GPT-5.1 Chat identifies the GPT-5.1 chat snapshot associated with ChatGPT. OpenAI’s release maps the Instant variant to this chat label and distinguishes it from GPT-5.1 Thinking. Instant is designed for everyday conversation while being able to spend additional reasoning on more challenging questions. The card keeps the chat variant’s identity and specification separate from the Thinking model.
The release begins with conversational quality: a warmer default tone, more natural exchanges and improved adherence to the user’s instructions. Those qualities are useful when an assistant needs to answer a question directly, revise an explanation after feedback or maintain an appropriate style for its audience. The practical task remains specific: explain the supplied material, prepare a response or refine a selected passage.
Adaptive reasoning is a notable part of the Instant update. OpenAI describes the model deciding when a harder question needs more thinking before the answer, with improvements in mathematical and coding tasks. This is a model behavior rather than a claim that every chat request uses the same fixed reasoning process. The release’s separate description of GPT-5.1 Thinking belongs to that other variant.
The manufacturer also emphasizes customization and more faithful handling of custom instructions. An application can express the desired role, tone, response length and required details in its task instructions. For example, a support assistant may need a concise professional answer, while an educational explanation may need simpler wording and explicit intermediate steps. ChatGPT’s product-level personality settings are distinct from the instructions supplied by another application.
This chat model accepts text and images and returns text. Its published context window is 128,000 tokens, maximum output is 16,384 tokens and documented knowledge cutoff is September 30, 2024. Streaming, function calling and Structured Outputs are supported. Audio and video input, fine-tuning and predicted outputs are not supported in the published configuration. These limits describe GPT-5.1 Chat rather than another member of the family.
Functions and structured responses support different integration goals. A function call supplies an action name and arguments for the application to execute, after which the resulting information can return to the conversation. A structured response can organize the final material into agreed fields. When images are involved, the same task can ask for an explanation of visible content; clear, legible source material is especially important for small text and detailed visual relationships.
For an assistant using Ailoot API, give the model the inquiry context and a clear completion criterion. Request an artifact such as an edited document, a client response, an explanation of a screenshot or a structured summary. Preserve the source information and specify how to handle missing details. Follow-up messages can then adjust one part of the result while keeping the conversation focused on the user’s original task.
", "aboutText": "GPT-5.1 Chat identifies the GPT-5.1 chat snapshot associated with ChatGPT. OpenAI’s release maps the Instant variant to this chat label and distinguishes it from GPT-5.1 Thinking. Instant is designed for everyday conversation while being able to spend additional reasoning on more challenging questions. The card keeps the chat variant’s identity and specification separate from the Thinking model.\n\nThe release begins with conversational quality: a warmer default tone, more natural exchanges and improved adherence to the user’s instructions. Those qualities are useful when an assistant needs to answer a question directly, revise an explanation after feedback or maintain an appropriate style for its audience. The practical task remains specific: explain the supplied material, prepare a response or refine a selected passage.\n\nAdaptive reasoning is a notable part of the Instant update. OpenAI describes the model deciding when a harder question needs more thinking before the answer, with improvements in mathematical and coding tasks. This is a model behavior rather than a claim that every chat request uses the same fixed reasoning process. The release’s separate description of GPT-5.1 Thinking belongs to that other variant.\n\nThe manufacturer also emphasizes customization and more faithful handling of custom instructions. An application can express the desired role, tone, response length and required details in its task instructions. For example, a support assistant may need a concise professional answer, while an educational explanation may need simpler wording and explicit intermediate steps. ChatGPT’s product-level personality settings are distinct from the instructions supplied by another application.\n\nThis chat model accepts text and images and returns text. Its published context window is 128,000 tokens, maximum output is 16,384 tokens and documented knowledge cutoff is September 30, 2024. Streaming, function calling and Structured Outputs are supported. Audio and video input, fine-tuning and predicted outputs are not supported in the published configuration. These limits describe GPT-5.1 Chat rather than another member of the family.\n\nFunctions and structured responses support different integration goals. A function call supplies an action name and arguments for the application to execute, after which the resulting information can return to the conversation. A structured response can organize the final material into agreed fields. When images are involved, the same task can ask for an explanation of visible content; clear, legible source material is especially important for small text and detailed visual relationships.\n\nFor an assistant using Ailoot API, give the model the inquiry context and a clear completion criterion. Request an artifact such as an edited document, a client response, an explanation of a screenshot or a structured summary. Preserve the source information and specify how to handle missing details. Follow-up messages can then adjust one part of the result while keeping the conversation focused on the user’s original task.", "editorialOverlay": { "copy_fields": { "practical_html": "Technical example in English
Explain a password-reset rule clearly\n\nExplain this policy to a nontechnical customer in under 90 words: a reset link expires after 30 minutes; requesting another link invalidates earlier links; changing the password signs out other sessions. The customer requested two links and opened the first. Explain what to do next, using a warm, direct tone and no invented account details.One concise customer explanation and a clear next step grounded in the supplied policy.The reply explains why the first link fails and directs the customer to the newest unexpired link.gpt-5.1-chat-latestGPT-5.1 Chat is OpenAI’s Instant chat variant with adaptive reasoning for harder questions. It combines text and image input, a more conversational style, instruction following, function calling and structured responses in 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-5.1 Chat identifies the GPT-5.1 chat snapshot associated with ChatGPT. OpenAI’s release maps the Instant variant to this chat label and distinguishes it from GPT-5.1 Thinking. Instant is designed for everyday conversation while being able to spend additional reasoning on more challenging questions. The card keeps the chat variant’s identity and specification separate from the Thinking model.
The release begins with conversational quality: a warmer default tone, more natural exchanges and improved adherence to the user’s instructions. Those qualities are useful when an assistant needs to answer a question directly, revise an explanation after feedback or maintain an appropriate style for its audience. The practical task remains specific: explain the supplied material, prepare a response or refine a selected passage.
Adaptive reasoning is a notable part of the Instant update. OpenAI describes the model deciding when a harder question needs more thinking before the answer, with improvements in mathematical and coding tasks. This is a model behavior rather than a claim that every chat request uses the same fixed reasoning process. The release’s separate description of GPT-5.1 Thinking belongs to that other variant.
The manufacturer also emphasizes customization and more faithful handling of custom instructions. An application can express the desired role, tone, response length and required details in its task instructions. For example, a support assistant may need a concise professional answer, while an educational explanation may need simpler wording and explicit intermediate steps. ChatGPT’s product-level personality settings are distinct from the instructions supplied by another application.
This chat model accepts text and images and returns text. Its published context window is 128,000 tokens, maximum output is 16,384 tokens and documented knowledge cutoff is September 30, 2024. Streaming, function calling and Structured Outputs are supported. Audio and video input, fine-tuning and predicted outputs are not supported in the published configuration. These limits describe GPT-5.1 Chat rather than another member of the family.
Functions and structured responses support different integration goals. A function call supplies an action name and arguments for the application to execute, after which the resulting information can return to the conversation. A structured response can organize the final material into agreed fields. When images are involved, the same task can ask for an explanation of visible content; clear, legible source material is especially important for small text and detailed visual relationships.
For an assistant using Ailoot API, give the model the inquiry context and a clear completion criterion. Request an artifact such as an edited document, a client response, an explanation of a screenshot or a structured summary. Preserve the source information and specify how to handle missing details. Follow-up messages can then adjust one part of the result while keeping the conversation focused on the user’s original task.
POST /v1/chat/completionstexttext| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits gpt-5.1-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 $0.0025 / 1K tokens Output · Output tokens $0.02 / 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-5.1-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-5.1-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-5.1-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-5.1-chat-latest
Use the method paths shown above.
gpt-5.1-chat-latestGPT-5.1 Chat is OpenAI’s Instant chat variant with adaptive reasoning for harder questions. It combines text and image input, a more conversational style, instruction following, function calling and structured responses in 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-5.1 Chat identifies the GPT-5.1 chat snapshot associated with ChatGPT. OpenAI’s release maps the Instant variant to this chat label and distinguishes it from GPT-5.1 Thinking. Instant is designed for everyday conversation while being able to spend additional reasoning on more challenging questions. The card keeps the chat variant’s identity and specification separate from the Thinking model.
The release begins with conversational quality: a warmer default tone, more natural exchanges and improved adherence to the user’s instructions. Those qualities are useful when an assistant needs to answer a question directly, revise an explanation after feedback or maintain an appropriate style for its audience. The practical task remains specific: explain the supplied material, prepare a response or refine a selected passage.
Adaptive reasoning is a notable part of the Instant update. OpenAI describes the model deciding when a harder question needs more thinking before the answer, with improvements in mathematical and coding tasks. This is a model behavior rather than a claim that every chat request uses the same fixed reasoning process. The release’s separate description of GPT-5.1 Thinking belongs to that other variant.
The manufacturer also emphasizes customization and more faithful handling of custom instructions. An application can express the desired role, tone, response length and required details in its task instructions. For example, a support assistant may need a concise professional answer, while an educational explanation may need simpler wording and explicit intermediate steps. ChatGPT’s product-level personality settings are distinct from the instructions supplied by another application.
This chat model accepts text and images and returns text. Its published context window is 128,000 tokens, maximum output is 16,384 tokens and documented knowledge cutoff is September 30, 2024. Streaming, function calling and Structured Outputs are supported. Audio and video input, fine-tuning and predicted outputs are not supported in the published configuration. These limits describe GPT-5.1 Chat rather than another member of the family.
Functions and structured responses support different integration goals. A function call supplies an action name and arguments for the application to execute, after which the resulting information can return to the conversation. A structured response can organize the final material into agreed fields. When images are involved, the same task can ask for an explanation of visible content; clear, legible source material is especially important for small text and detailed visual relationships.
For an assistant using Ailoot API, give the model the inquiry context and a clear completion criterion. Request an artifact such as an edited document, a client response, an explanation of a screenshot or a structured summary. Preserve the source information and specify how to handle missing details. Follow-up messages can then adjust one part of the result while keeping the conversation focused on the user’s original task.
POST /v1/messagestextjson, sse| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits gpt-5.1-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 $0.0025 / 1K tokens Output · Output tokens $0.02 / 1K tokens Input · Cache read $0 / 1K tokens Input · Cache write $0 / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
{\n "model": "gpt-5.1-chat-latest",\n "max_tokens": 128,\n "messages": [\n {\n "role": "user",\n "content": "Summarize the incident report."\n }\n ],\n "stream": false\n}set -eu\n: "${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-5.1-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-5.1-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-5.1-chat-latest
Use the method paths shown above.
Technical example in English
Explain a password-reset rule clearly\n\nExplain this policy to a nontechnical customer in under 90 words: a reset link expires after 30 minutes; requesting another link invalidates earlier links; changing the password signs out other sessions. The customer requested two links and opened the first. Explain what to do next, using a warm, direct tone and no invented account details.One concise customer explanation and a clear next step grounded in the supplied policy.The reply explains why the first link fails and directs the customer to the newest unexpired link.