GPT-5 Image Mini is a combined language-and-image profile built around OpenAI’s GPT-5 Mini and GPT Image 1 Mini. Its language component interprets a written task and source material, while its image component creates or revises visual content. The published profile emphasizes instruction following, text rendering and detailed image editing. It is useful for a focused visual brief that also needs textual interpretation, explanation or supporting copy.
Prepare a brief with the intended asset, subject, composition and required text. Describe how the image will be used, such as a product concept, an illustration accompanying a short explanation or a visual draft for a page. Ask for the written and visual deliverables explicitly so an explanation of the concept is easy to distinguish from the image itself.
For example, supply a description of a reusable water bottle and request a simple product-scene concept with a short caption. Specify the visible bottle shape, background, light direction and the exact words that belong in the visual. Keep facts used in the caption separate from decorative details in the scene. Review the returned image and written text against those two parts of the brief.
The documented combined profile accepts text, images and files such as PDFs. A document can provide requirements or facts to interpret before creating the visual. An image can supply appearance, layout or an editing source. State each source’s role rather than assume that every attachment should affect every part of the result. Keep source names and ordering clear when several inputs are supplied.
For a new image, specify what should be visible and how the parts relate to one another. For a reference-led edit, identify the change and the details to preserve. A product scene can request a new setting while retaining the item’s recognizable form and visible label. A diagram can ask for a corrected relationship while naming the text and elements that should stay.
The profile’s own published card permits up to sixteen reference images for editing. Choose references that contribute to the task and explain their purpose: the product, character, background or visual treatment. Several references can contain conflicting lighting or composition, so name the intended result clearly. Inspect subject details, lettering and the boundaries of a requested change in the actual returned image.
Text rendering is a documented capability, but a visual draft still needs a word-by-word check. Provide exact wording, punctuation and placement, then compare important labels and numbers with the brief at the intended viewing size. For a repeated series, retain the references and visual requirements with each result and compare the completed images together.
The combined profile publishes a 400,000-token context window and up to 128,000 completion tokens. These are working-context and completion allowances, separate from an image’s pixel dimensions or the number of returned image artifacts. Organize long source material around the required interpretation and leave room for the response. A compact brief with relevant references is more useful than unrelated documents whose role has not been explained.
Its published text-output contract supports JSON-schema structured results. Define the fields and types when a written result needs to be consumed by an application, and validate the values against the supplied evidence. The combined profile does not expose application function calling. Its use of a language component with image generation is a named profile capability; it does not make every feature of a standalone GPT-5 Mini interface part of this workflow.
Image settings should come from the exact profile and interface being used. Keep the selected configuration with the prompt when comparing revisions. The direct GPT Image 1 Mini component and a combined chat profile have different request arrangements, so component-specific size, masking or output controls need an explicit mapping before they are treated as settings of this combined profile.
The documented chat request schema can select text and image as output modalities. Process the returned text and every actual image artifact that accompanies the completion. Save the images in the format identified by the result and retain the written response with the prompt, source references and settings. A completion containing explanatory text alone is different from a completed visual deliverable, so check what the result actually contains before using it in a layout.
GPT-5 Image Mini is distinct from the full GPT-5 Image profile, which combines GPT-5 with GPT Image 1. Use the Mini profile for bounded language-and-visual tasks whose brief and review criteria are clear. Check composition, object identity, image text and supporting prose, then request the specific correction required. Keep the successful elements and the relevant references in the next brief so a revision has a concrete target.
", "aboutText": "GPT-5 Image Mini is a combined language-and-image profile built around OpenAI’s GPT-5 Mini and GPT Image 1 Mini. Its language component interprets a written task and source material, while its image component creates or revises visual content. The published profile emphasizes instruction following, text rendering and detailed image editing. It is useful for a focused visual brief that also needs textual interpretation, explanation or supporting copy.\n\n### Bring the written task and visual result together\n\nPrepare a brief with the intended asset, subject, composition and required text. Describe how the image will be used, such as a product concept, an illustration accompanying a short explanation or a visual draft for a page. Ask for the written and visual deliverables explicitly so an explanation of the concept is easy to distinguish from the image itself.\n\nFor example, supply a description of a reusable water bottle and request a simple product-scene concept with a short caption. Specify the visible bottle shape, background, light direction and the exact words that belong in the visual. Keep facts used in the caption separate from decorative details in the scene. Review the returned image and written text against those two parts of the brief.\n\nThe documented combined profile accepts text, images and files such as PDFs. A document can provide requirements or facts to interpret before creating the visual. An image can supply appearance, layout or an editing source. State each source’s role rather than assume that every attachment should affect every part of the result. Keep source names and ordering clear when several inputs are supplied.\n\n### Generate and revise with references\n\nFor a new image, specify what should be visible and how the parts relate to one another. For a reference-led edit, identify the change and the details to preserve. A product scene can request a new setting while retaining the item’s recognizable form and visible label. A diagram can ask for a corrected relationship while naming the text and elements that should stay.\n\nThe profile’s own published card permits up to sixteen reference images for editing. Choose references that contribute to the task and explain their purpose: the product, character, background or visual treatment. Several references can contain conflicting lighting or composition, so name the intended result clearly. Inspect subject details, lettering and the boundaries of a requested change in the actual returned image.\n\nText rendering is a documented capability, but a visual draft still needs a word-by-word check. Provide exact wording, punctuation and placement, then compare important labels and numbers with the brief at the intended viewing size. For a repeated series, retain the references and visual requirements with each result and compare the completed images together.\n\n### Working context and controls\n\nThe combined profile publishes a 400,000-token context window and up to 128,000 completion tokens. These are working-context and completion allowances, separate from an image’s pixel dimensions or the number of returned image artifacts. Organize long source material around the required interpretation and leave room for the response. A compact brief with relevant references is more useful than unrelated documents whose role has not been explained.\n\nIts published text-output contract supports JSON-schema structured results. Define the fields and types when a written result needs to be consumed by an application, and validate the values against the supplied evidence. The combined profile does not expose application function calling. Its use of a language component with image generation is a named profile capability; it does not make every feature of a standalone GPT-5 Mini interface part of this workflow.\n\nImage settings should come from the exact profile and interface being used. Keep the selected configuration with the prompt when comparing revisions. The direct GPT Image 1 Mini component and a combined chat profile have different request arrangements, so component-specific size, masking or output controls need an explicit mapping before they are treated as settings of this combined profile.\n\n### Keep the text and image artifacts\n\nThe documented chat request schema can select text and image as output modalities. Process the returned text and every actual image artifact that accompanies the completion. Save the images in the format identified by the result and retain the written response with the prompt, source references and settings. A completion containing explanatory text alone is different from a completed visual deliverable, so check what the result actually contains before using it in a layout.\n\nGPT-5 Image Mini is distinct from the full GPT-5 Image profile, which combines GPT-5 with GPT Image 1. Use the Mini profile for bounded language-and-visual tasks whose brief and review criteria are clear. Check composition, object identity, image text and supporting prose, then request the specific correction required. Keep the successful elements and the relevant references in the next brief so a revision has a concrete target.", "editorialOverlay": { "copy_fields": { "practical_html": "Technical example in English
Create a labelled refill-bottle scene\n\nCreate a product-scene image of a plain cylindrical blue reusable water bottle with a silver screw cap, standing upright on pale stone. Soft daylight enters from the left. Place the exact word REFILL clearly on the bottle, with no other text, logos, accessories or unsupported product claims. Keep the full bottle visible.One image artifact showing the full specified bottle, left-side daylight and the exact single-word label.Inspect the actual image for bottle shape, cap, complete framing and letter-by-letter spelling of REFILL.openai/gpt-5-image-miniGPT-5 Image Mini combines OpenAI’s GPT-5 Mini language capabilities with GPT Image 1 Mini for visual generation and reference-led edits. It accepts text, images and documents and returns text and images, supporting workflows that need both a written response and a visual asset.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
GPT-5 Image Mini is a combined language-and-image profile built around OpenAI’s GPT-5 Mini and GPT Image 1 Mini. Its language component interprets a written task and source material, while its image component creates or revises visual content. The published profile emphasizes instruction following, text rendering and detailed image editing. It is useful for a focused visual brief that also needs textual interpretation, explanation or supporting copy.
Prepare a brief with the intended asset, subject, composition and required text. Describe how the image will be used, such as a product concept, an illustration accompanying a short explanation or a visual draft for a page. Ask for the written and visual deliverables explicitly so an explanation of the concept is easy to distinguish from the image itself.
For example, supply a description of a reusable water bottle and request a simple product-scene concept with a short caption. Specify the visible bottle shape, background, light direction and the exact words that belong in the visual. Keep facts used in the caption separate from decorative details in the scene. Review the returned image and written text against those two parts of the brief.
The documented combined profile accepts text, images and files such as PDFs. A document can provide requirements or facts to interpret before creating the visual. An image can supply appearance, layout or an editing source. State each source’s role rather than assume that every attachment should affect every part of the result. Keep source names and ordering clear when several inputs are supplied.
For a new image, specify what should be visible and how the parts relate to one another. For a reference-led edit, identify the change and the details to preserve. A product scene can request a new setting while retaining the item’s recognizable form and visible label. A diagram can ask for a corrected relationship while naming the text and elements that should stay.
The profile’s own published card permits up to sixteen reference images for editing. Choose references that contribute to the task and explain their purpose: the product, character, background or visual treatment. Several references can contain conflicting lighting or composition, so name the intended result clearly. Inspect subject details, lettering and the boundaries of a requested change in the actual returned image.
Text rendering is a documented capability, but a visual draft still needs a word-by-word check. Provide exact wording, punctuation and placement, then compare important labels and numbers with the brief at the intended viewing size. For a repeated series, retain the references and visual requirements with each result and compare the completed images together.
The combined profile publishes a 400,000-token context window and up to 128,000 completion tokens. These are working-context and completion allowances, separate from an image’s pixel dimensions or the number of returned image artifacts. Organize long source material around the required interpretation and leave room for the response. A compact brief with relevant references is more useful than unrelated documents whose role has not been explained.
Its published text-output contract supports JSON-schema structured results. Define the fields and types when a written result needs to be consumed by an application, and validate the values against the supplied evidence. The combined profile does not expose application function calling. Its use of a language component with image generation is a named profile capability; it does not make every feature of a standalone GPT-5 Mini interface part of this workflow.
Image settings should come from the exact profile and interface being used. Keep the selected configuration with the prompt when comparing revisions. The direct GPT Image 1 Mini component and a combined chat profile have different request arrangements, so component-specific size, masking or output controls need an explicit mapping before they are treated as settings of this combined profile.
The documented chat request schema can select text and image as output modalities. Process the returned text and every actual image artifact that accompanies the completion. Save the images in the format identified by the result and retain the written response with the prompt, source references and settings. A completion containing explanatory text alone is different from a completed visual deliverable, so check what the result actually contains before using it in a layout.
GPT-5 Image Mini is distinct from the full GPT-5 Image profile, which combines GPT-5 with GPT Image 1. Use the Mini profile for bounded language-and-visual tasks whose brief and review criteria are clear. Check composition, object identity, image text and supporting prose, then request the specific correction required. Keep the successful elements and the relevant references in the next brief so a revision has a concrete target.
POST /v1/chat/completionstexttext| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits openai/gpt-5-image-mini | Default— |
Fieldmessages | Typemessage[] | RequirementRequired | Values & limits Minimum items: 1 | Default— |
Fieldmessages[].role | Typestring | RequirementRequired | Values & limits Examples: system, user, assistant, tool, function Conditions: These roles are explicitly represented by Source compatibility types; the exact model role set remains model-specific. Model-specific | Default— |
Fieldmessages[].content | Typestring|content_part[]|null | RequirementOptional | Values & limits | Default— |
Fieldmessages[].tool_calls | Typetool_call[] | RequirementOptional | Values & limits | Default— |
Fieldmessages[].tool_call_id | Typestring | RequirementOptional | Values & limits Applies when: role=tool; use actual returned ID | Default— |
Fieldmessages[].name | Typestring | RequirementOptional | Values & limits | Default— |
Fieldmessages[].reasoning_content | Typestring | RequirementOptional | Values & limits Applies when: Round-trip actual returned reasoning where this model uses it | Default— |
Fieldmessages[].reasoning_details | Typeobject|array | RequirementOptional | Values & limits Model-specific | Default— |
Fieldmax_tokens | Typeinteger | RequirementOptional | Values & limits Minimum: 1 Model-specific | Default— |
Fieldmax_completion_tokens | Typeinteger | RequirementOptional | Values & limits Minimum: 1 Model-specific | Default— |
Fieldstream | Typeboolean | RequirementOptional | Values & limits | Defaultfalse |
Fieldstream_options.include_usage | Typeboolean | RequirementOptional | Values & limits Model-specific | Default— |
Fieldtemperature | Typenumber | RequirementOptional | Values & limits Model-specific | Default— |
Fieldtop_p | Typenumber | RequirementOptional | Values & limits Model-specific | Default— |
Fieldstop | Typestring|string[] | RequirementOptional | Values & limits Model-specific | Default— |
Fieldtools | Typefunction_tool[] | RequirementOptional | Values & limits Model-specific | Default— |
Fieldtools[].type | Typestring | RequirementOptional | Values & limits function Model-specific | Default— |
Fieldtools[].function.name | Typestring | RequirementRequired | Values & limits Applies when: function tool supplied | Default— |
Fieldtools[].function.description | Typestring | RequirementOptional | Values & limits | Default— |
Fieldtools[].function.parameters | TypeJSON_schema | RequirementOptional | Values & limits | Default— |
Fieldtool_choice | Typestring|object | RequirementOptional | Values & limits Model-specific | Default— |
Fieldparallel_tool_calls | Typeboolean | RequirementOptional | Values & limits Model-specific | Default— |
Fieldresponse_format | Typeobject | RequirementOptional | Values & limits Model-specific | Default— |
Fieldreasoning_effort | Typestring | RequirementOptional | Values & limits Model-specific | Default— |
Fieldreasoning | Typeobject | RequirementOptional | Values & limits Model-specific | Default— |
Fieldservice_tier | Typestring | RequirementOptional | Values & limits Model-specific | Default— |
Fieldinstructions | Typestring | RequirementOptional | Values & limits Compatibility field | Default— |
Fieldfunctions | Typefunction[] | RequirementOptional | Values & limits Model-specific | Default— |
Fieldfunction_call | Typestring|object | RequirementOptional | Values & limits Model-specific | Default— |
Fieldprompt_cache_options | Typeobject | RequirementOptional | Values & limits Model-specific | Default— |
Fieldmodalities | Typestring[] | RequirementOptional | Values & limits text | Default— |
Fieldprovider | Typeobject | RequirementOptional | Values & limits Conditions: provider.options is rejected on this Ultra text route | Default— |
Fieldcache_control.ttl | Typestring | RequirementOptional | Values & limits Conditions: 1h is rejected in actual prompt-cache locations | Default— |
Text · UTF8 text
messages[].content
| Parameter | Contract |
|---|---|
Parametercontent_type | Contractapplication/json |
Parameterrequired | Contractmodel, messages |
Parametermaterial_types | Contracttext |
Parametermessages | Contractmessage array with role/content shape |
Parameterfile_material | Contractactual UTF-8 document inserted as literal text; no file ID inferred |
Parameteroptional_controls | Contractstream, temperature, top_p, stop, max_tokens, max_completion_tokens, reasoning, response_format, tools, tool_choice |
Parametercontrol_scope | Contractmodel-specific; user function tools differ from hosted tools |
Parameteroutput_modalities | Contracttext |
Parameterhosted_operations | Contractfalse |
Parametermultimodal_input_binding | Contractnull |
Lifecycle: synchronous_or_streamed
Send exact model ID with messages.
Context length is not an output cap.
A text file may supply literal text; image/audio/file-object inputs need exact binding.
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 | Context tokens, more than | Context tokens, up to and including | Charge |
|---|---|---|---|---|
| ConfigurationBase token rates | Rate scheduleBase rate | Context tokens, more than— | Context tokens, up to and includingNo upper bound | Charge Input · Input tokens $0.005 / 1K tokens Output · Output tokens $0.004 / 1K tokens Input · Cache read $0.0005 / 1K tokens Input · Cache write — / 1K tokens Input · Cache write, 1 hour — / 1K tokens Output · Image output tokens $0.016 / 1K image tokens |
| ConfigurationRequest-length tier 1 | Rate schedule— | Context tokens, more than0 | Context tokens, up to and includingNo upper bound | Charge Input · Input tokens $0.005 / 1K tokens Output · Output tokens $0.004 / 1K tokens Input · Cache read $0.0005 / 1K tokens Input · Cache write — / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Model-level reference schedules do not change method selection, quotes or charges. Active charges use the selected Ailoot method rates.
| Configuration | Billing conditions | Charge |
|---|---|---|
| ConfigurationOpenAI: GPT-5 Image Mini · Default configuration | Billing conditionsDefault configuration | Charge Input · Input tokens $0.005 / 1K tokens Output · Output tokens $0.004 / 1K tokens Output · Output image $0.016 / 1K tokens Output · Web search $0.02 per search Input · Cache read $0.0005 / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{"model": "openai/gpt-5-image-mini", "messages": [{"role": "user", "content": "Explain the difference between an observation and a hypothesis in three sentences."}]}')\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/chat/completions \\\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 json\nfrom pathlib import Path\nr=json.loads(Path("result.json").read_text())\nfor x in r.get("choices",[]):\n if x.get("message",{}).get("content"): print(x["message"]["content"])\nPY_RESULT\nfrom openai import OpenAI\nimport os\n\nif not os.environ.get("AILOOT_API_KEY", "").strip(): raise SystemExit("Set AILOOT_API_KEY before making a request")\nclient=OpenAI(base_url="https://api.ailoot.ai/v1",api_key=os.environ["AILOOT_API_KEY"],max_retries=0)\nresult=client.chat.completions.create(model='openai/gpt-5-image-mini',messages=[{"role":"user","content":"Explain observations and hypotheses in three sentences."}])\nimport base64, json\nfrom pathlib import Path\n\ndef save_result(data, stem="result"):\n # Keep every field, including usage, IDs, reasoning and tool requests.\n Path(stem + ".json").write_text(json.dumps(data, ensure_ascii=False, indent=2), encoding="utf-8")\n print(json.dumps(data, ensure_ascii=False, indent=2))\n def save_blob(encoded, suffix, label):\n raw = base64.b64decode(encoded, altchars=b"-_", validate=True)\n ext = "png" if raw.startswith(b"\\x89PNG\\r\\n\\x1a\\n") else "jpg" if raw.startswith(b"\\xff\\xd8") else "webp" if raw[:4] == b"RIFF" and raw[8:12] == b"WEBP" else suffix\n file = Path(stem + "-" + label + "." + ext)\n file.write_bytes(raw)\n print("Saved", file)\n for index, block in enumerate(data.get("content") or []):\n print("Content", index, json.dumps(block, ensure_ascii=False))\n for choice in data.get("choices") or []:\n print("Choice", choice.get("index"), json.dumps(choice.get("message", choice.get("delta", {})), ensure_ascii=False))\n for index, item in enumerate(data.get("output") or []):\n print("Output", index, json.dumps(item, ensure_ascii=False))\n if item.get("type") == "image_generation_call" and item.get("result"):\n save_blob(item["result"], item.get("output_format") or "bin", "output-" + str(index))\n for ci, candidate in enumerate(data.get("candidates") or []):\n for pi, part in enumerate((candidate.get("content") or {}).get("parts") or []):\n print("Candidate/part", ci, pi, json.dumps(part, ensure_ascii=False))\n blob = part.get("inlineData") or part.get("inline_data")\n if blob and blob.get("data"):\n mime = blob.get("mimeType") or blob.get("mime_type")\n suffix = {"image/png":"png", "image/jpeg":"jpg", "image/webp":"webp", "audio/wav":"wav", "audio/mpeg":"mp3", "video/mp4":"mp4"}.get(mime, "bin")\n save_blob(blob["data"], suffix, "candidate-" + str(ci) + "-part-" + str(pi))\n\nsave_result(result.to_dict(mode="json"))\nfrom openai import OpenAI\nimport os\nfrom pathlib import Path\n\nif not os.environ.get("AILOOT_API_KEY", "").strip(): raise SystemExit("Set AILOOT_API_KEY before making a request")\nclient=OpenAI(base_url="https://api.ailoot.ai/v1",api_key=os.environ["AILOOT_API_KEY"],max_retries=0)\nimport base64, json\nfrom pathlib import Path\n\ndef save_result(data, stem="result"):\n # Keep every field, including usage, IDs, reasoning and tool requests.\n Path(stem + ".json").write_text(json.dumps(data, ensure_ascii=False, indent=2), encoding="utf-8")\n print(json.dumps(data, ensure_ascii=False, indent=2))\n def save_blob(encoded, suffix, label):\n raw = base64.b64decode(encoded, altchars=b"-_", validate=True)\n ext = "png" if raw.startswith(b"\\x89PNG\\r\\n\\x1a\\n") else "jpg" if raw.startswith(b"\\xff\\xd8") else "webp" if raw[:4] == b"RIFF" and raw[8:12] == b"WEBP" else suffix\n file = Path(stem + "-" + label + "." + ext)\n file.write_bytes(raw)\n print("Saved", file)\n for index, block in enumerate(data.get("content") or []):\n print("Content", index, json.dumps(block, ensure_ascii=False))\n for choice in data.get("choices") or []:\n print("Choice", choice.get("index"), json.dumps(choice.get("message", choice.get("delta", {})), ensure_ascii=False))\n for index, item in enumerate(data.get("output") or []):\n print("Output", index, json.dumps(item, ensure_ascii=False))\n if item.get("type") == "image_generation_call" and item.get("result"):\n save_blob(item["result"], item.get("output_format") or "bin", "output-" + str(index))\n for ci, candidate in enumerate(data.get("candidates") or []):\n for pi, part in enumerate((candidate.get("content") or {}).get("parts") or []):\n print("Candidate/part", ci, pi, json.dumps(part, ensure_ascii=False))\n blob = part.get("inlineData") or part.get("inline_data")\n if blob and blob.get("data"):\n mime = blob.get("mimeType") or blob.get("mime_type")\n suffix = {"image/png":"png", "image/jpeg":"jpg", "image/webp":"webp", "audio/wav":"wav", "audio/mpeg":"mp3", "video/mp4":"mp4"}.get(mime, "bin")\n save_blob(blob["data"], suffix, "candidate-" + str(ci) + "-part-" + str(pi))\n\nwith client.chat.completions.stream(model='openai/gpt-5-image-mini',messages=[{"role":"user","content":"Explain observations and hypotheses in three sentences."}]) as stream:\n with Path("stream-events.jsonl").open("w",encoding="utf-8") as log:\n for event in stream:\n payload=event.to_dict(mode="json")\n log.write(json.dumps(payload,ensure_ascii=False)+"\\n"); log.flush()\n print(json.dumps(payload,ensure_ascii=False))\n final=stream.get_final_completion()\n save_result(final.to_dict(mode="json"),"stream-result")\njson or SSE
Keep every complete returned response, file and content part.
Keep every choice and its content/tool calls/reasoning. In SSE assemble deltas by choice/tool index, retain the final finish reason and usage.
Keep request/response IDs and headers. No generic text job retrieval URL is supplied.
| Field | Type |
|---|---|
Fieldchoices[].index | Typeinteger |
Fieldchoices[].message.content | Typestring|null |
Fieldchoices[].message.tool_calls | Typetool_call[] |
Fieldchoices[].message.reasoning_content | Typestring |
Fieldchoices[].message.reasoning_details | Typeobject|array |
Fieldchoices[].finish_reason | Typestring|null |
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. |
Ailoot Ultra openai/gpt-5-image-mini
/v1/images
openai/gpt-5-image-miniGPT-5 Image Mini combines OpenAI’s GPT-5 Mini language capabilities with GPT Image 1 Mini for visual generation and reference-led edits. It accepts text, images and documents and returns text and images, supporting workflows that need both a written response and a visual asset.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
GPT-5 Image Mini is a combined language-and-image profile built around OpenAI’s GPT-5 Mini and GPT Image 1 Mini. Its language component interprets a written task and source material, while its image component creates or revises visual content. The published profile emphasizes instruction following, text rendering and detailed image editing. It is useful for a focused visual brief that also needs textual interpretation, explanation or supporting copy.
Prepare a brief with the intended asset, subject, composition and required text. Describe how the image will be used, such as a product concept, an illustration accompanying a short explanation or a visual draft for a page. Ask for the written and visual deliverables explicitly so an explanation of the concept is easy to distinguish from the image itself.
For example, supply a description of a reusable water bottle and request a simple product-scene concept with a short caption. Specify the visible bottle shape, background, light direction and the exact words that belong in the visual. Keep facts used in the caption separate from decorative details in the scene. Review the returned image and written text against those two parts of the brief.
The documented combined profile accepts text, images and files such as PDFs. A document can provide requirements or facts to interpret before creating the visual. An image can supply appearance, layout or an editing source. State each source’s role rather than assume that every attachment should affect every part of the result. Keep source names and ordering clear when several inputs are supplied.
For a new image, specify what should be visible and how the parts relate to one another. For a reference-led edit, identify the change and the details to preserve. A product scene can request a new setting while retaining the item’s recognizable form and visible label. A diagram can ask for a corrected relationship while naming the text and elements that should stay.
The profile’s own published card permits up to sixteen reference images for editing. Choose references that contribute to the task and explain their purpose: the product, character, background or visual treatment. Several references can contain conflicting lighting or composition, so name the intended result clearly. Inspect subject details, lettering and the boundaries of a requested change in the actual returned image.
Text rendering is a documented capability, but a visual draft still needs a word-by-word check. Provide exact wording, punctuation and placement, then compare important labels and numbers with the brief at the intended viewing size. For a repeated series, retain the references and visual requirements with each result and compare the completed images together.
The combined profile publishes a 400,000-token context window and up to 128,000 completion tokens. These are working-context and completion allowances, separate from an image’s pixel dimensions or the number of returned image artifacts. Organize long source material around the required interpretation and leave room for the response. A compact brief with relevant references is more useful than unrelated documents whose role has not been explained.
Its published text-output contract supports JSON-schema structured results. Define the fields and types when a written result needs to be consumed by an application, and validate the values against the supplied evidence. The combined profile does not expose application function calling. Its use of a language component with image generation is a named profile capability; it does not make every feature of a standalone GPT-5 Mini interface part of this workflow.
Image settings should come from the exact profile and interface being used. Keep the selected configuration with the prompt when comparing revisions. The direct GPT Image 1 Mini component and a combined chat profile have different request arrangements, so component-specific size, masking or output controls need an explicit mapping before they are treated as settings of this combined profile.
The documented chat request schema can select text and image as output modalities. Process the returned text and every actual image artifact that accompanies the completion. Save the images in the format identified by the result and retain the written response with the prompt, source references and settings. A completion containing explanatory text alone is different from a completed visual deliverable, so check what the result actually contains before using it in a layout.
GPT-5 Image Mini is distinct from the full GPT-5 Image profile, which combines GPT-5 with GPT Image 1. Use the Mini profile for bounded language-and-visual tasks whose brief and review criteria are clear. Check composition, object identity, image text and supporting prose, then request the specific correction required. Keep the successful elements and the relevant references in the next brief so a revision has a concrete target.
POST /v1/images/generationstext, imageimage| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits openai/gpt-5-image-mini | Default— |
Fieldprompt | Typestring | RequirementRequired | Values & limits Minimum length: 1 | Default— |
Fieldaspect_ratio | Typestring | RequirementOptional | Values & limits 1:1, 3:2, 2:3, auto | Default— |
Fieldquality | Typestring | RequirementOptional | Values & limits auto, low, medium, high | 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 $0.065536 estimate before generation This estimate is not an additional charge or a fixed rate for the completed generation. |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
set -eu\n: "${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-5-image-mini', '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 Ultra openai/gpt-5-image-mini
/v1/images
openai/gpt-5-image-miniGPT-5 Image Mini combines OpenAI’s GPT-5 Mini language capabilities with GPT Image 1 Mini for visual generation and reference-led edits. It accepts text, images and documents and returns text and images, supporting workflows that need both a written response and a visual asset.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
GPT-5 Image Mini is a combined language-and-image profile built around OpenAI’s GPT-5 Mini and GPT Image 1 Mini. Its language component interprets a written task and source material, while its image component creates or revises visual content. The published profile emphasizes instruction following, text rendering and detailed image editing. It is useful for a focused visual brief that also needs textual interpretation, explanation or supporting copy.
Prepare a brief with the intended asset, subject, composition and required text. Describe how the image will be used, such as a product concept, an illustration accompanying a short explanation or a visual draft for a page. Ask for the written and visual deliverables explicitly so an explanation of the concept is easy to distinguish from the image itself.
For example, supply a description of a reusable water bottle and request a simple product-scene concept with a short caption. Specify the visible bottle shape, background, light direction and the exact words that belong in the visual. Keep facts used in the caption separate from decorative details in the scene. Review the returned image and written text against those two parts of the brief.
The documented combined profile accepts text, images and files such as PDFs. A document can provide requirements or facts to interpret before creating the visual. An image can supply appearance, layout or an editing source. State each source’s role rather than assume that every attachment should affect every part of the result. Keep source names and ordering clear when several inputs are supplied.
For a new image, specify what should be visible and how the parts relate to one another. For a reference-led edit, identify the change and the details to preserve. A product scene can request a new setting while retaining the item’s recognizable form and visible label. A diagram can ask for a corrected relationship while naming the text and elements that should stay.
The profile’s own published card permits up to sixteen reference images for editing. Choose references that contribute to the task and explain their purpose: the product, character, background or visual treatment. Several references can contain conflicting lighting or composition, so name the intended result clearly. Inspect subject details, lettering and the boundaries of a requested change in the actual returned image.
Text rendering is a documented capability, but a visual draft still needs a word-by-word check. Provide exact wording, punctuation and placement, then compare important labels and numbers with the brief at the intended viewing size. For a repeated series, retain the references and visual requirements with each result and compare the completed images together.
The combined profile publishes a 400,000-token context window and up to 128,000 completion tokens. These are working-context and completion allowances, separate from an image’s pixel dimensions or the number of returned image artifacts. Organize long source material around the required interpretation and leave room for the response. A compact brief with relevant references is more useful than unrelated documents whose role has not been explained.
Its published text-output contract supports JSON-schema structured results. Define the fields and types when a written result needs to be consumed by an application, and validate the values against the supplied evidence. The combined profile does not expose application function calling. Its use of a language component with image generation is a named profile capability; it does not make every feature of a standalone GPT-5 Mini interface part of this workflow.
Image settings should come from the exact profile and interface being used. Keep the selected configuration with the prompt when comparing revisions. The direct GPT Image 1 Mini component and a combined chat profile have different request arrangements, so component-specific size, masking or output controls need an explicit mapping before they are treated as settings of this combined profile.
The documented chat request schema can select text and image as output modalities. Process the returned text and every actual image artifact that accompanies the completion. Save the images in the format identified by the result and retain the written response with the prompt, source references and settings. A completion containing explanatory text alone is different from a completed visual deliverable, so check what the result actually contains before using it in a layout.
GPT-5 Image Mini is distinct from the full GPT-5 Image profile, which combines GPT-5 with GPT Image 1. Use the Mini profile for bounded language-and-visual tasks whose brief and review criteria are clear. Check composition, object identity, image text and supporting prose, then request the specific correction required. Keep the successful elements and the relevant references in the next brief so a revision has a concrete target.
POST /v1/messagestextjson, sse| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits openai/gpt-5-image-mini | Default— |
Fieldmessages | Typemessage[] | RequirementRequired | Values & limits | Default— |
Fieldmessages[].role | Typestring | RequirementRequired | Values & limits user, assistant | Default— |
Fieldmessages[].content | Typestring | RequirementRequired | Values & limits | Default— |
Fieldmax_tokens | Typeinteger | RequirementRequired | Values & limits | Default— |
Fieldstream | Typeboolean | RequirementOptional | Values & limits | Defaultfalse |
Text · UTF-8 text
messages[].content string
| Parameter | Contract |
|---|---|
Parametercontent_type | Contractapplication/json |
Parameterrequired | Contractmodel, messages, max_tokens |
Parameteroptional_controls | Contractstream |
Parametermaterial_types | Contracttext |
Parametertext_file_material | ContractRead the actual UTF-8 document and put its text in the indicated input or message content string. |
Lifecycle: synchronous_or_streamed
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 | Context tokens, more than | Context tokens, up to and including | Charge |
|---|---|---|---|---|
| ConfigurationBase token rates | Rate scheduleBase rate | Context tokens, more than— | Context tokens, up to and includingNo upper bound | Charge Input · Input tokens $0.005 / 1K tokens Output · Output tokens $0.004 / 1K tokens Input · Cache read $0.0005 / 1K tokens Input · Cache write — / 1K tokens Input · Cache write, 1 hour — / 1K tokens Output · Image output tokens $0.016 / 1K image tokens |
| ConfigurationRequest-length tier 1 | Rate schedule— | Context tokens, more than0 | Context tokens, up to and includingNo upper bound | Charge Input · Input tokens $0.005 / 1K tokens Output · Output tokens $0.004 / 1K tokens Input · Cache read $0.0005 / 1K tokens Input · Cache write — / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
{\n "model": "openai/gpt-5-image-mini",\n "max_tokens": 128,\n "messages": [\n {\n "role": "user",\n "content": "Summarize the incident report."\n }\n ],\n "stream": false\n}set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\n: "${AILOOT_TEXT_FILE:?Set your actual UTF-8 document}"\nexport AILOOT_TEXT_FILE\npython3 - <<'PY_REQUEST'\nimport json, os\nfrom pathlib import Path\n\nsource_file = Path(os.environ['AILOOT_TEXT_FILE'])\nif not source_file.is_file():\n raise SystemExit('Set AILOOT_TEXT_FILE to your actual UTF-8 document')\ntext = source_file.read_text(encoding='utf-8')\nif not text.strip():\n raise SystemExit('Choose a document containing text')\npayload = {'model': 'openai/gpt-5-image-mini', 'max_tokens': 128, 'messages': [{'role': 'user', 'content': 'Summarize the incident report.'}]}\npayload['messages'][0]['content'] = 'Summarize this material and quote supporting source passages.\\n\\n' + text\nPath('request.json').write_text(json.dumps(payload, ensure_ascii=False), encoding='utf-8')\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/messages \\\n -H "x-api-key: $AILOOT_API_KEY" -H 'anthropic-version: 2023-06-01' -H 'Content-Type: application/json' \\\n --data-binary @request.json -D response-headers.txt -o result.json\npython3 - <<'PY_RESULT'\nimport json\nfrom pathlib import Path\n\nresult = json.loads(Path('result.json').read_text(encoding='utf-8'))\nprint(json.dumps(result, ensure_ascii=False, indent=2))\nPY_RESULT\nimport json, os\nfrom pathlib import Path\n\nsource_file = Path(os.environ['AILOOT_TEXT_FILE'])\nif not source_file.is_file():\n raise SystemExit('Set AILOOT_TEXT_FILE to your actual UTF-8 document')\ntext = source_file.read_text(encoding='utf-8')\nif not text.strip():\n raise SystemExit('Choose a document containing text')\npayload = {'model': 'openai/gpt-5-image-mini', 'max_tokens': 128, 'messages': [{'role': 'user', 'content': 'Summarize the incident report.'}]}\npayload['messages'][0]['content'] = 'Summarize this material and quote supporting source passages.\\n\\n' + text\nfrom urllib.request import Request, urlopen\nfrom urllib.error import HTTPError\n\napi_key = os.environ['AILOOT_API_KEY']\nif not api_key.strip():\n raise SystemExit('Set AILOOT_API_KEY before making a request')\nheaders = {'x-api-key': 'PLACEHOLDER_KEY', 'anthropic-version': '2023-06-01', 'Content-Type': 'application/json'}\nheaders['x-api-key'] = api_key\nrequest = Request('https://api.ailoot.ai/v1/messages', data=json.dumps(payload, ensure_ascii=False).encode('utf-8'), headers=headers, method='POST')\ntry:\n with urlopen(request, timeout=120) as response:\n raw = response.read()\n Path('response-headers.txt').write_text(str(response.headers), encoding='utf-8')\nexcept HTTPError as error:\n Path('result.json').write_bytes(error.read())\n Path('response-headers.txt').write_text(str(error.headers), encoding='utf-8')\n raise\nPath('result.json').write_bytes(raw)\nimport json\nfrom pathlib import Path\n\nresult = json.loads(Path('result.json').read_text(encoding='utf-8'))\nprint(json.dumps(result, ensure_ascii=False, indent=2))\nJSON or SSE
Keep every complete returned response, file and content part.
Keep all content blocks, thinking signatures and actual tool-use IDs. Assemble all SSE block deltas; a first text block is not the complete output.
Keep request/response IDs and headers. No generic text job retrieval URL is supplied.
| Field | Type |
|---|---|
Fieldid | Typestring |
Fieldmodel | Typestring |
Fieldcontent[] | Typetext|thinking|tool_use|other content block |
Fieldstop_reason | Typestring|null |
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. |
Ailoot Ultra openai/gpt-5-image-mini
/v1/images
Technical example in English
Create a labelled refill-bottle scene\n\nCreate a product-scene image of a plain cylindrical blue reusable water bottle with a silver screw cap, standing upright on pale stone. Soft daylight enters from the left. Place the exact word REFILL clearly on the bottle, with no other text, logos, accessories or unsupported product claims. Keep the full bottle visible.One image artifact showing the full specified bottle, left-side daylight and the exact single-word label.Inspect the actual image for bottle shape, cap, complete framing and letter-by-letter spelling of REFILL.