ChatGPT-4o identifies the GPT-4o snapshot associated with ChatGPT’s conversational experience. Its focus is a dialogue in which text and visual material contribute to one answer: a user can describe a task, attach an image and refine the result through follow-up messages. The model produces text, including explanations, rewritten passages and code. This card describes the chat alias and its own documented configuration.
OpenAI’s introduction of the GPT-4o family emphasizes combined language and visual understanding. In this text-and-image configuration, that supports questions about visible content alongside ordinary writing and coding discussion. The model card offers examples such as mathematical tutoring, travel assistance, clothing recommendations and recipe preparation. These are conversational uses: the task, supplied information and desired answer format guide the response.
The family also develops multilingual language understanding and generation. An application can use that foundation to explain material for a chosen audience, revise its tone or discuss the same source in another language. For a document or screenshot, a useful instruction identifies the relevant passage or visible element and specifies the artifact to produce: a concise explanation, revised text or sequence of actions.
The specification lists text and image input, text output, a 128,000-token context window and a maximum of 16,384 output tokens. The documented knowledge cutoff is October 1, 2023. Audio and video are not listed as supported inputs for this alias. The broader GPT-4o family announcement describes additional modalities, but the configuration on this card is the text-and-image interface.
Streaming and predicted outputs are listed as supported features. Predicted outputs are intended for regeneration tasks in which much of the expected text is already known, such as revising a document while keeping most of it unchanged. Function calling, Structured Outputs and fine-tuning are not supported in the published chat-alias configuration. These distinctions matter when choosing between a conversational text result and an integration that needs tool calls or a schema-enforced response.
OpenAI’s vision guidance identifies practical limits around small or rotated text, precise spatial interpretation, object counts and difficult image shapes. Supply a clear image and identify the region relevant to the question. The resulting explanation concerns visible content; filenames and image metadata are not a substitute for information shown in the picture.
For an assistant using Ailoot API, combine the source material with a concrete goal and preserve the relevant conversation context. A suitable task could ask for a screenshot-based explanation, a revised customer message or a document outline. The unversioned chat label identifies this conversational GPT-4o variant and remains explicit when organizing repeated requests.
", "aboutText": "ChatGPT-4o identifies the GPT-4o snapshot associated with ChatGPT’s conversational experience. Its focus is a dialogue in which text and visual material contribute to one answer: a user can describe a task, attach an image and refine the result through follow-up messages. The model produces text, including explanations, rewritten passages and code. This card describes the chat alias and its own documented configuration.\n\nOpenAI’s introduction of the GPT-4o family emphasizes combined language and visual understanding. In this text-and-image configuration, that supports questions about visible content alongside ordinary writing and coding discussion. The model card offers examples such as mathematical tutoring, travel assistance, clothing recommendations and recipe preparation. These are conversational uses: the task, supplied information and desired answer format guide the response.\n\nThe family also develops multilingual language understanding and generation. An application can use that foundation to explain material for a chosen audience, revise its tone or discuss the same source in another language. For a document or screenshot, a useful instruction identifies the relevant passage or visible element and specifies the artifact to produce: a concise explanation, revised text or sequence of actions.\n\nThe specification lists text and image input, text output, a 128,000-token context window and a maximum of 16,384 output tokens. The documented knowledge cutoff is October 1, 2023. Audio and video are not listed as supported inputs for this alias. The broader GPT-4o family announcement describes additional modalities, but the configuration on this card is the text-and-image interface.\n\nStreaming and predicted outputs are listed as supported features. Predicted outputs are intended for regeneration tasks in which much of the expected text is already known, such as revising a document while keeping most of it unchanged. Function calling, Structured Outputs and fine-tuning are not supported in the published chat-alias configuration. These distinctions matter when choosing between a conversational text result and an integration that needs tool calls or a schema-enforced response.\n\nOpenAI’s vision guidance identifies practical limits around small or rotated text, precise spatial interpretation, object counts and difficult image shapes. Supply a clear image and identify the region relevant to the question. The resulting explanation concerns visible content; filenames and image metadata are not a substitute for information shown in the picture.\n\nFor an assistant using Ailoot API, combine the source material with a concrete goal and preserve the relevant conversation context. A suitable task could ask for a screenshot-based explanation, a revised customer message or a document outline. The unversioned chat label identifies this conversational GPT-4o variant and remains explicit when organizing repeated requests.", "editorialOverlay": { "copy_fields": { "practical_html": "Technical example in English
Prepare a helpful customer reply\n\nDraft a warm customer reply under 90 words. Customer: 'My parcel says delivered, but reception has nothing.' Facts: tracking shows delivery at 14:10; the courier has not supplied a proof-of-delivery photo; support can request it today. Acknowledge the problem, explain the next action, and avoid claiming the parcel is lost or promising a refund.One sendable customer message with acknowledgement, a request for delivery evidence and a clear next step.Every factual statement comes from the supplied facts, with no invented resolution or refund promise.chatgpt-4o-latestChatGPT-4o is OpenAI’s conversational GPT-4o variant for text and image input with text output. It supports multilingual writing, code discussion and visual explanations within a 128,000-token context, with a feature set specific to this chat alias.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
ChatGPT-4o identifies the GPT-4o snapshot associated with ChatGPT’s conversational experience. Its focus is a dialogue in which text and visual material contribute to one answer: a user can describe a task, attach an image and refine the result through follow-up messages. The model produces text, including explanations, rewritten passages and code. This card describes the chat alias and its own documented configuration.
OpenAI’s introduction of the GPT-4o family emphasizes combined language and visual understanding. In this text-and-image configuration, that supports questions about visible content alongside ordinary writing and coding discussion. The model card offers examples such as mathematical tutoring, travel assistance, clothing recommendations and recipe preparation. These are conversational uses: the task, supplied information and desired answer format guide the response.
The family also develops multilingual language understanding and generation. An application can use that foundation to explain material for a chosen audience, revise its tone or discuss the same source in another language. For a document or screenshot, a useful instruction identifies the relevant passage or visible element and specifies the artifact to produce: a concise explanation, revised text or sequence of actions.
The specification lists text and image input, text output, a 128,000-token context window and a maximum of 16,384 output tokens. The documented knowledge cutoff is October 1, 2023. Audio and video are not listed as supported inputs for this alias. The broader GPT-4o family announcement describes additional modalities, but the configuration on this card is the text-and-image interface.
Streaming and predicted outputs are listed as supported features. Predicted outputs are intended for regeneration tasks in which much of the expected text is already known, such as revising a document while keeping most of it unchanged. Function calling, Structured Outputs and fine-tuning are not supported in the published chat-alias configuration. These distinctions matter when choosing between a conversational text result and an integration that needs tool calls or a schema-enforced response.
OpenAI’s vision guidance identifies practical limits around small or rotated text, precise spatial interpretation, object counts and difficult image shapes. Supply a clear image and identify the region relevant to the question. The resulting explanation concerns visible content; filenames and image metadata are not a substitute for information shown in the picture.
For an assistant using Ailoot API, combine the source material with a concrete goal and preserve the relevant conversation context. A suitable task could ask for a screenshot-based explanation, a revised customer message or a document outline. The unversioned chat label identifies this conversational GPT-4o variant and remains explicit when organizing repeated requests.
POST /v1/chat/completionstexttext| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits chatgpt-4o-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 $0.01 / 1K tokens Output · Output tokens $0.03 / 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": "chatgpt-4o-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='chatgpt-4o-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='chatgpt-4o-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 chatgpt-4o-latest
Use the method paths shown above.
chatgpt-4o-latestChatGPT-4o is OpenAI’s conversational GPT-4o variant for text and image input with text output. It supports multilingual writing, code discussion and visual explanations within a 128,000-token context, with a feature set specific to this chat alias.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
ChatGPT-4o identifies the GPT-4o snapshot associated with ChatGPT’s conversational experience. Its focus is a dialogue in which text and visual material contribute to one answer: a user can describe a task, attach an image and refine the result through follow-up messages. The model produces text, including explanations, rewritten passages and code. This card describes the chat alias and its own documented configuration.
OpenAI’s introduction of the GPT-4o family emphasizes combined language and visual understanding. In this text-and-image configuration, that supports questions about visible content alongside ordinary writing and coding discussion. The model card offers examples such as mathematical tutoring, travel assistance, clothing recommendations and recipe preparation. These are conversational uses: the task, supplied information and desired answer format guide the response.
The family also develops multilingual language understanding and generation. An application can use that foundation to explain material for a chosen audience, revise its tone or discuss the same source in another language. For a document or screenshot, a useful instruction identifies the relevant passage or visible element and specifies the artifact to produce: a concise explanation, revised text or sequence of actions.
The specification lists text and image input, text output, a 128,000-token context window and a maximum of 16,384 output tokens. The documented knowledge cutoff is October 1, 2023. Audio and video are not listed as supported inputs for this alias. The broader GPT-4o family announcement describes additional modalities, but the configuration on this card is the text-and-image interface.
Streaming and predicted outputs are listed as supported features. Predicted outputs are intended for regeneration tasks in which much of the expected text is already known, such as revising a document while keeping most of it unchanged. Function calling, Structured Outputs and fine-tuning are not supported in the published chat-alias configuration. These distinctions matter when choosing between a conversational text result and an integration that needs tool calls or a schema-enforced response.
OpenAI’s vision guidance identifies practical limits around small or rotated text, precise spatial interpretation, object counts and difficult image shapes. Supply a clear image and identify the region relevant to the question. The resulting explanation concerns visible content; filenames and image metadata are not a substitute for information shown in the picture.
For an assistant using Ailoot API, combine the source material with a concrete goal and preserve the relevant conversation context. A suitable task could ask for a screenshot-based explanation, a revised customer message or a document outline. The unversioned chat label identifies this conversational GPT-4o variant and remains explicit when organizing repeated requests.
POST /v1/messagestextjson, sse| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits chatgpt-4o-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 $0.01 / 1K tokens Output · Output tokens $0.03 / 1K tokens Input · Cache read $0 / 1K tokens Input · Cache write $0 / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
{\n "model": "chatgpt-4o-latest",\n "max_tokens": 128,\n "messages": [\n {\n "role": "user",\n "content": "Summarize the incident report."\n }\n ],\n "stream": false\n}set -eu\n: "${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': 'chatgpt-4o-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': 'chatgpt-4o-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 chatgpt-4o-latest
Use the method paths shown above.
Technical example in English
Prepare a helpful customer reply\n\nDraft a warm customer reply under 90 words. Customer: 'My parcel says delivered, but reception has nothing.' Facts: tracking shows delivery at 14:10; the courier has not supplied a proof-of-delivery photo; support can request it today. Acknowledge the problem, explain the next action, and avoid claiming the parcel is lost or promising a refund.One sendable customer message with acknowledgement, a request for delivery evidence and a clear next step.Every factual statement comes from the supplied facts, with no invented resolution or refund promise.