports an opaque background and automatic selection. Transparency allows an image to be used in a later composition without a specified backdrop.\n\nManufacturer settings describe model capabilities; execution depends on parameter support in the selected method. A separate generation creates a new result, so an identical description does not guarantee that images match completely.\n\n### What to check in the finished image\n\nAccording to OpenAI, a complex prompt may take up to two minutes to process. This describes generation with the manufacturer, not a guarantee of total result-delivery time in a particular application.\n\nText, precise object placement and preservation of one appearance across images need checking. A mask in a separately supported editing operation guides the change rather than imposing a hard boundary. For text-to-image generation, it is useful to describe the composition precisely and inspect the file itself: visible details, readability and conformity to the specified subject.", "editorialOverlay": { "copy_fields": null, "copy_sha256": null, "copy_source": null, "html_sha256": "086623c7f1d72f6935545ccb6ff15f2ec1886e633aed7e8a83239cc4fff00938", "module_sha256": "ee9dc1c50b1e6a8fb7fa062030fcb4646b9aaaeb08be513b723a5a92a7290f06", "raw_model_unchanged": true, "schema": "ailoot-native-model-editorial-v1" }, "locale": "en", "methods": [ { "html": "gpt-image-2.5-flare-2026-09-08GPT Image 2.5 Flare is intended for everyday image generation from descriptions. Provides custom sizes, an extended set of quality levels and transparent backgrounds.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
GPT Image 2.5 Flare is an OpenAI model for creating images. According to the manufacturer, its purpose is fast everyday generation of high-quality visual material. A prompt can describe the subject, style, detail placement and desired frame shape; the result is an image.
In Ailoot, this card presents generation with text input. OpenAI also describes work with source images, references and edits. These methods require image-input support; they do not mean that the text method already accepts such material.
The levels low, medium, high, xhigh, max and automatic selection with auto are supported. Quality affects the amount of work performed when forming the result. It is specified separately from frame size and compression of the finished file.
Square, landscape and portrait size options, automatic selection and custom sizes are provided. Aspect ratios can be selected to suit a composition while meeting all manufacturer limits.
Each dimension must be a multiple of 16 pixels and no longer than 3840 pixels. The ratio of the longer dimension to the shorter must not exceed 3:1. Permitted area is between 655,360 and 8,294,400 pixels. The limits apply together: a permitted aspect ratio does not remove the area limit.
Resolutions above 2560x1440 are designated as experimental. When selecting a large frame, the composition itself matters: more pixels do not guarantee correct placement of every element.
The model provides PNG, JPEG and WebP, with PNG as the standard format. Compression can be controlled for JPEG and WebP. A transparent background requires PNG or WebP. The manufacturer also supports an opaque background and automatic selection. Transparency allows an image to be used in a later composition without a specified backdrop.
Manufacturer settings describe model capabilities; execution depends on parameter support in the selected method. A separate generation creates a new result, so an identical description does not guarantee that images match completely.
According to OpenAI, a complex prompt may take up to two minutes to process. This describes generation with the manufacturer, not a guarantee of total result-delivery time in a particular application.
Text, precise object placement and preservation of one appearance across images need checking. A mask in a separately supported editing operation guides the change rather than imposing a hard boundary. For text-to-image generation, it is useful to describe the composition precisely and inspect the file itself: visible details, readability and conformity to the specified subject.
POST /v1/images/generationstextimage| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits gpt-image-2.5-flare-2026-09-08 | Default— |
Fieldprompt | Typestring | RequirementRequired | Values & limits Minimum length: 1 | Default— |
Fieldn | Typeinteger | RequirementOptional | Values & limits Minimum: 1 | Default1 |
Fieldresponse_format | Typestring | RequirementOptional | Values & limits url, b64_json | Default— |
Fieldstream | Typeboolean | RequirementOptional | Values & limits | Defaultfalse |
Fieldsize | Typestring | RequirementOptional | Values & limits Model-specific | Default— |
Fieldquality | Typestring | RequirementOptional | Values & limits Model-specific | Default— |
Fieldbackground | Typestring | RequirementOptional | Values & limits Model-specific | Default— |
Fieldoutput_format | Typestring | RequirementOptional | Values & limits Model-specific | Default— |
Fieldoutput_compression | Typeinteger | RequirementOptional | Values & limits Model-specific | Default— |
Fieldmoderation | Typestring | RequirementOptional | Values & limits Model-specific | Default— |
Fieldinput_fidelity | Typestring | RequirementOptional | Values & limits Model-specific | Default— |
Fieldstyle | Typestring | RequirementOptional | Values & limits Model-specific | Default— |
Fieldpartial_images | Typeinteger | RequirementOptional | Values & limits Model-specific | Default— |
Text to image · Required: prompt
| Parameter | Contract |
|---|---|
Parametermaterial_types | Contracttext |
Parameterrequired | Contractmodel, prompt |
Parameteroptional_controls | Contractnull |
Parametercontent_type | Contractapplication/json |
Lifecycle: synchronous_result_then_owned_library
GET /v1/media/tasks/{task_id} · actual returned task_id or media library record
GET /v1/media/assets/{asset_id}/content
GET /v1/media/assets
GET /v1/media/assets/{asset_id}
Method shapes and IDs are exact; supplier capability is separate from admission.
POST /v1/images/generationsAiloot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\nexport AILOOT_API_KEY\nexport AILOOT_API_KEY\n: "${AILOOT_PROMPT_FILE:?Set your actual UTF-8 image brief}"\nexport AILOOT_PROMPT_FILE\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{"model": "gpt-image-2.5-flare-2026-09-08", "prompt": ""}')\nf=Path(os.environ["AILOOT_PROMPT_FILE"])\nif not f.is_file():raise SystemExit("Choose your actual UTF-8 brief")\np['prompt']=f.read_text(encoding="utf-8")\nif not p['prompt'].strip():raise SystemExit("Brief must contain text")\nPath("request.json").write_text(json.dumps(p,ensure_ascii=False))\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/images/generations \\\n -H "Authorization: Bearer $AILOOT_API_KEY" -H 'Content-Type: application/json' \\\n --data-binary @request.json -D response-headers.txt -o result.json\npython3 - <<'PY_RESULT'\nimport base64, json, os, subprocess\nfrom pathlib import Path\nfrom urllib.parse import urlsplit, urljoin\n\ndef save_images(items):\n if not items: raise SystemExit("No completed images; retain the full response")\n for index, item in enumerate(items):\n encoded=item.get("b64_json")\n uri=item.get("url", "")\n if not encoded and uri.startswith("data:image/"):\n header, separator, encoded=uri.partition(",")\n if not separator or ";base64" not in header: raise SystemExit("Unsupported image data URI")\n if encoded:\n raw=base64.b64decode(encoded,validate=True)\n elif uri:\n uri=urljoin("https://api.ailoot.ai",uri)\n parsed=urlsplit(uri)\n if parsed.scheme!="https" or not parsed.netloc or parsed.username or parsed.password:\n raise SystemExit("Invalid returned image URL")\n owned=parsed.netloc=="api.ailoot.ai" and parsed.path.startswith("/v1/media/assets/") and parsed.path.endswith("/content")\n args=["curl","--fail-with-body","--silent","--show-error",uri]\n if owned: args += ["-H","Authorization: Bearer "+os.environ["AILOOT_API_KEY"]]\n temporary=Path(f"image-{index}.bin")\n subprocess.run(args+["-o",str(temporary)],check=True)\n raw=temporary.read_bytes()\n temporary.unlink()\n else: raise SystemExit("Image has neither bytes nor URL")\n if not raw: raise SystemExit("Empty image result")\n ext="png" if raw.startswith(b"\\x89PNG") else "jpg" if raw.startswith(b"\\xff\\xd8") else "webp" if raw[:4]==b"RIFF" and raw[8:12]==b"WEBP" else "svg" if raw.lstrip().startswith((b"<svg",b"<?xml")) else "bin"\n Path(f"image-{index}.{ext}").write_bytes(raw)\n print("Every returned image saved; complete response metadata retained")\nsave_images(json.loads(Path("result.json").read_text())["data"])\nPY_RESULT\nfrom openai import OpenAI\nimport base64, json, os, subprocess\nfrom pathlib import Path\nfrom urllib.parse import urlsplit, urljoin\n\ndef save_images(items):\n if not items: raise SystemExit("No completed images; retain the full response")\n for index, item in enumerate(items):\n encoded=item.get("b64_json")\n uri=item.get("url", "")\n if not encoded and uri.startswith("data:image/"):\n header, separator, encoded=uri.partition(",")\n if not separator or ";base64" not in header: raise SystemExit("Unsupported image data URI")\n if encoded:\n raw=base64.b64decode(encoded,validate=True)\n elif uri:\n uri=urljoin("https://api.ailoot.ai",uri)\n parsed=urlsplit(uri)\n if parsed.scheme!="https" or not parsed.netloc or parsed.username or parsed.password:\n raise SystemExit("Invalid returned image URL")\n owned=parsed.netloc=="api.ailoot.ai" and parsed.path.startswith("/v1/media/assets/") and parsed.path.endswith("/content")\n args=["curl","--fail-with-body","--silent","--show-error",uri]\n if owned: args += ["-H","Authorization: Bearer "+os.environ["AILOOT_API_KEY"]]\n temporary=Path(f"image-{index}.bin")\n subprocess.run(args+["-o",str(temporary)],check=True)\n raw=temporary.read_bytes()\n temporary.unlink()\n else: raise SystemExit("Image has neither bytes nor URL")\n if not raw: raise SystemExit("Empty image result")\n ext="png" if raw.startswith(b"\\x89PNG") else "jpg" if raw.startswith(b"\\xff\\xd8") else "webp" if raw[:4]==b"RIFF" and raw[8:12]==b"WEBP" else "svg" if raw.lstrip().startswith((b"<svg",b"<?xml")) else "bin"\n Path(f"image-{index}.{ext}").write_bytes(raw)\n print("Every returned image saved; complete response metadata retained")\n\nf=Path(os.environ["AILOOT_PROMPT_FILE"])\nif not f.is_file():raise SystemExit("Set your actual UTF-8 image brief file")\nrequest={'model': 'gpt-image-2.5-flare-2026-09-08', 'prompt': ''}\nrequest["prompt"]=f.read_text(encoding="utf-8")\nclient=OpenAI(base_url="https://api.ailoot.ai/v1",api_key=os.environ["AILOOT_API_KEY"],max_retries=0)\nresult=client.images.generate(**request)\nPath("sdk-result.txt").write_text(str(result),encoding="utf-8")\nsave_images([{"b64_json":getattr(item,"b64_json",None),"url":getattr(item,"url",None)} for item in result.data])\nimport OpenAI from "openai";\nimport { readFileSync, writeFileSync } from "node:fs";\nconst key=process.env.AILOOT_API_KEY;\nif(!key) throw new Error("Set your actual Ailoot API key");\nconst client=new OpenAI({baseURL:"https://api.ailoot.ai/v1",apiKey:key,maxRetries:0});\nconst request={"model": "gpt-image-2.5-flare-2026-09-08", "prompt": ""};\nif(!process.env.AILOOT_PROMPT_FILE)throw new Error("Set your actual UTF-8 image brief file");\nrequest.prompt=readFileSync(process.env.AILOOT_PROMPT_FILE,"utf8");\nconst result=await client.images.generate(request);\nwriteFileSync("sdk-result.json",JSON.stringify(result,null,2));\nif(!result.data?.length) throw new Error("No completed images");\nfor(let index=0;index<result.data.length;index++) {\n const item=result.data[index];let encoded=item.b64_json,uri=item.url||"",raw;\n if(!encoded && uri.startsWith("data:image/")) {\n const comma=uri.indexOf(",");if(comma<0 || !uri.slice(0,comma).includes(";base64")) throw new Error("Unsupported image data URI");\n encoded=uri.slice(comma+1);\n }\n if(encoded) {\n if(encoded.length%4!==0 || !/^[A-Za-z0-9+/]*={0,2}$/.test(encoded)) throw new Error("Invalid Base64");\n raw=Buffer.from(encoded,"base64");\n } else if(uri) {\n const u=new URL(uri,"https://api.ailoot.ai");\n if(u.protocol!=="https:" || u.username || u.password) throw new Error("Invalid returned URL");\n const owned=u.origin==="https://api.ailoot.ai" && u.pathname.startsWith("/v1/media/assets/") && u.pathname.endsWith("/content");\n const response=await fetch(u,{headers:owned?{Authorization:"Bearer "+key}:{},redirect:"manual"});\n if(!response.ok) throw new Error("Image content download failed: "+response.status);\n raw=Buffer.from(await response.arrayBuffer());\n } else throw new Error("Image has neither bytes nor URL");\n if(!raw.length) throw new Error("Empty image result");\n const ext=raw.subarray(0,4).equals(Buffer.from([137,80,78,71]))?"png":raw[0]===255&&raw[1]===216?"jpg":raw.toString("ascii",0,4)==="RIFF"&&raw.toString("ascii",8,12)==="WEBP"?"webp":raw.toString("utf8").trimStart().startsWith("<svg")||raw.toString("utf8").trimStart().startsWith("<?xml")?"svg":"bin";\n writeFileSync(`image-${index}.${ext}`,raw);\n}\nconsole.log("Every returned image saved; sdk-result.json retains complete metadata");\nDownload each returned image or save its Base64 contents
GET /v1/media/tasks/{task_id}GET /v1/media/assets/{asset_id}/contentGET /v1/media/assetsGET /v1/media/assets/{asset_id}Save every completed image; retrieve each returned URL or decode its Base64 contents.
Save every data item. Decode each b64_json or fetch every returned signed URL. Select a filename extension from the actual MIME/bytes.
If an image generation or edit result requires a check, keep the original operation_id/X-Client-Request-ID and returned result information. Recover existing owned results through the media library using actual task_id or asset_id values. Do not resubmit the generation/edit request or construct a polling URL from operation_id.
| Field | Type |
|---|---|
Fieldcreated | Typeinteger |
Fielddata[].url | Typestring |
Fielddata[].b64_json | Typebase64 string |
Fieldusage | Typeobject |
| Response | Condition | Next step |
|---|---|---|
Response400 · invalid_request_error | ConditionInvalid required fields, material representation or parameter value | Next stepCorrect the indicated fields before a new creation request. |
Responseserver_error | ConditionThe returned response says the operation or result requires a check | Next stepKeep the original returned operation/task ID, request headers and response; continue supported reads of that original operation. Do not resubmit the creation request. |
Ailoot gpt-image-2.5-flare-2026-09-08
Use the method paths shown above.