GPT-6 Astra Pro denotes OpenAI’s GPT-6 Astra with Pro reasoning. The base Astra model is designed for demanding reasoning, coding, research, computer use and professional work. It accepts text and images and produces text. Pro adds model work when developing the final answer, while reasoning effort controls the amount of analysis within the chosen execution mode. Pro changes the execution mode while GPT-6 Astra remains the underlying model.
The manufacturer’s Astra release describes workflows that turn relevant material into documents, spreadsheets, presentations and analysis. It emphasizes following templates and writing or visual style while selecting the context that matters to the task. For a professional document, provide the source material, the template requirements and the decision the reader needs to make. An illustrative request is to reconcile two sets of project requirements and produce a brief that separates agreed facts, conflicting evidence and the conditions for a recommendation.
For engineering or research work, specify the objective, the evidence to inspect and what would count as completion. A coding task can include the required behavior, related files and constraints; a research task can include the question and the criteria for weighing the supplied material. Astra’s documented computer-use and browsing capabilities connect the model’s decisions with an environment that supplies the corresponding tools. The environment’s actual outputs are evidence for the next step, distinct from the model’s proposed actions.
OpenAI’s GPT-6 Astra specification lists a 1,050,000-token context window, a maximum input of 922,000 tokens and a maximum output of 128,000 tokens, with a knowledge cutoff of April 30, 2026. Its reasoning-effort choices are low, medium, high, xhigh and max; none is unsupported. Reasoning tokens and the visible result both occupy context space. OpenAI documents Pro in the Responses API for difficult work that can accommodate additional latency and model activity; mode and effort remain independent settings.
The base model lists streaming, Structured Outputs, function calling, web and file search, image-generation tooling, code interpretation, hosted shell and patch operations, skills, computer use, MCP connections and tool search. Function calling uses the producer’s Responses interface. The GPT-6 guide also describes asynchronous tool work and steering an active turn. Applications execute their own operations, manage pending work and return the resulting information; the model’s own output remains text even when a separate tool creates another artifact.
The Astra release describes adapting to new requirements while keeping the broader goal in view. It also distinguishes defensive code review and patching from advanced exploit-development work, which the model may refuse, and describes automatic checks that can interrupt a task. Prepare a request around the intended result, permitted work and evidence, rather than treating extra reasoning as a substitute for missing source material. A useful final artifact states the conclusion or proposed change, its basis in the supplied information and any question that would materially alter the decision.
", "aboutText": "GPT-6 Astra Pro denotes OpenAI’s GPT-6 Astra with Pro reasoning. The base Astra model is designed for demanding reasoning, coding, research, computer use and professional work. It accepts text and images and produces text. Pro adds model work when developing the final answer, while reasoning effort controls the amount of analysis within the chosen execution mode. Pro changes the execution mode while GPT-6 Astra remains the underlying model.\n\nThe manufacturer’s Astra release describes workflows that turn relevant material into documents, spreadsheets, presentations and analysis. It emphasizes following templates and writing or visual style while selecting the context that matters to the task. For a professional document, provide the source material, the template requirements and the decision the reader needs to make. An illustrative request is to reconcile two sets of project requirements and produce a brief that separates agreed facts, conflicting evidence and the conditions for a recommendation.\n\nFor engineering or research work, specify the objective, the evidence to inspect and what would count as completion. A coding task can include the required behavior, related files and constraints; a research task can include the question and the criteria for weighing the supplied material. Astra’s documented computer-use and browsing capabilities connect the model’s decisions with an environment that supplies the corresponding tools. The environment’s actual outputs are evidence for the next step, distinct from the model’s proposed actions.\n\nOpenAI’s GPT-6 Astra specification lists a 1,050,000-token context window, a maximum input of 922,000 tokens and a maximum output of 128,000 tokens, with a knowledge cutoff of April 30, 2026. Its reasoning-effort choices are low, medium, high, xhigh and max; none is unsupported. Reasoning tokens and the visible result both occupy context space. OpenAI documents Pro in the Responses API for difficult work that can accommodate additional latency and model activity; mode and effort remain independent settings.\n\nThe base model lists streaming, Structured Outputs, function calling, web and file search, image-generation tooling, code interpretation, hosted shell and patch operations, skills, computer use, MCP connections and tool search. Function calling uses the producer’s Responses interface. The GPT-6 guide also describes asynchronous tool work and steering an active turn. Applications execute their own operations, manage pending work and return the resulting information; the model’s own output remains text even when a separate tool creates another artifact.\n\nThe Astra release describes adapting to new requirements while keeping the broader goal in view. It also distinguishes defensive code review and patching from advanced exploit-development work, which the model may refuse, and describes automatic checks that can interrupt a task. Prepare a request around the intended result, permitted work and evidence, rather than treating extra reasoning as a substitute for missing source material. A useful final artifact states the conclusion or proposed change, its basis in the supplied information and any question that would materially alter the decision.", "editorialOverlay": { "copy_fields": { "practical_html": "Technical example in English
Reconcile conflicting launch requirements\n\nWrite a decision brief using this template: Decision needed; Agreed facts; Conflict; Recommendation conditions. Product requirement P1 says launch must include CSV export. Security requirement S1 prohibits exporting customer email addresses. Current design D1 exports name and email only. Reconcile the requirements, propose a compliant export scope and list the decision still needed about useful fields.A brief under the four supplied headings, with P1, S1 and D1 references beside relevant findings.Check that the proposed scope preserves an export feature while excluding emails and does not invent an approved field list.openai/gpt-6-astra-proGPT-6 Astra with Pro reasoning for demanding research, coding and professional work. Pro adds model work before the final answer; the base model accepts text and images and supports multi-step work in a tool-connected environment.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
GPT-6 Astra Pro denotes OpenAI’s GPT-6 Astra with Pro reasoning. The base Astra model is designed for demanding reasoning, coding, research, computer use and professional work. It accepts text and images and produces text. Pro adds model work when developing the final answer, while reasoning effort controls the amount of analysis within the chosen execution mode. Pro changes the execution mode while GPT-6 Astra remains the underlying model.
The manufacturer’s Astra release describes workflows that turn relevant material into documents, spreadsheets, presentations and analysis. It emphasizes following templates and writing or visual style while selecting the context that matters to the task. For a professional document, provide the source material, the template requirements and the decision the reader needs to make. An illustrative request is to reconcile two sets of project requirements and produce a brief that separates agreed facts, conflicting evidence and the conditions for a recommendation.
For engineering or research work, specify the objective, the evidence to inspect and what would count as completion. A coding task can include the required behavior, related files and constraints; a research task can include the question and the criteria for weighing the supplied material. Astra’s documented computer-use and browsing capabilities connect the model’s decisions with an environment that supplies the corresponding tools. The environment’s actual outputs are evidence for the next step, distinct from the model’s proposed actions.
OpenAI’s GPT-6 Astra specification lists a 1,050,000-token context window, a maximum input of 922,000 tokens and a maximum output of 128,000 tokens, with a knowledge cutoff of April 30, 2026. Its reasoning-effort choices are low, medium, high, xhigh and max; none is unsupported. Reasoning tokens and the visible result both occupy context space. OpenAI documents Pro in the Responses API for difficult work that can accommodate additional latency and model activity; mode and effort remain independent settings.
The base model lists streaming, Structured Outputs, function calling, web and file search, image-generation tooling, code interpretation, hosted shell and patch operations, skills, computer use, MCP connections and tool search. Function calling uses the producer’s Responses interface. The GPT-6 guide also describes asynchronous tool work and steering an active turn. Applications execute their own operations, manage pending work and return the resulting information; the model’s own output remains text even when a separate tool creates another artifact.
The Astra release describes adapting to new requirements while keeping the broader goal in view. It also distinguishes defensive code review and patching from advanced exploit-development work, which the model may refuse, and describes automatic checks that can interrupt a task. Prepare a request around the intended result, permitted work and evidence, rather than treating extra reasoning as a substitute for missing source material. A useful final artifact states the conclusion or proposed change, its basis in the supplied information and any question that would materially alter the decision.
POST /v1/chat/completionstexttext| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits openai/gpt-6-astra-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 $0.02 / 1K tokens Output · Output tokens $0.1 / 1K tokens Input · Cache read $0.002 / 1K tokens Input · Cache write $0.025 / 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 $0.02 / 1K tokens Output · Output tokens $0.1 / 1K tokens Input · Cache read $0.002 / 1K tokens Input · Cache write $0.025 / 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 $0.04 / 1K tokens Output · Output tokens $0.15 / 1K tokens Input · Cache read $0.004 / 1K tokens Input · Cache write $0.05 / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
set -eu\n: "${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-astra-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-astra-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-astra-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-astra-pro
Use the method paths shown above.
openai/gpt-6-astra-proGPT-6 Astra with Pro reasoning for demanding research, coding and professional work. Pro adds model work before the final answer; the base model accepts text and images and supports multi-step work in a tool-connected environment.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
GPT-6 Astra Pro denotes OpenAI’s GPT-6 Astra with Pro reasoning. The base Astra model is designed for demanding reasoning, coding, research, computer use and professional work. It accepts text and images and produces text. Pro adds model work when developing the final answer, while reasoning effort controls the amount of analysis within the chosen execution mode. Pro changes the execution mode while GPT-6 Astra remains the underlying model.
The manufacturer’s Astra release describes workflows that turn relevant material into documents, spreadsheets, presentations and analysis. It emphasizes following templates and writing or visual style while selecting the context that matters to the task. For a professional document, provide the source material, the template requirements and the decision the reader needs to make. An illustrative request is to reconcile two sets of project requirements and produce a brief that separates agreed facts, conflicting evidence and the conditions for a recommendation.
For engineering or research work, specify the objective, the evidence to inspect and what would count as completion. A coding task can include the required behavior, related files and constraints; a research task can include the question and the criteria for weighing the supplied material. Astra’s documented computer-use and browsing capabilities connect the model’s decisions with an environment that supplies the corresponding tools. The environment’s actual outputs are evidence for the next step, distinct from the model’s proposed actions.
OpenAI’s GPT-6 Astra specification lists a 1,050,000-token context window, a maximum input of 922,000 tokens and a maximum output of 128,000 tokens, with a knowledge cutoff of April 30, 2026. Its reasoning-effort choices are low, medium, high, xhigh and max; none is unsupported. Reasoning tokens and the visible result both occupy context space. OpenAI documents Pro in the Responses API for difficult work that can accommodate additional latency and model activity; mode and effort remain independent settings.
The base model lists streaming, Structured Outputs, function calling, web and file search, image-generation tooling, code interpretation, hosted shell and patch operations, skills, computer use, MCP connections and tool search. Function calling uses the producer’s Responses interface. The GPT-6 guide also describes asynchronous tool work and steering an active turn. Applications execute their own operations, manage pending work and return the resulting information; the model’s own output remains text even when a separate tool creates another artifact.
The Astra release describes adapting to new requirements while keeping the broader goal in view. It also distinguishes defensive code review and patching from advanced exploit-development work, which the model may refuse, and describes automatic checks that can interrupt a task. Prepare a request around the intended result, permitted work and evidence, rather than treating extra reasoning as a substitute for missing source material. A useful final artifact states the conclusion or proposed change, its basis in the supplied information and any question that would materially alter the decision.
POST /v1/messagestexttext| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits openai/gpt-6-astra-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 $0.02 / 1K tokens Output · Output tokens $0.1 / 1K tokens Input · Cache read $0.002 / 1K tokens Input · Cache write $0.025 / 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 $0.02 / 1K tokens Output · Output tokens $0.1 / 1K tokens Input · Cache read $0.002 / 1K tokens Input · Cache write $0.025 / 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 $0.04 / 1K tokens Output · Output tokens $0.15 / 1K tokens Input · Cache read $0.004 / 1K tokens Input · Cache write $0.05 / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
{\n "model": "openai/gpt-6-astra-pro",\n "max_tokens": 4096,\n "messages": [\n {\n "role": "user",\n "content": "Summarize the incident report."\n }\n ]\n}set -eu\n: "${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-astra-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-astra-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-astra-pro
Use the method paths shown above.
openai/gpt-6-astra-proGPT-6 Astra with Pro reasoning for demanding research, coding and professional work. Pro adds model work before the final answer; the base model accepts text and images and supports multi-step work in a tool-connected environment.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
GPT-6 Astra Pro denotes OpenAI’s GPT-6 Astra with Pro reasoning. The base Astra model is designed for demanding reasoning, coding, research, computer use and professional work. It accepts text and images and produces text. Pro adds model work when developing the final answer, while reasoning effort controls the amount of analysis within the chosen execution mode. Pro changes the execution mode while GPT-6 Astra remains the underlying model.
The manufacturer’s Astra release describes workflows that turn relevant material into documents, spreadsheets, presentations and analysis. It emphasizes following templates and writing or visual style while selecting the context that matters to the task. For a professional document, provide the source material, the template requirements and the decision the reader needs to make. An illustrative request is to reconcile two sets of project requirements and produce a brief that separates agreed facts, conflicting evidence and the conditions for a recommendation.
For engineering or research work, specify the objective, the evidence to inspect and what would count as completion. A coding task can include the required behavior, related files and constraints; a research task can include the question and the criteria for weighing the supplied material. Astra’s documented computer-use and browsing capabilities connect the model’s decisions with an environment that supplies the corresponding tools. The environment’s actual outputs are evidence for the next step, distinct from the model’s proposed actions.
OpenAI’s GPT-6 Astra specification lists a 1,050,000-token context window, a maximum input of 922,000 tokens and a maximum output of 128,000 tokens, with a knowledge cutoff of April 30, 2026. Its reasoning-effort choices are low, medium, high, xhigh and max; none is unsupported. Reasoning tokens and the visible result both occupy context space. OpenAI documents Pro in the Responses API for difficult work that can accommodate additional latency and model activity; mode and effort remain independent settings.
The base model lists streaming, Structured Outputs, function calling, web and file search, image-generation tooling, code interpretation, hosted shell and patch operations, skills, computer use, MCP connections and tool search. Function calling uses the producer’s Responses interface. The GPT-6 guide also describes asynchronous tool work and steering an active turn. Applications execute their own operations, manage pending work and return the resulting information; the model’s own output remains text even when a separate tool creates another artifact.
The Astra release describes adapting to new requirements while keeping the broader goal in view. It also distinguishes defensive code review and patching from advanced exploit-development work, which the model may refuse, and describes automatic checks that can interrupt a task. Prepare a request around the intended result, permitted work and evidence, rather than treating extra reasoning as a substitute for missing source material. A useful final artifact states the conclusion or proposed change, its basis in the supplied information and any question that would materially alter the decision.
POST /v1/responsestexttext| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits openai/gpt-6-astra-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 $0.02 / 1K tokens Output · Output tokens $0.1 / 1K tokens Input · Cache read $0.002 / 1K tokens Input · Cache write $0.025 / 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 $0.02 / 1K tokens Output · Output tokens $0.1 / 1K tokens Input · Cache read $0.002 / 1K tokens Input · Cache write $0.025 / 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 $0.04 / 1K tokens Output · Output tokens $0.15 / 1K tokens Input · Cache read $0.004 / 1K tokens Input · Cache write $0.05 / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
{\n "model": "openai/gpt-6-astra-pro",\n "input": [\n {\n "content": "Hello, how are you?",\n "role": "user",\n "type": "message"\n }\n ]\n}set -eu\n: "${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-astra-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-astra-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-astra-pro
Use the method paths shown above.
Technical example in English
Reconcile conflicting launch requirements\n\nWrite a decision brief using this template: Decision needed; Agreed facts; Conflict; Recommendation conditions. Product requirement P1 says launch must include CSV export. Security requirement S1 prohibits exporting customer email addresses. Current design D1 exports name and email only. Reconcile the requirements, propose a compliant export scope and list the decision still needed about useful fields.A brief under the four supplied headings, with P1, S1 and D1 references beside relevant findings.Check that the proposed scope preserves an export feature while excluding emails and does not invent an approved field list.