GPT-5.3 Chat identifies the GPT-5.3 Instant snapshot associated with ChatGPT. OpenAI describes this Instant update around the everyday qualities of a conversation: relevance, useful answers and a smoother flow. The model accepts text and images and produces text, keeping a chat-focused identity separate from other GPT-5.3 variants.
The release first addresses response judgment. OpenAI reports fewer unnecessary refusals and fewer defensive or moralizing introductions before an answer. The intended behavior is to respond directly when a useful answer is appropriate and stay focused on the question. In a practical assistant, this supports explanations and task-focused responses without turning every request into a long preamble.
When web information is available to the surrounding product, the release emphasizes synthesis rather than a loosely connected list of retrieved pages. GPT-5.3 Instant is described as combining online findings with its existing understanding, identifying the important point and adding context. This is a description of how it handles supplied or retrieved information; the application determines which search tools and source materials are available.
Conversational style and writing are separate areas of the update. OpenAI describes a more focused, natural tone and greater range in expressive prose, from drafting fiction to refining a passage or developing an idea. Its factuality work is also reported as reducing hallucinations across varied topics. The release presents these as improvements in the model’s response behavior across factual and creative tasks.
The release records a concrete language limitation: in some languages, including Japanese and Korean, the response style can remain stilted or overly literal. Tone and naturalness continue to be areas of development. For multilingual work, identify the audience, terminology and desired register explicitly; a writing task can distinguish between factual content to preserve and the expressive choices to revise.
The model specification lists a 128,000-token context window, up to 16,384 output tokens and a knowledge cutoff of August 31, 2025. Text and images are supported inputs, and text is the output. Streaming, function calling and Structured Outputs are supported, while audio and video input, fine-tuning and predicted outputs are not part of the published configuration.
Function calling lets a connected application provide actions and data: the model selects a function and supplies arguments, the application executes it and returns the result. Structured output organizes the response itself into agreed fields. A screenshot, diagram or document image can add visual context to a text question, with source legibility and clear identification of relevant details helping the interpretation.
For an assistant using Ailoot API, define the artifact you need: a concise answer, revised document, visual explanation or structured set of facts. Provide the original material and the requirements that should shape it. The Instant chat label remains explicit, and follow-up messages can refine tone, focus or structure while keeping the source material and original requirements in view.
", "aboutText": "GPT-5.3 Chat identifies the GPT-5.3 Instant snapshot associated with ChatGPT. OpenAI describes this Instant update around the everyday qualities of a conversation: relevance, useful answers and a smoother flow. The model accepts text and images and produces text, keeping a chat-focused identity separate from other GPT-5.3 variants.\n\nThe release first addresses response judgment. OpenAI reports fewer unnecessary refusals and fewer defensive or moralizing introductions before an answer. The intended behavior is to respond directly when a useful answer is appropriate and stay focused on the question. In a practical assistant, this supports explanations and task-focused responses without turning every request into a long preamble.\n\nWhen web information is available to the surrounding product, the release emphasizes synthesis rather than a loosely connected list of retrieved pages. GPT-5.3 Instant is described as combining online findings with its existing understanding, identifying the important point and adding context. This is a description of how it handles supplied or retrieved information; the application determines which search tools and source materials are available.\n\nConversational style and writing are separate areas of the update. OpenAI describes a more focused, natural tone and greater range in expressive prose, from drafting fiction to refining a passage or developing an idea. Its factuality work is also reported as reducing hallucinations across varied topics. The release presents these as improvements in the model’s response behavior across factual and creative tasks.\n\nThe release records a concrete language limitation: in some languages, including Japanese and Korean, the response style can remain stilted or overly literal. Tone and naturalness continue to be areas of development. For multilingual work, identify the audience, terminology and desired register explicitly; a writing task can distinguish between factual content to preserve and the expressive choices to revise.\n\nThe model specification lists a 128,000-token context window, up to 16,384 output tokens and a knowledge cutoff of August 31, 2025. Text and images are supported inputs, and text is the output. Streaming, function calling and Structured Outputs are supported, while audio and video input, fine-tuning and predicted outputs are not part of the published configuration.\n\nFunction calling lets a connected application provide actions and data: the model selects a function and supplies arguments, the application executes it and returns the result. Structured output organizes the response itself into agreed fields. A screenshot, diagram or document image can add visual context to a text question, with source legibility and clear identification of relevant details helping the interpretation.\n\nFor an assistant using Ailoot API, define the artifact you need: a concise answer, revised document, visual explanation or structured set of facts. Provide the original material and the requirements that should shape it. The Instant chat label remains explicit, and follow-up messages can refine tone, focus or structure while keeping the source material and original requirements in view.", "editorialOverlay": { "copy_fields": { "practical_html": "Technical example in English
A natural follow-up message\n\nDraft a friendly follow-up to a colleague who missed a planning meeting. Facts: we chose the blue packaging sample, kept the existing launch date, and need their feedback on the label by Thursday. Use about 60 words, with a direct opening and one clear request. Keep the tone warm without inventing a reason for their absence.One concise message containing the two decisions and the Thursday feedback request.The launch date stays unchanged, and no explanation of the colleague's absence is invented.gpt-5.3-chat-latestGPT-5.3 Chat is OpenAI’s GPT-5.3 Instant variant for direct, conversational text responses informed by images. It focuses on relevance, factuality and stronger writing, with function calling, structured outputs and a 128,000-token context.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
GPT-5.3 Chat identifies the GPT-5.3 Instant snapshot associated with ChatGPT. OpenAI describes this Instant update around the everyday qualities of a conversation: relevance, useful answers and a smoother flow. The model accepts text and images and produces text, keeping a chat-focused identity separate from other GPT-5.3 variants.
The release first addresses response judgment. OpenAI reports fewer unnecessary refusals and fewer defensive or moralizing introductions before an answer. The intended behavior is to respond directly when a useful answer is appropriate and stay focused on the question. In a practical assistant, this supports explanations and task-focused responses without turning every request into a long preamble.
When web information is available to the surrounding product, the release emphasizes synthesis rather than a loosely connected list of retrieved pages. GPT-5.3 Instant is described as combining online findings with its existing understanding, identifying the important point and adding context. This is a description of how it handles supplied or retrieved information; the application determines which search tools and source materials are available.
Conversational style and writing are separate areas of the update. OpenAI describes a more focused, natural tone and greater range in expressive prose, from drafting fiction to refining a passage or developing an idea. Its factuality work is also reported as reducing hallucinations across varied topics. The release presents these as improvements in the model’s response behavior across factual and creative tasks.
The release records a concrete language limitation: in some languages, including Japanese and Korean, the response style can remain stilted or overly literal. Tone and naturalness continue to be areas of development. For multilingual work, identify the audience, terminology and desired register explicitly; a writing task can distinguish between factual content to preserve and the expressive choices to revise.
The model specification lists a 128,000-token context window, up to 16,384 output tokens and a knowledge cutoff of August 31, 2025. Text and images are supported inputs, and text is the output. Streaming, function calling and Structured Outputs are supported, while audio and video input, fine-tuning and predicted outputs are not part of the published configuration.
Function calling lets a connected application provide actions and data: the model selects a function and supplies arguments, the application executes it and returns the result. Structured output organizes the response itself into agreed fields. A screenshot, diagram or document image can add visual context to a text question, with source legibility and clear identification of relevant details helping the interpretation.
For an assistant using Ailoot API, define the artifact you need: a concise answer, revised document, visual explanation or structured set of facts. Provide the original material and the requirements that should shape it. The Instant chat label remains explicit, and follow-up messages can refine tone, focus or structure while keeping the source material and original requirements in view.
POST /v1/chat/completionstexttext| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits gpt-5.3-chat-latest | 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.
| Configuration | Rate schedule | Charge |
|---|---|---|
| ConfigurationBase token rates | Rate scheduleBase rate | Charge Input · Input tokens $0.0035 / 1K tokens Output · Output tokens $0.028 / 1K tokens Input · Cache read $0 / 1K tokens Input · Cache write $0 / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{"model": "gpt-5.3-chat-latest", "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='gpt-5.3-chat-latest',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='gpt-5.3-chat-latest',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 gpt-5.3-chat-latest
Use the method paths shown above.
gpt-5.3-chat-latestGPT-5.3 Chat is OpenAI’s GPT-5.3 Instant variant for direct, conversational text responses informed by images. It focuses on relevance, factuality and stronger writing, with function calling, structured outputs and a 128,000-token context.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
GPT-5.3 Chat identifies the GPT-5.3 Instant snapshot associated with ChatGPT. OpenAI describes this Instant update around the everyday qualities of a conversation: relevance, useful answers and a smoother flow. The model accepts text and images and produces text, keeping a chat-focused identity separate from other GPT-5.3 variants.
The release first addresses response judgment. OpenAI reports fewer unnecessary refusals and fewer defensive or moralizing introductions before an answer. The intended behavior is to respond directly when a useful answer is appropriate and stay focused on the question. In a practical assistant, this supports explanations and task-focused responses without turning every request into a long preamble.
When web information is available to the surrounding product, the release emphasizes synthesis rather than a loosely connected list of retrieved pages. GPT-5.3 Instant is described as combining online findings with its existing understanding, identifying the important point and adding context. This is a description of how it handles supplied or retrieved information; the application determines which search tools and source materials are available.
Conversational style and writing are separate areas of the update. OpenAI describes a more focused, natural tone and greater range in expressive prose, from drafting fiction to refining a passage or developing an idea. Its factuality work is also reported as reducing hallucinations across varied topics. The release presents these as improvements in the model’s response behavior across factual and creative tasks.
The release records a concrete language limitation: in some languages, including Japanese and Korean, the response style can remain stilted or overly literal. Tone and naturalness continue to be areas of development. For multilingual work, identify the audience, terminology and desired register explicitly; a writing task can distinguish between factual content to preserve and the expressive choices to revise.
The model specification lists a 128,000-token context window, up to 16,384 output tokens and a knowledge cutoff of August 31, 2025. Text and images are supported inputs, and text is the output. Streaming, function calling and Structured Outputs are supported, while audio and video input, fine-tuning and predicted outputs are not part of the published configuration.
Function calling lets a connected application provide actions and data: the model selects a function and supplies arguments, the application executes it and returns the result. Structured output organizes the response itself into agreed fields. A screenshot, diagram or document image can add visual context to a text question, with source legibility and clear identification of relevant details helping the interpretation.
For an assistant using Ailoot API, define the artifact you need: a concise answer, revised document, visual explanation or structured set of facts. Provide the original material and the requirements that should shape it. The Instant chat label remains explicit, and follow-up messages can refine tone, focus or structure while keeping the source material and original requirements in view.
POST /v1/messagestextjson, sse| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits gpt-5.3-chat-latest | 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.
| Configuration | Rate schedule | Charge |
|---|---|---|
| ConfigurationBase token rates | Rate scheduleBase rate | Charge Input · Input tokens $0.0035 / 1K tokens Output · Output tokens $0.028 / 1K tokens Input · Cache read $0 / 1K tokens Input · Cache write $0 / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
{\n "model": "gpt-5.3-chat-latest",\n "max_tokens": 128,\n "messages": [\n {\n "role": "user",\n "content": "Summarize the incident report."\n }\n ],\n "stream": false\n}set -eu\n: "${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': 'gpt-5.3-chat-latest', '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': 'gpt-5.3-chat-latest', '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 gpt-5.3-chat-latest
Use the method paths shown above.
Technical example in English
A natural follow-up message\n\nDraft a friendly follow-up to a colleague who missed a planning meeting. Facts: we chose the blue packaging sample, kept the existing launch date, and need their feedback on the label by Thursday. Use about 60 words, with a direct opening and one clear request. Keep the tone warm without inventing a reason for their absence.One concise message containing the two decisions and the Thursday feedback request.The launch date stays unchanged, and no explanation of the colleague's absence is invented.