GPT-6 Luna Pro denotes OpenAI’s GPT-6 Luna with Pro reasoning. Luna is designed for focused tasks and high-volume workloads; its inputs are text and images and its output is text. Pro adds model work when developing the final answer. It is an execution mode applied to the chosen base model, distinct from the reasoning-effort setting that controls the amount of analysis within that mode.
A practical application is processing a narrowly defined request with several rules or exceptions. Supply the request, the categories it may belong to and the information that determines the next action. Ask for the category, the relevant facts and a short proposed response. An illustrative task is to classify a document request, identify a missing field and draft a message asking for that field. The task remains focused even when its conditions require careful consideration.
For an image-based question, provide a clear screenshot or other visual material and identify the detail to inspect. You can request an explanation of a visible interface state together with a short instruction for the reader. For written material, define the fields or statements that must survive the transformation, the allowed categories and the expected answer format. A small set of representative examples can clarify the boundary between similar categories without making the model infer your rules.
OpenAI specifies a 1,050,000-token context window for GPT-6 Luna, with a maximum input of 922,000 tokens and a maximum output of 128,000 tokens. The documented knowledge cutoff is May 18, 2026. The base model supports none, low, medium, high, xhigh and max reasoning effort, with medium as the default. Reasoning occupies token space as well as the final answer, so the task brief and supplied material should leave room for analysis and the requested result.
The producer documents Pro in the Responses API for difficult requests that allow additional latency and model activity. GPT-6 Luna’s built-in tools and reasoning with function calls use Responses; in the producer’s Chat Completions interface, function calling is supported only with reasoning effort set to none. The base model lists streaming, Structured Outputs, web and file search, function calling and prompt caching. Its tool set also includes image generation, code interpretation, shell and patch operations, skills, computer use, MCP connections and tool search. Your application determines and executes the operations it exposes.
Use a precise output contract for the final task: the decision, the fields supporting it and any information still missing. For example, ask for one category, a brief justification tied to the supplied rule and the next question to send to the reader. Review the result against those rules rather than adding an unrestricted discussion. This workflow uses Luna’s focused-task role while reserving Pro’s extra work for a request whose dependencies or exceptions need fuller examination.
", "aboutText": "GPT-6 Luna Pro denotes OpenAI’s GPT-6 Luna with Pro reasoning. Luna is designed for focused tasks and high-volume workloads; its inputs are text and images and its output is text. Pro adds model work when developing the final answer. It is an execution mode applied to the chosen base model, distinct from the reasoning-effort setting that controls the amount of analysis within that mode.\n\nA practical application is processing a narrowly defined request with several rules or exceptions. Supply the request, the categories it may belong to and the information that determines the next action. Ask for the category, the relevant facts and a short proposed response. An illustrative task is to classify a document request, identify a missing field and draft a message asking for that field. The task remains focused even when its conditions require careful consideration.\n\nFor an image-based question, provide a clear screenshot or other visual material and identify the detail to inspect. You can request an explanation of a visible interface state together with a short instruction for the reader. For written material, define the fields or statements that must survive the transformation, the allowed categories and the expected answer format. A small set of representative examples can clarify the boundary between similar categories without making the model infer your rules.\n\nOpenAI specifies a 1,050,000-token context window for GPT-6 Luna, with a maximum input of 922,000 tokens and a maximum output of 128,000 tokens. The documented knowledge cutoff is May 18, 2026. The base model supports none, low, medium, high, xhigh and max reasoning effort, with medium as the default. Reasoning occupies token space as well as the final answer, so the task brief and supplied material should leave room for analysis and the requested result.\n\nThe producer documents Pro in the Responses API for difficult requests that allow additional latency and model activity. GPT-6 Luna’s built-in tools and reasoning with function calls use Responses; in the producer’s Chat Completions interface, function calling is supported only with reasoning effort set to none. The base model lists streaming, Structured Outputs, web and file search, function calling and prompt caching. Its tool set also includes image generation, code interpretation, shell and patch operations, skills, computer use, MCP connections and tool search. Your application determines and executes the operations it exposes.\n\nUse a precise output contract for the final task: the decision, the fields supporting it and any information still missing. For example, ask for one category, a brief justification tied to the supplied rule and the next question to send to the reader. Review the result against those rules rather than adding an unrestricted discussion. This workflow uses Luna’s focused-task role while reserving Pro’s extra work for a request whose dependencies or exceptions need fuller examination.", "editorialOverlay": { "copy_fields": { "practical_html": "Technical example in English
Find missing request details\n\nProcess this equipment request using the supplied rules. Every request needs item, quantity and delivery site. Urgent requests also need a reason. Message: "Urgent: please send 4 replacement keyboards." Return the supplied fields, missing required fields and a brief reply asking only for the missing details. Do not invent a site or urgency reason.A compact field table, missing-field list and targeted clarification message.Check that quantity four and keyboards are retained, while delivery site and urgent reason stay missing.openai/gpt-6-luna-proGPT-6 Luna with Pro reasoning for focused tasks that need additional analysis before a final answer. The base model accepts text and images, making it useful for rule-based document handling, visual questions and concise written results.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
GPT-6 Luna Pro denotes OpenAI’s GPT-6 Luna with Pro reasoning. Luna is designed for focused tasks and high-volume workloads; its inputs are text and images and its output is text. Pro adds model work when developing the final answer. It is an execution mode applied to the chosen base model, distinct from the reasoning-effort setting that controls the amount of analysis within that mode.
A practical application is processing a narrowly defined request with several rules or exceptions. Supply the request, the categories it may belong to and the information that determines the next action. Ask for the category, the relevant facts and a short proposed response. An illustrative task is to classify a document request, identify a missing field and draft a message asking for that field. The task remains focused even when its conditions require careful consideration.
For an image-based question, provide a clear screenshot or other visual material and identify the detail to inspect. You can request an explanation of a visible interface state together with a short instruction for the reader. For written material, define the fields or statements that must survive the transformation, the allowed categories and the expected answer format. A small set of representative examples can clarify the boundary between similar categories without making the model infer your rules.
OpenAI specifies a 1,050,000-token context window for GPT-6 Luna, with a maximum input of 922,000 tokens and a maximum output of 128,000 tokens. The documented knowledge cutoff is May 18, 2026. The base model supports none, low, medium, high, xhigh and max reasoning effort, with medium as the default. Reasoning occupies token space as well as the final answer, so the task brief and supplied material should leave room for analysis and the requested result.
The producer documents Pro in the Responses API for difficult requests that allow additional latency and model activity. GPT-6 Luna’s built-in tools and reasoning with function calls use Responses; in the producer’s Chat Completions interface, function calling is supported only with reasoning effort set to none. The base model lists streaming, Structured Outputs, web and file search, function calling and prompt caching. Its tool set also includes image generation, code interpretation, shell and patch operations, skills, computer use, MCP connections and tool search. Your application determines and executes the operations it exposes.
Use a precise output contract for the final task: the decision, the fields supporting it and any information still missing. For example, ask for one category, a brief justification tied to the supplied rule and the next question to send to the reader. Review the result against those rules rather than adding an unrestricted discussion. This workflow uses Luna’s focused-task role while reserving Pro’s extra work for a request whose dependencies or exceptions need fuller examination.
POST /v1/chat/completionstexttext| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits openai/gpt-6-luna-pro | Default— |
Fieldmessages | Typemessage[] | RequirementRequired | Values & limits Minimum items: 1 | Default— |
Fieldmessages[].role | Typestring | RequirementRequired | Values & limits Examples: system, user, assistant, tool, function Conditions: These roles are explicitly represented by Source compatibility types; the exact model role set remains model-specific. Model-specific | Default— |
Fieldmessages[].content | Typestring|content_part[]|null | RequirementOptional | Values & limits | Default— |
Fieldmessages[].tool_calls | Typetool_call[] | RequirementOptional | Values & limits | Default— |
Fieldmessages[].tool_call_id | Typestring | RequirementOptional | Values & limits Applies when: role=tool; use actual returned ID | Default— |
Fieldmessages[].name | Typestring | RequirementOptional | Values & limits | Default— |
Fieldmessages[].reasoning_content | Typestring | RequirementOptional | Values & limits Applies when: Round-trip actual returned reasoning where this model uses it | Default— |
Fieldmessages[].reasoning_details | Typeobject|array | RequirementOptional | Values & limits Model-specific | Default— |
Fieldmax_tokens | Typeinteger | RequirementOptional | Values & limits Minimum: 1 Model-specific | Default— |
Fieldmax_completion_tokens | Typeinteger | RequirementOptional | Values & limits Minimum: 1 Model-specific | Default— |
Fieldstream | Typeboolean | RequirementOptional | Values & limits | Defaultfalse |
Fieldstream_options.include_usage | Typeboolean | RequirementOptional | Values & limits Model-specific | Default— |
Fieldtemperature | Typenumber | RequirementOptional | Values & limits Model-specific | Default— |
Fieldtop_p | Typenumber | RequirementOptional | Values & limits Model-specific | Default— |
Fieldstop | Typestring|string[] | RequirementOptional | Values & limits Model-specific | Default— |
Fieldtools | Typefunction_tool[] | RequirementOptional | Values & limits Model-specific | Default— |
Fieldtools[].type | Typestring | RequirementOptional | Values & limits function Model-specific | Default— |
Fieldtools[].function.name | Typestring | RequirementRequired | Values & limits Applies when: function tool supplied | Default— |
Fieldtools[].function.description | Typestring | RequirementOptional | Values & limits | Default— |
Fieldtools[].function.parameters | TypeJSON_schema | RequirementOptional | Values & limits | Default— |
Fieldtool_choice | Typestring|object | RequirementOptional | Values & limits Model-specific | Default— |
Fieldparallel_tool_calls | Typeboolean | RequirementOptional | Values & limits Model-specific | Default— |
Fieldresponse_format | Typeobject | RequirementOptional | Values & limits Model-specific | Default— |
Fieldreasoning_effort | Typestring | RequirementOptional | Values & limits Model-specific | Default— |
Fieldreasoning | Typeobject | RequirementOptional | Values & limits Model-specific | Default— |
Fieldservice_tier | Typestring | RequirementOptional | Values & limits Model-specific | Default— |
Fieldinstructions | Typestring | RequirementOptional | Values & limits Compatibility field | Default— |
Fieldfunctions | Typefunction[] | RequirementOptional | Values & limits Model-specific | Default— |
Fieldfunction_call | Typestring|object | RequirementOptional | Values & limits Model-specific | Default— |
Fieldprompt_cache_options | Typeobject | RequirementOptional | Values & limits Model-specific | Default— |
Fieldmodalities | Typestring[] | RequirementOptional | Values & limits text | Default— |
Fieldprovider | Typeobject | RequirementOptional | Values & limits Conditions: provider.options is rejected on this Ultra text route | Default— |
Fieldcache_control.ttl | Typestring | RequirementOptional | Values & limits Conditions: 1h is rejected in actual prompt-cache locations | Default— |
Text · UTF8 text
messages[].content
| Parameter | Contract |
|---|---|
Parametercontent_type | Contractapplication/json |
Parameterrequired | Contractmodel, messages |
Parametermaterial_types | Contracttext |
Parametermessages | Contractmessage array with role/content shape |
Parameterfile_material | Contractactual UTF-8 document inserted as literal text; no file ID inferred |
Parameteroptional_controls | Contractstream, temperature, top_p, stop, max_tokens, max_completion_tokens, reasoning, response_format, tools, tool_choice |
Parametercontrol_scope | Contractmodel-specific; user function tools differ from hosted tools |
Parameteroutput_modalities | Contracttext |
Parameterhosted_operations | Contractfalse |
Parametermultimodal_input_binding | Contractnull |
Lifecycle: synchronous_or_streamed
Send exact model ID with messages.
Context length is not an output cap.
A text file may supply literal text; image/audio/file-object inputs need exact binding.
Tariff references do not add request options. Supported parameters are listed for the selected method.
Match each charge to the conditions shown. Use one rate for each set of alternative configurations.
| Configuration | Rate schedule | Context tokens, more than | Context tokens, up to and including | Charge |
|---|---|---|---|---|
| ConfigurationBase token rates | Rate scheduleBase rate | Context tokens, more than— | Context tokens, up to and includingNo upper bound | Charge Input · Input tokens $0.0002 / 1K tokens Output · Output tokens $0.001 / 1K tokens Input · Cache read $0.00002 / 1K tokens Input · Cache write $0.00025 / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
| ConfigurationRequest-length tier 1 | Rate schedule— | Context tokens, more than0 | Context tokens, up to and including272,000 | Charge Input · Input tokens $0.0002 / 1K tokens Output · Output tokens $0.001 / 1K tokens Input · Cache read $0.00002 / 1K tokens Input · Cache write $0.00025 / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
| ConfigurationRequest-length tier 2 | Rate schedule— | Context tokens, more than272,000 | Context tokens, up to and includingNo upper bound | Charge Input · Input tokens $0.0004 / 1K tokens Output · Output tokens $0.0015 / 1K tokens Input · Cache read $0.00004 / 1K tokens Input · Cache write $0.0005 / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{"model": "openai/gpt-6-luna-pro", "messages": [{"role": "user", "content": "Explain the difference between an observation and a hypothesis in three sentences."}]}')\n\nPath("request.json").write_text(json.dumps(p,ensure_ascii=False))\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/chat/completions \\\n -H "Authorization: Bearer $AILOOT_API_KEY" -H 'Content-Type: application/json' \\\n --data-binary @request.json -D response-headers.txt -o result.json\npython3 - <<'PY_RESULT'\nimport json\nfrom pathlib import Path\nr=json.loads(Path("result.json").read_text())\nfor x in r.get("choices",[]):\n if x.get("message",{}).get("content"): print(x["message"]["content"])\nPY_RESULT\nfrom openai import OpenAI\nimport os\n\nif not os.environ.get("AILOOT_API_KEY", "").strip(): raise SystemExit("Set AILOOT_API_KEY before making a request")\nclient=OpenAI(base_url="https://api.ailoot.ai/v1",api_key=os.environ["AILOOT_API_KEY"],max_retries=0)\nresult=client.chat.completions.create(model='openai/gpt-6-luna-pro',messages=[{"role":"user","content":"Explain observations and hypotheses in three sentences."}])\nimport base64, json\nfrom pathlib import Path\n\ndef save_result(data, stem="result"):\n # Keep every field, including usage, IDs, reasoning and tool requests.\n Path(stem + ".json").write_text(json.dumps(data, ensure_ascii=False, indent=2), encoding="utf-8")\n print(json.dumps(data, ensure_ascii=False, indent=2))\n def save_blob(encoded, suffix, label):\n raw = base64.b64decode(encoded, altchars=b"-_", validate=True)\n ext = "png" if raw.startswith(b"\\x89PNG\\r\\n\\x1a\\n") else "jpg" if raw.startswith(b"\\xff\\xd8") else "webp" if raw[:4] == b"RIFF" and raw[8:12] == b"WEBP" else suffix\n file = Path(stem + "-" + label + "." + ext)\n file.write_bytes(raw)\n print("Saved", file)\n for index, block in enumerate(data.get("content") or []):\n print("Content", index, json.dumps(block, ensure_ascii=False))\n for choice in data.get("choices") or []:\n print("Choice", choice.get("index"), json.dumps(choice.get("message", choice.get("delta", {})), ensure_ascii=False))\n for index, item in enumerate(data.get("output") or []):\n print("Output", index, json.dumps(item, ensure_ascii=False))\n if item.get("type") == "image_generation_call" and item.get("result"):\n save_blob(item["result"], item.get("output_format") or "bin", "output-" + str(index))\n for ci, candidate in enumerate(data.get("candidates") or []):\n for pi, part in enumerate((candidate.get("content") or {}).get("parts") or []):\n print("Candidate/part", ci, pi, json.dumps(part, ensure_ascii=False))\n blob = part.get("inlineData") or part.get("inline_data")\n if blob and blob.get("data"):\n mime = blob.get("mimeType") or blob.get("mime_type")\n suffix = {"image/png":"png", "image/jpeg":"jpg", "image/webp":"webp", "audio/wav":"wav", "audio/mpeg":"mp3", "video/mp4":"mp4"}.get(mime, "bin")\n save_blob(blob["data"], suffix, "candidate-" + str(ci) + "-part-" + str(pi))\n\nsave_result(result.to_dict(mode="json"))\nfrom openai import OpenAI\nimport os\nfrom pathlib import Path\n\nif not os.environ.get("AILOOT_API_KEY", "").strip(): raise SystemExit("Set AILOOT_API_KEY before making a request")\nclient=OpenAI(base_url="https://api.ailoot.ai/v1",api_key=os.environ["AILOOT_API_KEY"],max_retries=0)\nimport base64, json\nfrom pathlib import Path\n\ndef save_result(data, stem="result"):\n # Keep every field, including usage, IDs, reasoning and tool requests.\n Path(stem + ".json").write_text(json.dumps(data, ensure_ascii=False, indent=2), encoding="utf-8")\n print(json.dumps(data, ensure_ascii=False, indent=2))\n def save_blob(encoded, suffix, label):\n raw = base64.b64decode(encoded, altchars=b"-_", validate=True)\n ext = "png" if raw.startswith(b"\\x89PNG\\r\\n\\x1a\\n") else "jpg" if raw.startswith(b"\\xff\\xd8") else "webp" if raw[:4] == b"RIFF" and raw[8:12] == b"WEBP" else suffix\n file = Path(stem + "-" + label + "." + ext)\n file.write_bytes(raw)\n print("Saved", file)\n for index, block in enumerate(data.get("content") or []):\n print("Content", index, json.dumps(block, ensure_ascii=False))\n for choice in data.get("choices") or []:\n print("Choice", choice.get("index"), json.dumps(choice.get("message", choice.get("delta", {})), ensure_ascii=False))\n for index, item in enumerate(data.get("output") or []):\n print("Output", index, json.dumps(item, ensure_ascii=False))\n if item.get("type") == "image_generation_call" and item.get("result"):\n save_blob(item["result"], item.get("output_format") or "bin", "output-" + str(index))\n for ci, candidate in enumerate(data.get("candidates") or []):\n for pi, part in enumerate((candidate.get("content") or {}).get("parts") or []):\n print("Candidate/part", ci, pi, json.dumps(part, ensure_ascii=False))\n blob = part.get("inlineData") or part.get("inline_data")\n if blob and blob.get("data"):\n mime = blob.get("mimeType") or blob.get("mime_type")\n suffix = {"image/png":"png", "image/jpeg":"jpg", "image/webp":"webp", "audio/wav":"wav", "audio/mpeg":"mp3", "video/mp4":"mp4"}.get(mime, "bin")\n save_blob(blob["data"], suffix, "candidate-" + str(ci) + "-part-" + str(pi))\n\nwith client.chat.completions.stream(model='openai/gpt-6-luna-pro',messages=[{"role":"user","content":"Explain observations and hypotheses in three sentences."}]) as stream:\n with Path("stream-events.jsonl").open("w",encoding="utf-8") as log:\n for event in stream:\n payload=event.to_dict(mode="json")\n log.write(json.dumps(payload,ensure_ascii=False)+"\\n"); log.flush()\n print(json.dumps(payload,ensure_ascii=False))\n final=stream.get_final_completion()\n save_result(final.to_dict(mode="json"),"stream-result")\njson or SSE
Keep every complete returned response, file and content part.
Keep every choice and its content/tool calls/reasoning. In SSE assemble deltas by choice/tool index, retain the final finish reason and usage.
Keep request/response IDs and headers. No generic text job retrieval URL is supplied.
| Field | Type |
|---|---|
Fieldchoices[].index | Typeinteger |
Fieldchoices[].message.content | Typestring|null |
Fieldchoices[].message.tool_calls | Typetool_call[] |
Fieldchoices[].message.reasoning_content | Typestring |
Fieldchoices[].message.reasoning_details | Typeobject|array |
Fieldchoices[].finish_reason | Typestring|null |
Fieldusage | Typeobject |
| Response | Condition | Next step |
|---|---|---|
Response400 · invalid_request_error | ConditionInvalid required fields, material representation or parameter value | Next stepCorrect the indicated fields before a new creation request. |
Ailoot Ultra openai/gpt-6-luna-pro
Use the method paths shown above.
openai/gpt-6-luna-proGPT-6 Luna with Pro reasoning for focused tasks that need additional analysis before a final answer. The base model accepts text and images, making it useful for rule-based document handling, visual questions and concise written results.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
GPT-6 Luna Pro denotes OpenAI’s GPT-6 Luna with Pro reasoning. Luna is designed for focused tasks and high-volume workloads; its inputs are text and images and its output is text. Pro adds model work when developing the final answer. It is an execution mode applied to the chosen base model, distinct from the reasoning-effort setting that controls the amount of analysis within that mode.
A practical application is processing a narrowly defined request with several rules or exceptions. Supply the request, the categories it may belong to and the information that determines the next action. Ask for the category, the relevant facts and a short proposed response. An illustrative task is to classify a document request, identify a missing field and draft a message asking for that field. The task remains focused even when its conditions require careful consideration.
For an image-based question, provide a clear screenshot or other visual material and identify the detail to inspect. You can request an explanation of a visible interface state together with a short instruction for the reader. For written material, define the fields or statements that must survive the transformation, the allowed categories and the expected answer format. A small set of representative examples can clarify the boundary between similar categories without making the model infer your rules.
OpenAI specifies a 1,050,000-token context window for GPT-6 Luna, with a maximum input of 922,000 tokens and a maximum output of 128,000 tokens. The documented knowledge cutoff is May 18, 2026. The base model supports none, low, medium, high, xhigh and max reasoning effort, with medium as the default. Reasoning occupies token space as well as the final answer, so the task brief and supplied material should leave room for analysis and the requested result.
The producer documents Pro in the Responses API for difficult requests that allow additional latency and model activity. GPT-6 Luna’s built-in tools and reasoning with function calls use Responses; in the producer’s Chat Completions interface, function calling is supported only with reasoning effort set to none. The base model lists streaming, Structured Outputs, web and file search, function calling and prompt caching. Its tool set also includes image generation, code interpretation, shell and patch operations, skills, computer use, MCP connections and tool search. Your application determines and executes the operations it exposes.
Use a precise output contract for the final task: the decision, the fields supporting it and any information still missing. For example, ask for one category, a brief justification tied to the supplied rule and the next question to send to the reader. Review the result against those rules rather than adding an unrestricted discussion. This workflow uses Luna’s focused-task role while reserving Pro’s extra work for a request whose dependencies or exceptions need fuller examination.
POST /v1/messagestexttext| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits openai/gpt-6-luna-pro | Default— |
Fieldmessages | Typemessage[] | RequirementRequired | Values & limits Minimum items: 1 | Default— |
Fieldmessages[].role | Typestring | RequirementRequired | Values & limits user, assistant | Default— |
Fieldmessages[].content | Typestring|content_block[] | RequirementRequired | Values & limits | Default— |
Fieldsystem | Typestring|content_block[] | RequirementOptional | Values & limits | Default— |
Fieldmax_tokens | Typeinteger | RequirementRequired | Values & limits Minimum: 1 Limit applies to: exact selected model/card ceiling | Default— |
Fieldstream | Typeboolean | RequirementOptional | Values & limits | Defaultfalse |
Fieldtemperature | Typenumber | RequirementOptional | Values & limits Model-specific | Default— |
Fieldtop_p | Typenumber | RequirementOptional | Values & limits Model-specific | Default— |
Fieldstop_sequences | Typestring[] | RequirementOptional | Values & limits Model-specific | Default— |
Fieldtools | Typetool[] | RequirementOptional | Values & limits Model-specific | Default— |
Fieldtools[].name | Typestring | RequirementOptional | Values & limits Applies when: tool supplied | Default— |
Fieldtools[].description | Typestring | RequirementOptional | Values & limits | Default— |
Fieldtools[].input_schema | TypeJSON_schema | RequirementOptional | Values & limits | Default— |
Fieldtool_choice | Typeobject | RequirementOptional | Values & limits Model-specific | Default— |
Fieldthinking.type | Typestring | RequirementOptional | Values & limits Model-specific | Default— |
Fieldthinking.budget_tokens | Typeinteger | RequirementOptional | Values & limits Model-specific | Default— |
Fieldoutput_config.effort | Typestring | RequirementOptional | Values & limits Model-specific | Default— |
Fieldmetadata | Typeobject | RequirementOptional | Values & limits | Default— |
Fieldcache_control | Typeobject | RequirementOptional | Values & limits Model-specific | Default— |
Text · UTF8 text
messages[].content or type=text block
Text + image
{type:image,source:{type:base64,media_type,data}} or source type=url
| Parameter | Contract |
|---|---|
Parametermaterial_types | Contracttext |
Parameterrequired | Contractmodel, messages, max_tokens |
Parameteroptional_controls | Contractnull |
Parametercontent_type | Contractapplication/json |
Parametertext_file_material | ContractRead the actual UTF-8 document and put its text in the indicated input or message content string. |
Lifecycle: exact retained published method
Send the exact model ID and required fields.
Keep every returned item and its original identifiers.
Tariff references do not add request options. Supported parameters are listed for the selected method.
Match each charge to the conditions shown. Use one rate for each set of alternative configurations.
| Configuration | Rate schedule | Context tokens, more than | Context tokens, up to and including | Charge |
|---|---|---|---|---|
| ConfigurationBase token rates | Rate scheduleBase rate | Context tokens, more than— | Context tokens, up to and includingNo upper bound | Charge Input · Input tokens $0.0002 / 1K tokens Output · Output tokens $0.001 / 1K tokens Input · Cache read $0.00002 / 1K tokens Input · Cache write $0.00025 / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
| ConfigurationRequest-length tier 1 | Rate schedule— | Context tokens, more than0 | Context tokens, up to and including272,000 | Charge Input · Input tokens $0.0002 / 1K tokens Output · Output tokens $0.001 / 1K tokens Input · Cache read $0.00002 / 1K tokens Input · Cache write $0.00025 / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
| ConfigurationRequest-length tier 2 | Rate schedule— | Context tokens, more than272,000 | Context tokens, up to and includingNo upper bound | Charge Input · Input tokens $0.0004 / 1K tokens Output · Output tokens $0.0015 / 1K tokens Input · Cache read $0.00004 / 1K tokens Input · Cache write $0.0005 / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
{\n "model": "openai/gpt-6-luna-pro",\n "max_tokens": 4096,\n "messages": [\n {\n "role": "user",\n "content": "Summarize the incident report."\n }\n ]\n}set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\n: "${AILOOT_TEXT_FILE:?Set your actual UTF-8 document}"\nexport AILOOT_TEXT_FILE\npython3 - <<'PY_REQUEST'\nimport json, os\nfrom pathlib import Path\n\nsource_file = Path(os.environ['AILOOT_TEXT_FILE'])\nif not source_file.is_file():\n raise SystemExit('Set AILOOT_TEXT_FILE to your actual UTF-8 document')\ntext = source_file.read_text(encoding='utf-8')\nif not text.strip():\n raise SystemExit('Choose a document containing text')\npayload = {'model': 'openai/gpt-6-luna-pro', 'max_tokens': 4096, 'messages': [{'role': 'user', 'content': 'Summarize the incident report.'}]}\npayload['messages'][0]['content'] = 'Summarize this material and quote supporting source passages.\\n\\n' + text\nPath('request.json').write_text(json.dumps(payload, ensure_ascii=False), encoding='utf-8')\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/messages \\\n -H "x-api-key: $AILOOT_API_KEY" -H 'anthropic-version: 2023-06-01' -H 'Content-Type: application/json' \\\n --data-binary @request.json -D response-headers.txt -o result.json\npython3 - <<'PY_RESULT'\nimport json\nfrom pathlib import Path\n\nresult = json.loads(Path('result.json').read_text(encoding='utf-8'))\nprint(json.dumps(result, ensure_ascii=False, indent=2))\nPY_RESULT\nimport json, os\nfrom pathlib import Path\n\nsource_file = Path(os.environ['AILOOT_TEXT_FILE'])\nif not source_file.is_file():\n raise SystemExit('Set AILOOT_TEXT_FILE to your actual UTF-8 document')\ntext = source_file.read_text(encoding='utf-8')\nif not text.strip():\n raise SystemExit('Choose a document containing text')\npayload = {'model': 'openai/gpt-6-luna-pro', 'max_tokens': 4096, 'messages': [{'role': 'user', 'content': 'Summarize the incident report.'}]}\npayload['messages'][0]['content'] = 'Summarize this material and quote supporting source passages.\\n\\n' + text\nfrom urllib.request import Request, urlopen\nfrom urllib.error import HTTPError\n\napi_key = os.environ['AILOOT_API_KEY']\nif not api_key.strip():\n raise SystemExit('Set AILOOT_API_KEY before making a request')\nheaders = {'x-api-key': 'PLACEHOLDER_KEY', 'anthropic-version': '2023-06-01', 'Content-Type': 'application/json'}\nheaders['x-api-key'] = api_key\nrequest = Request('https://api.ailoot.ai/v1/messages', data=json.dumps(payload, ensure_ascii=False).encode('utf-8'), headers=headers, method='POST')\ntry:\n with urlopen(request, timeout=120) as response:\n raw = response.read()\n Path('response-headers.txt').write_text(str(response.headers), encoding='utf-8')\nexcept HTTPError as error:\n Path('result.json').write_bytes(error.read())\n Path('response-headers.txt').write_text(str(error.headers), encoding='utf-8')\n raise\nPath('result.json').write_bytes(raw)\nimport json\nfrom pathlib import Path\n\nresult = json.loads(Path('result.json').read_text(encoding='utf-8'))\nprint(json.dumps(result, ensure_ascii=False, indent=2))\njson or SSE
Keep every complete returned response, file and content part.
Keep all content blocks, thinking signatures and actual tool-use IDs. Assemble all SSE block deltas; a first text block is not the complete output.
Keep request/response IDs and headers. No generic text job retrieval URL is supplied.
| Field | Type |
|---|---|
Fieldid | Typestring |
Fieldmodel | Typestring |
Fieldcontent[] | Typetext|thinking|tool_use|other content block |
Fieldstop_reason | Typestring|null |
Fieldusage | Typeobject |
| Response | Condition | Next step |
|---|---|---|
Response400 · invalid_request_error | ConditionInvalid required fields, material representation or parameter value | Next stepCorrect the indicated fields before a new creation request. |
Ailoot Ultra openai/gpt-6-luna-pro
Use the method paths shown above.
openai/gpt-6-luna-proGPT-6 Luna with Pro reasoning for focused tasks that need additional analysis before a final answer. The base model accepts text and images, making it useful for rule-based document handling, visual questions and concise written results.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
GPT-6 Luna Pro denotes OpenAI’s GPT-6 Luna with Pro reasoning. Luna is designed for focused tasks and high-volume workloads; its inputs are text and images and its output is text. Pro adds model work when developing the final answer. It is an execution mode applied to the chosen base model, distinct from the reasoning-effort setting that controls the amount of analysis within that mode.
A practical application is processing a narrowly defined request with several rules or exceptions. Supply the request, the categories it may belong to and the information that determines the next action. Ask for the category, the relevant facts and a short proposed response. An illustrative task is to classify a document request, identify a missing field and draft a message asking for that field. The task remains focused even when its conditions require careful consideration.
For an image-based question, provide a clear screenshot or other visual material and identify the detail to inspect. You can request an explanation of a visible interface state together with a short instruction for the reader. For written material, define the fields or statements that must survive the transformation, the allowed categories and the expected answer format. A small set of representative examples can clarify the boundary between similar categories without making the model infer your rules.
OpenAI specifies a 1,050,000-token context window for GPT-6 Luna, with a maximum input of 922,000 tokens and a maximum output of 128,000 tokens. The documented knowledge cutoff is May 18, 2026. The base model supports none, low, medium, high, xhigh and max reasoning effort, with medium as the default. Reasoning occupies token space as well as the final answer, so the task brief and supplied material should leave room for analysis and the requested result.
The producer documents Pro in the Responses API for difficult requests that allow additional latency and model activity. GPT-6 Luna’s built-in tools and reasoning with function calls use Responses; in the producer’s Chat Completions interface, function calling is supported only with reasoning effort set to none. The base model lists streaming, Structured Outputs, web and file search, function calling and prompt caching. Its tool set also includes image generation, code interpretation, shell and patch operations, skills, computer use, MCP connections and tool search. Your application determines and executes the operations it exposes.
Use a precise output contract for the final task: the decision, the fields supporting it and any information still missing. For example, ask for one category, a brief justification tied to the supplied rule and the next question to send to the reader. Review the result against those rules rather than adding an unrestricted discussion. This workflow uses Luna’s focused-task role while reserving Pro’s extra work for a request whose dependencies or exceptions need fuller examination.
POST /v1/responsestexttext| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits openai/gpt-6-luna-pro | Default— |
Fieldinput | Typestring|input_item[] | RequirementRequired | Values & limits | Default— |
Fieldinstructions | Typestring | RequirementOptional | Values & limits | Default— |
Fieldinput[].type | Typestring | RequirementOptional | Values & limits message, reasoning, function_call, function_call_output | Default— |
Fieldinput[].content | Typestring|content_part[] | RequirementOptional | Values & limits | Default— |
Fieldinput[].call_id | Typestring | RequirementOptional | Values & limits Applies when: function_call_output; retain actual returned call_id | Default— |
Fieldinput[].output | Typestring|content_part[] | RequirementOptional | Values & limits Applies when: function_call_output | Default— |
Fieldmax_output_tokens | Typeinteger | RequirementOptional | Values & limits Minimum: 1 Model-specific | Default— |
Fieldstream | Typeboolean | RequirementOptional | Values & limits | Defaultfalse |
Fieldtemperature | Typenumber | RequirementOptional | Values & limits Model-specific | Default— |
Fieldtop_p | Typenumber | RequirementOptional | Values & limits Model-specific | Default— |
Fieldtools | Typetool[] | RequirementOptional | Values & limits Model-specific | Default— |
Fieldtool_choice | Typestring|object | RequirementOptional | Values & limits Model-specific | Default— |
Fieldparallel_tool_calls | Typeboolean | RequirementOptional | Values & limits Model-specific | Default— |
Fieldreasoning.effort | Typestring | RequirementOptional | Values & limits Model-specific | Default— |
Fieldreasoning.summary | Typestring | RequirementOptional | Values & limits Model-specific | Default— |
Fieldtext.format | Typeobject | RequirementOptional | Values & limits Model-specific | Default— |
Fieldtext.verbosity | Typestring | RequirementOptional | Values & limits Model-specific | Default— |
Fieldinclude | Typestring[] | RequirementOptional | Values & limits Model-specific | Default— |
Fieldstore | Typeboolean | RequirementOptional | Values & limits Model-specific | Default— |
Fieldprevious_response_id | Typestring | RequirementNot supported | Values & limits ID requirement: Only an actual response ID from a supported continuation Conditions: Replay the actual conversation history with each request. | Default— |
Fieldservice_tier | Typestring | RequirementOptional | Values & limits Model-specific | Default— |
Fieldprompt_cache_key | Typestring | RequirementOptional | Values & limits Model-specific | Default— |
Fieldprompt_cache_options | Typeobject | RequirementOptional | Values & limits Model-specific | Default— |
Text · UTF8 text
input text or input_text part
Text + image
{type:input_image,image_url}
| Parameter | Contract |
|---|---|
Parametermaterial_types | Contracttext |
Parameterrequired | Contractmodel, input |
Parameteroptional_controls | Contractnull |
Parametercontent_type | Contractapplication/json |
Parametertext_file_material | ContractRead the actual UTF-8 document and put its text in the indicated input or message content string. |
Lifecycle: exact retained published method
Send the exact model ID and required fields.
Keep every returned item and its original identifiers.
Tariff references do not add request options. Supported parameters are listed for the selected method.
Match each charge to the conditions shown. Use one rate for each set of alternative configurations.
| Configuration | Rate schedule | Context tokens, more than | Context tokens, up to and including | Charge |
|---|---|---|---|---|
| ConfigurationBase token rates | Rate scheduleBase rate | Context tokens, more than— | Context tokens, up to and includingNo upper bound | Charge Input · Input tokens $0.0002 / 1K tokens Output · Output tokens $0.001 / 1K tokens Input · Cache read $0.00002 / 1K tokens Input · Cache write $0.00025 / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
| ConfigurationRequest-length tier 1 | Rate schedule— | Context tokens, more than0 | Context tokens, up to and including272,000 | Charge Input · Input tokens $0.0002 / 1K tokens Output · Output tokens $0.001 / 1K tokens Input · Cache read $0.00002 / 1K tokens Input · Cache write $0.00025 / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
| ConfigurationRequest-length tier 2 | Rate schedule— | Context tokens, more than272,000 | Context tokens, up to and includingNo upper bound | Charge Input · Input tokens $0.0004 / 1K tokens Output · Output tokens $0.0015 / 1K tokens Input · Cache read $0.00004 / 1K tokens Input · Cache write $0.0005 / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
{\n "model": "openai/gpt-6-luna-pro",\n "input": [\n {\n "content": "Hello, how are you?",\n "role": "user",\n "type": "message"\n }\n ]\n}set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\n: "${AILOOT_TEXT_FILE:?Set your actual UTF-8 document}"\nexport AILOOT_TEXT_FILE\npython3 - <<'PY_REQUEST'\nimport json, os\nfrom pathlib import Path\n\nsource_file = Path(os.environ['AILOOT_TEXT_FILE'])\nif not source_file.is_file():\n raise SystemExit('Set AILOOT_TEXT_FILE to your actual UTF-8 document')\ntext = source_file.read_text(encoding='utf-8')\nif not text.strip():\n raise SystemExit('Choose a document containing text')\npayload = {'model': 'openai/gpt-6-luna-pro', 'input': [{'content': 'Hello, how are you?', 'role': 'user', 'type': 'message'}]}\npayload['input'][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/responses \\\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\n\nresult = json.loads(Path('result.json').read_text(encoding='utf-8'))\nprint(json.dumps(result, ensure_ascii=False, indent=2))\nPY_RESULT\nimport json, os\nfrom pathlib import Path\n\nsource_file = Path(os.environ['AILOOT_TEXT_FILE'])\nif not source_file.is_file():\n raise SystemExit('Set AILOOT_TEXT_FILE to your actual UTF-8 document')\ntext = source_file.read_text(encoding='utf-8')\nif not text.strip():\n raise SystemExit('Choose a document containing text')\npayload = {'model': 'openai/gpt-6-luna-pro', 'input': [{'content': 'Hello, how are you?', 'role': 'user', 'type': 'message'}]}\npayload['input'][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 = {'Authorization': 'Bearer PLACEHOLDER_KEY', 'Content-Type': 'application/json'}\nheaders['Authorization'] = 'Bearer ' + api_key\nrequest = Request('https://api.ailoot.ai/v1/responses', 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 every output item and content part, actual function call_id, encrypted reasoning and usage. In streaming retain complete event/item identity.
Keep request/response IDs and headers. No generic text job retrieval URL is supplied.
| Field | Type |
|---|---|
Fieldid | Typestring |
Fieldstatus | Typestring |
Fieldoutput[] | Typemessage|reasoning|function_call|other item |
Fieldoutput[].content[] | Typecontent part |
Fieldusage | Typeobject |
| Response | Condition | Next step |
|---|---|---|
Response400 · invalid_request_error | ConditionInvalid required fields, material representation or parameter value | Next stepCorrect the indicated fields before a new creation request. |
Ailoot Ultra openai/gpt-6-luna-pro
Use the method paths shown above.
Technical example in English
Find missing request details\n\nProcess this equipment request using the supplied rules. Every request needs item, quantity and delivery site. Urgent requests also need a reason. Message: "Urgent: please send 4 replacement keyboards." Return the supplied fields, missing required fields and a brief reply asking only for the missing details. Do not invent a site or urgency reason.A compact field table, missing-field list and targeted clarification message.Check that quantity four and keyboards are retained, while delivery site and urgent reason stay missing.