GPT Image 2.5 Flare is OpenAI’s image-generation and editing edition positioned for fast, high-quality everyday work. It accepts text and images and returns image results. Use it for visual drafts, illustrations, product concepts and recurring creative tasks where a brief needs to become a usable image promptly. Flare is separate from Sunburst, whose positioning emphasises editing precision. Choose Flare for routine image creation and Sunburst when precise editing is the priority.
Start with the main subject, action, environment, composition and visual treatment. For a campaign illustration, request a cyclist crossing a small bridge in warm evening light, with a bold silhouette and open sky for later text. A concise exploration brief can leave aesthetic decisions open; a production brief should name the details and placement that matter. The direct generation and editing workflows allow prompts up to 32,000 characters and one to ten results, making it useful to compare a few variations against the same purpose.
Editing can supply up to sixteen reference images. Assign each reference a clear role rather than expecting the model to decide whether an image represents subject identity, scenery or style. A mask can guide a change to a region; with multiple images it applies to the first. The image and mask need matching format and dimensions, and the mask must contain an alpha channel. The guide requires the source and mask to be below 50 MB. A mask is guidance rather than a promise of exact boundary preservation, so state both the desired modification and what should remain recognisable.
Flare supports low, medium, high, xhigh, max and automatic quality, with auto as the default. Lower quality can serve a quick visual draft; higher settings can be compared for a final asset’s detail. Quality is independent of size and output compression. Standard sizes include 1024 × 1024, 1536 × 1024 and 1024 × 1536, with automatic or custom dimensions also available. Choose the orientation around the final placement and the scene’s useful space.
Custom width and height must be multiples of sixteen, with neither edge above 3840 pixels. The allowed ratio runs from 1:3 to 3:1, and total area must be at least 655,360 and at most 8,294,400 pixels. All four conditions apply together. The guide identifies resolutions above 2560 × 1440 as experimental. Use a valid frame that suits the composition, and include any crop-sensitive details in the brief rather than treating a larger resolution as an automatic layout improvement.
The result format is PNG by default, with JPEG and WebP alternatives. JPEG and WebP allow compression from 0 to 100. For a transparent background, select PNG or WebP; opaque and automatic backgrounds are also supported. A transparent asset can be placed into a later layout, while an opaque image includes its own backdrop. Select format, background and compression according to the actual delivery need, and keep the completed file with the brief and chosen settings.
The producer documents direct image generation and edits, plus a conversational image tool that can retain selected results for follow-up changes. In that workflow, action can be automatic, forced generation or forced editing when an image is in context. Streaming can show up to three partial images before the final result. Those previews provide progress rather than additional finished deliverables. For iterative work, continue from the chosen image and describe a visible change, such as moving one object or adjusting the colour treatment. The manufacturer interface also supports batch processing.
Inspect the image’s lettering, object placement, subject details and composition at the intended size. GPT Image can still struggle with exact text placement, structured layouts and repeated visual identity across generations. Keep an accepted variation as the next reference and narrow the following edit to the issue that matters. Flare’s everyday-generation role is useful for a clear sequence of brief, visual alternatives, selection and refinement, with the final asset judged against the actual creative purpose.
", "aboutText": "GPT Image 2.5 Flare is OpenAI’s image-generation and editing edition positioned for fast, high-quality everyday work. It accepts text and images and returns image results. Use it for visual drafts, illustrations, product concepts and recurring creative tasks where a brief needs to become a usable image promptly. Flare is separate from Sunburst, whose positioning emphasises editing precision. Choose Flare for routine image creation and Sunburst when precise editing is the priority.\n\nStart with the main subject, action, environment, composition and visual treatment. For a campaign illustration, request a cyclist crossing a small bridge in warm evening light, with a bold silhouette and open sky for later text. A concise exploration brief can leave aesthetic decisions open; a production brief should name the details and placement that matter. The direct generation and editing workflows allow prompts up to 32,000 characters and one to ten results, making it useful to compare a few variations against the same purpose.\n\nEditing can supply up to sixteen reference images. Assign each reference a clear role rather than expecting the model to decide whether an image represents subject identity, scenery or style. A mask can guide a change to a region; with multiple images it applies to the first. The image and mask need matching format and dimensions, and the mask must contain an alpha channel. The guide requires the source and mask to be below 50 MB. A mask is guidance rather than a promise of exact boundary preservation, so state both the desired modification and what should remain recognisable.\n\nFlare supports low, medium, high, xhigh, max and automatic quality, with auto as the default. Lower quality can serve a quick visual draft; higher settings can be compared for a final asset’s detail. Quality is independent of size and output compression. Standard sizes include 1024 × 1024, 1536 × 1024 and 1024 × 1536, with automatic or custom dimensions also available. Choose the orientation around the final placement and the scene’s useful space.\n\nCustom width and height must be multiples of sixteen, with neither edge above 3840 pixels. The allowed ratio runs from 1:3 to 3:1, and total area must be at least 655,360 and at most 8,294,400 pixels. All four conditions apply together. The guide identifies resolutions above 2560 × 1440 as experimental. Use a valid frame that suits the composition, and include any crop-sensitive details in the brief rather than treating a larger resolution as an automatic layout improvement.\n\nThe result format is PNG by default, with JPEG and WebP alternatives. JPEG and WebP allow compression from 0 to 100. For a transparent background, select PNG or WebP; opaque and automatic backgrounds are also supported. A transparent asset can be placed into a later layout, while an opaque image includes its own backdrop. Select format, background and compression according to the actual delivery need, and keep the completed file with the brief and chosen settings.\n\nThe producer documents direct image generation and edits, plus a conversational image tool that can retain selected results for follow-up changes. In that workflow, action can be automatic, forced generation or forced editing when an image is in context. Streaming can show up to three partial images before the final result. Those previews provide progress rather than additional finished deliverables. For iterative work, continue from the chosen image and describe a visible change, such as moving one object or adjusting the colour treatment. The manufacturer interface also supports batch processing.\n\nInspect the image’s lettering, object placement, subject details and composition at the intended size. GPT Image can still struggle with exact text placement, structured layouts and repeated visual identity across generations. Keep an accepted variation as the next reference and narrow the following edit to the issue that matters. Flare’s everyday-generation role is useful for a clear sequence of brief, visual alternatives, selection and refinement, with the final asset judged against the actual creative purpose.", "editorialOverlay": { "copy_fields": null, "copy_sha256": null, "copy_source": null, "html_sha256": "e7a1d5e9571b8b0ea5bcd429b4fe8376688a89f6e2beb7fba10359b773ce2821", "module_sha256": "ee9dc1c50b1e6a8fb7fa062030fcb4646b9aaaeb08be513b723a5a92a7290f06", "raw_model_unchanged": true, "schema": "ailoot-native-model-editorial-v1" }, "locale": "en", "methods": [ { "html": "gpt-image-2.5-flareGPT Image 2.5 Flare generates and edits images from text and references, with a focus on fast everyday visual creation. It supports custom sizes, transparent backgrounds, masks and quality levels up to max for drafts, illustrations and campaign assets.
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 OpenAI’s image-generation and editing edition positioned for fast, high-quality everyday work. It accepts text and images and returns image results. Use it for visual drafts, illustrations, product concepts and recurring creative tasks where a brief needs to become a usable image promptly. Flare is separate from Sunburst, whose positioning emphasises editing precision. Choose Flare for routine image creation and Sunburst when precise editing is the priority.
Start with the main subject, action, environment, composition and visual treatment. For a campaign illustration, request a cyclist crossing a small bridge in warm evening light, with a bold silhouette and open sky for later text. A concise exploration brief can leave aesthetic decisions open; a production brief should name the details and placement that matter. The direct generation and editing workflows allow prompts up to 32,000 characters and one to ten results, making it useful to compare a few variations against the same purpose.
Editing can supply up to sixteen reference images. Assign each reference a clear role rather than expecting the model to decide whether an image represents subject identity, scenery or style. A mask can guide a change to a region; with multiple images it applies to the first. The image and mask need matching format and dimensions, and the mask must contain an alpha channel. The guide requires the source and mask to be below 50 MB. A mask is guidance rather than a promise of exact boundary preservation, so state both the desired modification and what should remain recognisable.
Flare supports low, medium, high, xhigh, max and automatic quality, with auto as the default. Lower quality can serve a quick visual draft; higher settings can be compared for a final asset’s detail. Quality is independent of size and output compression. Standard sizes include 1024 × 1024, 1536 × 1024 and 1024 × 1536, with automatic or custom dimensions also available. Choose the orientation around the final placement and the scene’s useful space.
Custom width and height must be multiples of sixteen, with neither edge above 3840 pixels. The allowed ratio runs from 1:3 to 3:1, and total area must be at least 655,360 and at most 8,294,400 pixels. All four conditions apply together. The guide identifies resolutions above 2560 × 1440 as experimental. Use a valid frame that suits the composition, and include any crop-sensitive details in the brief rather than treating a larger resolution as an automatic layout improvement.
The result format is PNG by default, with JPEG and WebP alternatives. JPEG and WebP allow compression from 0 to 100. For a transparent background, select PNG or WebP; opaque and automatic backgrounds are also supported. A transparent asset can be placed into a later layout, while an opaque image includes its own backdrop. Select format, background and compression according to the actual delivery need, and keep the completed file with the brief and chosen settings.
The producer documents direct image generation and edits, plus a conversational image tool that can retain selected results for follow-up changes. In that workflow, action can be automatic, forced generation or forced editing when an image is in context. Streaming can show up to three partial images before the final result. Those previews provide progress rather than additional finished deliverables. For iterative work, continue from the chosen image and describe a visible change, such as moving one object or adjusting the colour treatment. The manufacturer interface also supports batch processing.
Inspect the image’s lettering, object placement, subject details and composition at the intended size. GPT Image can still struggle with exact text placement, structured layouts and repeated visual identity across generations. Keep an accepted variation as the next reference and narrow the following edit to the issue that matters. Flare’s everyday-generation role is useful for a clear sequence of brief, visual alternatives, selection and refinement, with the final asset judged against the actual creative purpose.
POST /v1/images/generationstextimage| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits gpt-image-2.5-flare | 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", "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', '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", "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
Ailoot Ultra gpt-image-2.5-flare, openai/gpt-image-2.5-flare
/v1/images
openai/gpt-image-2.5-flare, gpt-image-2.5-flare, gpt-image-2.5-flare
gpt-image-2.5-flareGPT Image 2.5 Flare generates and edits images from text and references, with a focus on fast everyday visual creation. It supports custom sizes, transparent backgrounds, masks and quality levels up to max for drafts, illustrations and campaign assets.
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 OpenAI’s image-generation and editing edition positioned for fast, high-quality everyday work. It accepts text and images and returns image results. Use it for visual drafts, illustrations, product concepts and recurring creative tasks where a brief needs to become a usable image promptly. Flare is separate from Sunburst, whose positioning emphasises editing precision. Choose Flare for routine image creation and Sunburst when precise editing is the priority.
Start with the main subject, action, environment, composition and visual treatment. For a campaign illustration, request a cyclist crossing a small bridge in warm evening light, with a bold silhouette and open sky for later text. A concise exploration brief can leave aesthetic decisions open; a production brief should name the details and placement that matter. The direct generation and editing workflows allow prompts up to 32,000 characters and one to ten results, making it useful to compare a few variations against the same purpose.
Editing can supply up to sixteen reference images. Assign each reference a clear role rather than expecting the model to decide whether an image represents subject identity, scenery or style. A mask can guide a change to a region; with multiple images it applies to the first. The image and mask need matching format and dimensions, and the mask must contain an alpha channel. The guide requires the source and mask to be below 50 MB. A mask is guidance rather than a promise of exact boundary preservation, so state both the desired modification and what should remain recognisable.
Flare supports low, medium, high, xhigh, max and automatic quality, with auto as the default. Lower quality can serve a quick visual draft; higher settings can be compared for a final asset’s detail. Quality is independent of size and output compression. Standard sizes include 1024 × 1024, 1536 × 1024 and 1024 × 1536, with automatic or custom dimensions also available. Choose the orientation around the final placement and the scene’s useful space.
Custom width and height must be multiples of sixteen, with neither edge above 3840 pixels. The allowed ratio runs from 1:3 to 3:1, and total area must be at least 655,360 and at most 8,294,400 pixels. All four conditions apply together. The guide identifies resolutions above 2560 × 1440 as experimental. Use a valid frame that suits the composition, and include any crop-sensitive details in the brief rather than treating a larger resolution as an automatic layout improvement.
The result format is PNG by default, with JPEG and WebP alternatives. JPEG and WebP allow compression from 0 to 100. For a transparent background, select PNG or WebP; opaque and automatic backgrounds are also supported. A transparent asset can be placed into a later layout, while an opaque image includes its own backdrop. Select format, background and compression according to the actual delivery need, and keep the completed file with the brief and chosen settings.
The producer documents direct image generation and edits, plus a conversational image tool that can retain selected results for follow-up changes. In that workflow, action can be automatic, forced generation or forced editing when an image is in context. Streaming can show up to three partial images before the final result. Those previews provide progress rather than additional finished deliverables. For iterative work, continue from the chosen image and describe a visible change, such as moving one object or adjusting the colour treatment. The manufacturer interface also supports batch processing.
Inspect the image’s lettering, object placement, subject details and composition at the intended size. GPT Image can still struggle with exact text placement, structured layouts and repeated visual identity across generations. Keep an accepted variation as the next reference and narrow the following edit to the issue that matters. Flare’s everyday-generation role is useful for a clear sequence of brief, visual alternatives, selection and refinement, with the final asset judged against the actual creative purpose.
POST /v1/images/generationstextimage| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits gpt-image-2.5-flare | Default— |
Fieldprompt | Typestring | RequirementRequired | Values & limits Maximum length: 32,000 | Default— |
Fieldsize | Typestring | RequirementOptional | Values & limits Conditions: Use auto or WIDTHxHEIGHT with both dimensions divisible by 16. The longest side is at most 3,840 pixels and total size at most 8,300,000 pixels. | Defaultauto |
Fieldbackground | Typestring | RequirementOptional | Values & limits transparent, opaque, auto | Defaultauto |
Fieldmoderation | Typestring | RequirementOptional | Values & limits low, auto Conditions: Use auto for standard content moderation. | Default— |
Fieldn | Typeinteger | RequirementOptional | Values & limits Minimum: 1 Maximum: 10 | Default1 |
Fieldquality | Typestring | RequirementOptional | Values & limits auto, high, medium, low | Defaultauto |
Fieldoutput_compression | Typeinteger | RequirementOptional | Values & limits Minimum: 0 Maximum: 100 Conditions: Applies to JPEG and WebP output; send the documented integer value. | Default100 |
Fieldoutput_format | Typestring | RequirementOptional | Values & limits webp, png, jpeg | Defaultpng |
Fieldstream | Typeboolean | RequirementOptional | Values & limits false Conditions: This route returns one completed JSON response. | Defaultfalse |
| Parameter | Contract |
|---|---|
Parametercontent_type | Contractapplication/json |
Parameterrequired | Contractprompt, model |
Parametermaterial_types | Contracttext |
Parameteroptional | Contractsize, background, moderation, n, quality, output_compression, output_format, stream |
Parameterconditions | ContractA transparent background requires PNG or WebP., Output compression applies to JPEG or WebP., The selected method generates from text. Editing and streaming controls described for the manufacturer are separate capabilities. |
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}
The displayed rates estimate the cost before generation. The final charge is calculated from the cost recorded for the original generation. Measured output duration and forecast quantities are not the final billable quantity.
Tariff references do not add request options. Supported parameters are listed for the selected method.
Match each charge to the conditions shown. Use one rate for each set of alternative configurations.
| Configuration | Rate schedule | Forecast quantity | Charge |
|---|---|---|---|
| ConfigurationBase token rates | Rate scheduleBase rate | Forecast quantity— | Charge Input · Input tokens · Estimate for comparison only — / 1K tokens Output · Output tokens · Estimate for comparison only — / 1K tokens Input · Cache read · Estimate for comparison only — / 1K tokens Input · Cache write · Estimate for comparison only — / 1K tokens Input · Cache write, 1 hour · Estimate for comparison only — / 1K tokens |
| ConfigurationEstimated generation cost | Rate schedule— | Forecast quantityone generation estimate | Charge Output · Estimated generation cost · Estimate for comparison only $0.24576 estimate before generation This estimate is not an additional charge or a fixed rate for the completed generation. |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\nexport AILOOT_API_KEY\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np={'prompt': 'A cute baby sea otter', 'model': 'gpt-image-2.5-flare', 'size': '1024x1024', 'n': 1, 'background': 'auto', 'moderation': 'auto', 'output_format': 'png'}\n\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\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}set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/media/assets \\\n -H "Authorization: Bearer $AILOOT_API_KEY" -o owned-assets.json\n# Preserve every returned asset and follow its exact signed content URL. Do not construct a signed token.\nSave every completed image; retrieve each returned URL or decode its Base64 contents.
Retain the complete response and headers, every actual output, original returned identifiers and all usage fields. Recover the original operation without submitting creation again.
Keep the returned X-Client-Request-ID for reconciliation. Read only actual returned task_id/asset_id values or the owned media library; never build a task URL from the operation ID and never resubmit creation.
| Field | Type |
|---|---|
Fieldcreated | Type |
Fielddata[].url | Type |
Fielddata[].b64_json | Type |
Fieldusage | Type |
Ailoot gpt-image-2.5-flare
Ailoot Ultra gpt-image-2.5-flare, openai/gpt-image-2.5-flare
/v1/images
openai/gpt-image-2.5-flare, gpt-image-2.5-flare, gpt-image-2.5-flare
openai/gpt-image-2.5-flareGPT Image 2.5 Flare generates and edits images from text and references, with a focus on fast everyday visual creation. It supports custom sizes, transparent backgrounds, masks and quality levels up to max for drafts, illustrations and campaign assets.
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 OpenAI’s image-generation and editing edition positioned for fast, high-quality everyday work. It accepts text and images and returns image results. Use it for visual drafts, illustrations, product concepts and recurring creative tasks where a brief needs to become a usable image promptly. Flare is separate from Sunburst, whose positioning emphasises editing precision. Choose Flare for routine image creation and Sunburst when precise editing is the priority.
Start with the main subject, action, environment, composition and visual treatment. For a campaign illustration, request a cyclist crossing a small bridge in warm evening light, with a bold silhouette and open sky for later text. A concise exploration brief can leave aesthetic decisions open; a production brief should name the details and placement that matter. The direct generation and editing workflows allow prompts up to 32,000 characters and one to ten results, making it useful to compare a few variations against the same purpose.
Editing can supply up to sixteen reference images. Assign each reference a clear role rather than expecting the model to decide whether an image represents subject identity, scenery or style. A mask can guide a change to a region; with multiple images it applies to the first. The image and mask need matching format and dimensions, and the mask must contain an alpha channel. The guide requires the source and mask to be below 50 MB. A mask is guidance rather than a promise of exact boundary preservation, so state both the desired modification and what should remain recognisable.
Flare supports low, medium, high, xhigh, max and automatic quality, with auto as the default. Lower quality can serve a quick visual draft; higher settings can be compared for a final asset’s detail. Quality is independent of size and output compression. Standard sizes include 1024 × 1024, 1536 × 1024 and 1024 × 1536, with automatic or custom dimensions also available. Choose the orientation around the final placement and the scene’s useful space.
Custom width and height must be multiples of sixteen, with neither edge above 3840 pixels. The allowed ratio runs from 1:3 to 3:1, and total area must be at least 655,360 and at most 8,294,400 pixels. All four conditions apply together. The guide identifies resolutions above 2560 × 1440 as experimental. Use a valid frame that suits the composition, and include any crop-sensitive details in the brief rather than treating a larger resolution as an automatic layout improvement.
The result format is PNG by default, with JPEG and WebP alternatives. JPEG and WebP allow compression from 0 to 100. For a transparent background, select PNG or WebP; opaque and automatic backgrounds are also supported. A transparent asset can be placed into a later layout, while an opaque image includes its own backdrop. Select format, background and compression according to the actual delivery need, and keep the completed file with the brief and chosen settings.
The producer documents direct image generation and edits, plus a conversational image tool that can retain selected results for follow-up changes. In that workflow, action can be automatic, forced generation or forced editing when an image is in context. Streaming can show up to three partial images before the final result. Those previews provide progress rather than additional finished deliverables. For iterative work, continue from the chosen image and describe a visible change, such as moving one object or adjusting the colour treatment. The manufacturer interface also supports batch processing.
Inspect the image’s lettering, object placement, subject details and composition at the intended size. GPT Image can still struggle with exact text placement, structured layouts and repeated visual identity across generations. Keep an accepted variation as the next reference and narrow the following edit to the issue that matters. Flare’s everyday-generation role is useful for a clear sequence of brief, visual alternatives, selection and refinement, with the final asset judged against the actual creative purpose.
POST /v1/images/generationstext, imageimage| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits openai/gpt-image-2.5-flare | Default— |
Fieldprompt | Typestring | RequirementRequired | Values & limits Minimum length: 1 | Default— |
Fieldaspect_ratio | Typestring | RequirementOptional | Values & limits 1:1, 3:2, 2:3, 4:3, 3:4, 16:9, 9:16, 21:9, auto | Default— |
Fieldquality | Typestring | RequirementOptional | Values & limits auto, low, medium, high, xhigh, max | Default— |
Fieldbackground | Typestring | RequirementOptional | Values & limits auto, transparent, opaque | Default— |
Fieldn | Typeinteger | RequirementOptional | Values & limits Minimum: 1 Maximum: 10 | Default— |
Fieldinput_references | Typearray | RequirementOptional | Values & limits Minimum items: 0 Maximum items: 16 | Default— |
Fieldoutput_compression | Typeinteger | RequirementOptional | Values & limits Minimum: 0 Maximum: 100 | Default— |
Fieldprovider | Typeobject | RequirementOptional | Values & limits | Default— |
| Parameter | Contract |
|---|---|
Parametercontent_type | Contractapplication/json |
Parameterrequired | Contractmodel, prompt |
Parametermaterial_types | Contracttext, image |
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}
The displayed rates estimate the cost before generation. The final charge is calculated from the cost recorded for the original generation. Measured output duration and forecast quantities are not the final billable quantity.
Tariff references do not add request options. Supported parameters are listed for the selected method.
Match each charge to the conditions shown. Use one rate for each set of alternative configurations.
| Configuration | Rate schedule | Forecast quantity | Charge |
|---|---|---|---|
| ConfigurationBase token rates | Rate scheduleBase rate | Forecast quantity— | Charge Input · Input tokens · Estimate for comparison only — / 1K tokens Output · Output tokens · Estimate for comparison only — / 1K tokens Input · Cache read · Estimate for comparison only — / 1K tokens Input · Cache write · Estimate for comparison only — / 1K tokens Input · Cache write, 1 hour · Estimate for comparison only — / 1K tokens |
| ConfigurationEstimated generation cost | Rate schedule— | Forecast quantityone generation estimate | Charge Output · Estimated generation cost · Estimate for comparison only $0.24576 estimate before generation This estimate is not an additional charge or a fixed rate for the completed generation. |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\nexport AILOOT_API_KEY\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np={'model': 'openai/gpt-image-2.5-flare', 'prompt': 'a red panda astronaut floating in space, studio lighting'}\n\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\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}set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/media/assets \\\n -H "Authorization: Bearer $AILOOT_API_KEY" -o owned-assets.json\n# Preserve every returned asset and follow its exact signed content URL. Do not construct a signed token.\nSave every completed image; retrieve each returned URL or decode its Base64 contents.
Retain the complete response and headers, every actual output, original returned identifiers and all usage fields. Recover the original operation without submitting creation again.
Keep the returned X-Client-Request-ID for reconciliation. Read only actual returned task_id/asset_id values or the owned media library; never build a task URL from the operation ID and never resubmit creation.
| Field | Type |
|---|---|
Fieldcreated | Type |
Fielddata[].url | Type |
Fielddata[].b64_json | Type |
Fieldusage | Type |
Ailoot gpt-image-2.5-flare
Ailoot Ultra gpt-image-2.5-flare, openai/gpt-image-2.5-flare
/v1/images
openai/gpt-image-2.5-flare, gpt-image-2.5-flare, gpt-image-2.5-flare