GPT-5 Chat is OpenAI’s label for the conversational GPT-5 snapshot associated with ChatGPT. The developer announcement identifies this API chat variant as the non-reasoning model in the GPT-5 system. It is a separate choice from the main GPT-5 reasoning model: the name on this card refers to a chat-oriented response path, not the complete ChatGPT routing system.
OpenAI organizes the GPT-5 family’s practical uses around coding, writing and understanding information. For a conversational assistant, those directions translate into discussing a function, explaining a supplied document, editing a message or developing an idea through successive questions. The family’s writing work includes drafting and revising reports, emails and longer-form material, with attention to the requested structure and expressive style.
Visual context can accompany the text task. An image of an interface, chart, presentation or diagram gives the model something concrete to explain. Ask about a particular relationship or element and define the desired text result. The output is an explanation, rewritten material or code; the image serves as input rather than as an image-generation request.
The published configuration has a 128,000-token context window, up to 16,384 output tokens and a knowledge cutoff of September 30, 2024. It accepts text and images and produces text. Streaming, function calling and Structured Outputs are listed as supported. Audio and video input, fine-tuning and predicted outputs are not supported in this chat configuration. The limits belong to the named chat model rather than to the larger reasoning-model specification.
Function calling connects the assistant with actions and data supplied by an application. The model can select a described function and produce its arguments; the application executes it and returns the result for the next response. Structured output serves a different purpose: a response can follow an agreed data structure, for example fields for a summary, relevant facts and a proposed next step. Tool execution and access remain responsibilities of the surrounding application.
The GPT-5 family release also emphasizes following instructions, clearer handling of uncertain or incomplete tasks and a less excessively agreeable style. The chat-specific model can be framed with a role, audience, tone and explicit output requirements. General vision guidance still applies to its image-based tasks: small or rotated text and precise spatial interpretation require careful source preparation.
For a product using Ailoot API, define the conversation’s intended outcome and provide the material on which it should depend. A useful request might ask for a customer reply, a screenshot explanation or a document in specified sections. Keep mandatory facts and the selected passage explicit when refining the answer. The chat label preserves its own identity across these workflows, while the supplied conversation provides the context for each response.
", "aboutText": "GPT-5 Chat is OpenAI’s label for the conversational GPT-5 snapshot associated with ChatGPT. The developer announcement identifies this API chat variant as the non-reasoning model in the GPT-5 system. It is a separate choice from the main GPT-5 reasoning model: the name on this card refers to a chat-oriented response path, not the complete ChatGPT routing system.\n\nOpenAI organizes the GPT-5 family’s practical uses around coding, writing and understanding information. For a conversational assistant, those directions translate into discussing a function, explaining a supplied document, editing a message or developing an idea through successive questions. The family’s writing work includes drafting and revising reports, emails and longer-form material, with attention to the requested structure and expressive style.\n\nVisual context can accompany the text task. An image of an interface, chart, presentation or diagram gives the model something concrete to explain. Ask about a particular relationship or element and define the desired text result. The output is an explanation, rewritten material or code; the image serves as input rather than as an image-generation request.\n\nThe published configuration has a 128,000-token context window, up to 16,384 output tokens and a knowledge cutoff of September 30, 2024. It accepts text and images and produces text. Streaming, function calling and Structured Outputs are listed as supported. Audio and video input, fine-tuning and predicted outputs are not supported in this chat configuration. The limits belong to the named chat model rather than to the larger reasoning-model specification.\n\nFunction calling connects the assistant with actions and data supplied by an application. The model can select a described function and produce its arguments; the application executes it and returns the result for the next response. Structured output serves a different purpose: a response can follow an agreed data structure, for example fields for a summary, relevant facts and a proposed next step. Tool execution and access remain responsibilities of the surrounding application.\n\nThe GPT-5 family release also emphasizes following instructions, clearer handling of uncertain or incomplete tasks and a less excessively agreeable style. The chat-specific model can be framed with a role, audience, tone and explicit output requirements. General vision guidance still applies to its image-based tasks: small or rotated text and precise spatial interpretation require careful source preparation.\n\nFor a product using Ailoot API, define the conversation’s intended outcome and provide the material on which it should depend. A useful request might ask for a customer reply, a screenshot explanation or a document in specified sections. Keep mandatory facts and the selected passage explicit when refining the answer. The chat label preserves its own identity across these workflows, while the supplied conversation provides the context for each response.", "editorialOverlay": { "copy_fields": { "practical_html": "Technical example in English
Revise an operational status report\n\nRewrite this report into three short sections: completed, unresolved and next step. Source: 'The import accepted 420 rows. Eighteen rows failed because their account IDs were blank. We have not retried them. Vera will ask the source team for the missing IDs tomorrow.' Keep the counts and ownership exact and avoid describing the import as fully complete.A concise three-section report preserving counts, unresolved rows and Vera's planned follow-up.The result states that 18 rows remain unresolved and that no retry has occurred.gpt-5-chat-latestGPT-5 Chat is OpenAI’s conversational, non-reasoning GPT-5 variant for text and image input with text output. It supports writing, visual explanations, code discussion, function calling and structured responses within 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 Chat is OpenAI’s label for the conversational GPT-5 snapshot associated with ChatGPT. The developer announcement identifies this API chat variant as the non-reasoning model in the GPT-5 system. It is a separate choice from the main GPT-5 reasoning model: the name on this card refers to a chat-oriented response path, not the complete ChatGPT routing system.
OpenAI organizes the GPT-5 family’s practical uses around coding, writing and understanding information. For a conversational assistant, those directions translate into discussing a function, explaining a supplied document, editing a message or developing an idea through successive questions. The family’s writing work includes drafting and revising reports, emails and longer-form material, with attention to the requested structure and expressive style.
Visual context can accompany the text task. An image of an interface, chart, presentation or diagram gives the model something concrete to explain. Ask about a particular relationship or element and define the desired text result. The output is an explanation, rewritten material or code; the image serves as input rather than as an image-generation request.
The published configuration has a 128,000-token context window, up to 16,384 output tokens and a knowledge cutoff of September 30, 2024. It accepts text and images and produces text. Streaming, function calling and Structured Outputs are listed as supported. Audio and video input, fine-tuning and predicted outputs are not supported in this chat configuration. The limits belong to the named chat model rather than to the larger reasoning-model specification.
Function calling connects the assistant with actions and data supplied by an application. The model can select a described function and produce its arguments; the application executes it and returns the result for the next response. Structured output serves a different purpose: a response can follow an agreed data structure, for example fields for a summary, relevant facts and a proposed next step. Tool execution and access remain responsibilities of the surrounding application.
The GPT-5 family release also emphasizes following instructions, clearer handling of uncertain or incomplete tasks and a less excessively agreeable style. The chat-specific model can be framed with a role, audience, tone and explicit output requirements. General vision guidance still applies to its image-based tasks: small or rotated text and precise spatial interpretation require careful source preparation.
For a product using Ailoot API, define the conversation’s intended outcome and provide the material on which it should depend. A useful request might ask for a customer reply, a screenshot explanation or a document in specified sections. Keep mandatory facts and the selected passage explicit when refining the answer. The chat label preserves its own identity across these workflows, while the supplied conversation provides the context for each response.
POST /v1/chat/completionstexttext| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits gpt-5-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 $0.00025 / 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-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-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-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-chat-latest
Use the method paths shown above.
gpt-5-chat-latestGPT-5 Chat is OpenAI’s conversational, non-reasoning GPT-5 variant for text and image input with text output. It supports writing, visual explanations, code discussion, function calling and structured responses within 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 Chat is OpenAI’s label for the conversational GPT-5 snapshot associated with ChatGPT. The developer announcement identifies this API chat variant as the non-reasoning model in the GPT-5 system. It is a separate choice from the main GPT-5 reasoning model: the name on this card refers to a chat-oriented response path, not the complete ChatGPT routing system.
OpenAI organizes the GPT-5 family’s practical uses around coding, writing and understanding information. For a conversational assistant, those directions translate into discussing a function, explaining a supplied document, editing a message or developing an idea through successive questions. The family’s writing work includes drafting and revising reports, emails and longer-form material, with attention to the requested structure and expressive style.
Visual context can accompany the text task. An image of an interface, chart, presentation or diagram gives the model something concrete to explain. Ask about a particular relationship or element and define the desired text result. The output is an explanation, rewritten material or code; the image serves as input rather than as an image-generation request.
The published configuration has a 128,000-token context window, up to 16,384 output tokens and a knowledge cutoff of September 30, 2024. It accepts text and images and produces text. Streaming, function calling and Structured Outputs are listed as supported. Audio and video input, fine-tuning and predicted outputs are not supported in this chat configuration. The limits belong to the named chat model rather than to the larger reasoning-model specification.
Function calling connects the assistant with actions and data supplied by an application. The model can select a described function and produce its arguments; the application executes it and returns the result for the next response. Structured output serves a different purpose: a response can follow an agreed data structure, for example fields for a summary, relevant facts and a proposed next step. Tool execution and access remain responsibilities of the surrounding application.
The GPT-5 family release also emphasizes following instructions, clearer handling of uncertain or incomplete tasks and a less excessively agreeable style. The chat-specific model can be framed with a role, audience, tone and explicit output requirements. General vision guidance still applies to its image-based tasks: small or rotated text and precise spatial interpretation require careful source preparation.
For a product using Ailoot API, define the conversation’s intended outcome and provide the material on which it should depend. A useful request might ask for a customer reply, a screenshot explanation or a document in specified sections. Keep mandatory facts and the selected passage explicit when refining the answer. The chat label preserves its own identity across these workflows, while the supplied conversation provides the context for each response.
POST /v1/messagestextjson, sse| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits gpt-5-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 $0.00025 / 1K tokens Input · Cache write $0 / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
{\n "model": "gpt-5-chat-latest",\n "max_tokens": 128,\n "messages": [\n {\n "role": "user",\n "content": "Summarize the incident report."\n }\n ],\n "stream": false\n}set -eu\n: "${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-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-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-chat-latest
Use the method paths shown above.
Technical example in English
Revise an operational status report\n\nRewrite this report into three short sections: completed, unresolved and next step. Source: 'The import accepted 420 rows. Eighteen rows failed because their account IDs were blank. We have not retried them. Vera will ask the source team for the missing IDs tomorrow.' Keep the counts and ownership exact and avoid describing the import as fully complete.A concise three-section report preserving counts, unresolved rows and Vera's planned follow-up.The result states that 18 rows remain unresolved and that no retry has occurred.