GPT-6.1 Sol Pro denotes OpenAI’s GPT-6.1 Sol with Pro reasoning. The base model is designed for complex coding, computer use and professional work. It accepts text and images and produces text. Pro adds model work to develop the final answer; it describes how the selected model works on the request, while reasoning effort controls the amount of reasoning within that mode. This distinction keeps the identity of GPT-6.1 Sol separate from the Pro execution setting.
For a demanding engineering task, supply the required behavior, the relevant files or code excerpts and the constraints that the change must preserve. An illustrative request is to examine two possible implementations, identify the dependencies each affects and produce a proposed change with a concise explanation of the trade-offs. For professional analysis, supply the documents, the decision to be made and the criteria for comparing alternatives. If an image contains important evidence, identify the interface element, diagram or detail that the answer should address.
OpenAI lists a 1,050,000-token context window for the base model, a maximum input of 922,000 tokens and a maximum output of 128,000 tokens. Its documented knowledge cutoff is April 30, 2026. Reasoning also occupies token space, so a long input and a long visible answer need room for the model’s analysis. Prepare the relevant material with clear labels and definitions, and specify the deliverable: a code change, a decision memo, a procedure or a structured set of findings.
The base model’s documented reasoning-effort choices are low, medium, high, xhigh and max; medium is the default. It does not support none or minimal. OpenAI documents Pro in the Responses API for difficult work that can accommodate additional latency and model activity. For GPT-6.1 Sol, tool calling uses Responses; the producer’s Chat Completions interface accepts requests without tools. These distinctions matter when designing a workflow around the model rather than treating all interfaces and reasoning settings as interchangeable.
The producer lists streaming, Structured Outputs and function calling, together with web and file search, image-generation tooling, a code interpreter, shell and patch tools, skills, computer use, MCP connections and tool search. The base model’s output remains text; an image-generation tool is a separate operation. An application defines the available tools, executes its own operations and returns their results so the model can continue. GPT-6 documentation also describes asynchronous tool work, steering an active turn, retaining reasoning between calls and compacting a long conversation.
Give the model a clear completion criterion and the evidence it should use to check the result. In a code review, ask for the affected behavior and a concrete explanation of each significant issue. In a document decision, ask for the supplied facts, the selected approach and the conditions on which the conclusion depends. A useful final artifact makes the decision and its basis explicit, allowing the next person or application step to act on a specific result.
", "aboutText": "GPT-6.1 Sol Pro denotes OpenAI’s GPT-6.1 Sol with Pro reasoning. The base model is designed for complex coding, computer use and professional work. It accepts text and images and produces text. Pro adds model work to develop the final answer; it describes how the selected model works on the request, while reasoning effort controls the amount of reasoning within that mode. This distinction keeps the identity of GPT-6.1 Sol separate from the Pro execution setting.\n\nFor a demanding engineering task, supply the required behavior, the relevant files or code excerpts and the constraints that the change must preserve. An illustrative request is to examine two possible implementations, identify the dependencies each affects and produce a proposed change with a concise explanation of the trade-offs. For professional analysis, supply the documents, the decision to be made and the criteria for comparing alternatives. If an image contains important evidence, identify the interface element, diagram or detail that the answer should address.\n\nOpenAI lists a 1,050,000-token context window for the base model, a maximum input of 922,000 tokens and a maximum output of 128,000 tokens. Its documented knowledge cutoff is April 30, 2026. Reasoning also occupies token space, so a long input and a long visible answer need room for the model’s analysis. Prepare the relevant material with clear labels and definitions, and specify the deliverable: a code change, a decision memo, a procedure or a structured set of findings.\n\nThe base model’s documented reasoning-effort choices are low, medium, high, xhigh and max; medium is the default. It does not support none or minimal. OpenAI documents Pro in the Responses API for difficult work that can accommodate additional latency and model activity. For GPT-6.1 Sol, tool calling uses Responses; the producer’s Chat Completions interface accepts requests without tools. These distinctions matter when designing a workflow around the model rather than treating all interfaces and reasoning settings as interchangeable.\n\nThe producer lists streaming, Structured Outputs and function calling, together with web and file search, image-generation tooling, a code interpreter, shell and patch tools, skills, computer use, MCP connections and tool search. The base model’s output remains text; an image-generation tool is a separate operation. An application defines the available tools, executes its own operations and returns their results so the model can continue. GPT-6 documentation also describes asynchronous tool work, steering an active turn, retaining reasoning between calls and compacting a long conversation.\n\nGive the model a clear completion criterion and the evidence it should use to check the result. In a code review, ask for the affected behavior and a concrete explanation of each significant issue. In a document decision, ask for the supplied facts, the selected approach and the conditions on which the conclusion depends. A useful final artifact makes the decision and its basis explicit, allowing the next person or application step to act on a specific result.", "editorialOverlay": { "copy_fields": { "practical_html": "Technical example in English
Compare duplicate-prevention designs\n\nCompare two job designs using these supplied facts. A records completion after a remote send. B records 'started' before the send and skips all started jobs. The process may crash before or after the send, and the receiver supports an idempotency key. Propose a recoverable design, explain the failure windows and give scenario checks without performing any sends.A design comparison, proposed state transitions and crash-window checks using the stated receiver capability.Check the proposal handles both unsent started jobs and sends completed before local recording.openai/gpt-6.1-sol-proGPT-6.1 Sol with Pro reasoning for complex coding, computer use and professional analysis. Pro adds model work before the final answer, while the underlying model handles text and images and produces text.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
GPT-6.1 Sol Pro denotes OpenAI’s GPT-6.1 Sol with Pro reasoning. The base model is designed for complex coding, computer use and professional work. It accepts text and images and produces text. Pro adds model work to develop the final answer; it describes how the selected model works on the request, while reasoning effort controls the amount of reasoning within that mode. This distinction keeps the identity of GPT-6.1 Sol separate from the Pro execution setting.
For a demanding engineering task, supply the required behavior, the relevant files or code excerpts and the constraints that the change must preserve. An illustrative request is to examine two possible implementations, identify the dependencies each affects and produce a proposed change with a concise explanation of the trade-offs. For professional analysis, supply the documents, the decision to be made and the criteria for comparing alternatives. If an image contains important evidence, identify the interface element, diagram or detail that the answer should address.
OpenAI lists a 1,050,000-token context window for the base model, a maximum input of 922,000 tokens and a maximum output of 128,000 tokens. Its documented knowledge cutoff is April 30, 2026. Reasoning also occupies token space, so a long input and a long visible answer need room for the model’s analysis. Prepare the relevant material with clear labels and definitions, and specify the deliverable: a code change, a decision memo, a procedure or a structured set of findings.
The base model’s documented reasoning-effort choices are low, medium, high, xhigh and max; medium is the default. It does not support none or minimal. OpenAI documents Pro in the Responses API for difficult work that can accommodate additional latency and model activity. For GPT-6.1 Sol, tool calling uses Responses; the producer’s Chat Completions interface accepts requests without tools. These distinctions matter when designing a workflow around the model rather than treating all interfaces and reasoning settings as interchangeable.
The producer lists streaming, Structured Outputs and function calling, together with web and file search, image-generation tooling, a code interpreter, shell and patch tools, skills, computer use, MCP connections and tool search. The base model’s output remains text; an image-generation tool is a separate operation. An application defines the available tools, executes its own operations and returns their results so the model can continue. GPT-6 documentation also describes asynchronous tool work, steering an active turn, retaining reasoning between calls and compacting a long conversation.
Give the model a clear completion criterion and the evidence it should use to check the result. In a code review, ask for the affected behavior and a concrete explanation of each significant issue. In a document decision, ask for the supplied facts, the selected approach and the conditions on which the conclusion depends. A useful final artifact makes the decision and its basis explicit, allowing the next person or application step to act on a specific result.
POST /v1/chat/completionstexttext| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits openai/gpt-6.1-sol-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 $0.004 / 1K tokens Output · Output tokens $0.02 / 1K tokens Input · Cache read $0.0002 / 1K tokens Input · Cache write $0.005 / 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 $0.004 / 1K tokens Output · Output tokens $0.02 / 1K tokens Input · Cache read $0.0002 / 1K tokens Input · Cache write $0.005 / 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 $0.008 / 1K tokens Output · Output tokens $0.03 / 1K tokens Input · Cache read $0.0004 / 1K tokens Input · Cache write $0.01 / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{"model": "openai/gpt-6.1-sol-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-6.1-sol-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-6.1-sol-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-6.1-sol-pro
Use the method paths shown above.
openai/gpt-6.1-sol-proGPT-6.1 Sol with Pro reasoning for complex coding, computer use and professional analysis. Pro adds model work before the final answer, while the underlying model handles text and images and produces text.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
GPT-6.1 Sol Pro denotes OpenAI’s GPT-6.1 Sol with Pro reasoning. The base model is designed for complex coding, computer use and professional work. It accepts text and images and produces text. Pro adds model work to develop the final answer; it describes how the selected model works on the request, while reasoning effort controls the amount of reasoning within that mode. This distinction keeps the identity of GPT-6.1 Sol separate from the Pro execution setting.
For a demanding engineering task, supply the required behavior, the relevant files or code excerpts and the constraints that the change must preserve. An illustrative request is to examine two possible implementations, identify the dependencies each affects and produce a proposed change with a concise explanation of the trade-offs. For professional analysis, supply the documents, the decision to be made and the criteria for comparing alternatives. If an image contains important evidence, identify the interface element, diagram or detail that the answer should address.
OpenAI lists a 1,050,000-token context window for the base model, a maximum input of 922,000 tokens and a maximum output of 128,000 tokens. Its documented knowledge cutoff is April 30, 2026. Reasoning also occupies token space, so a long input and a long visible answer need room for the model’s analysis. Prepare the relevant material with clear labels and definitions, and specify the deliverable: a code change, a decision memo, a procedure or a structured set of findings.
The base model’s documented reasoning-effort choices are low, medium, high, xhigh and max; medium is the default. It does not support none or minimal. OpenAI documents Pro in the Responses API for difficult work that can accommodate additional latency and model activity. For GPT-6.1 Sol, tool calling uses Responses; the producer’s Chat Completions interface accepts requests without tools. These distinctions matter when designing a workflow around the model rather than treating all interfaces and reasoning settings as interchangeable.
The producer lists streaming, Structured Outputs and function calling, together with web and file search, image-generation tooling, a code interpreter, shell and patch tools, skills, computer use, MCP connections and tool search. The base model’s output remains text; an image-generation tool is a separate operation. An application defines the available tools, executes its own operations and returns their results so the model can continue. GPT-6 documentation also describes asynchronous tool work, steering an active turn, retaining reasoning between calls and compacting a long conversation.
Give the model a clear completion criterion and the evidence it should use to check the result. In a code review, ask for the affected behavior and a concrete explanation of each significant issue. In a document decision, ask for the supplied facts, the selected approach and the conditions on which the conclusion depends. A useful final artifact makes the decision and its basis explicit, allowing the next person or application step to act on a specific result.
POST /v1/messagestextjson, sse| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits openai/gpt-6.1-sol-pro | Default— |
Fieldmessages | Typemessage[] | RequirementRequired | Values & limits | Default— |
Fieldmessages[].role | Typestring | RequirementRequired | Values & limits user, assistant | Default— |
Fieldmessages[].content | Typestring | RequirementRequired | Values & limits | Default— |
Fieldmax_tokens | Typeinteger | RequirementRequired | Values & limits | Default— |
Fieldstream | Typeboolean | RequirementOptional | Values & limits | Defaultfalse |
Text · UTF-8 text
messages[].content string
| Parameter | Contract |
|---|---|
Parametercontent_type | Contractapplication/json |
Parameterrequired | Contractmodel, messages, max_tokens |
Parameteroptional_controls | Contractstream |
Parametermaterial_types | Contracttext |
Parametertext_file_material | ContractRead the actual UTF-8 document and put its text in the indicated input or message content string. |
Lifecycle: synchronous_or_streamed
Tariff references do not add request options. Supported parameters are listed for the selected method.
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 $0.004 / 1K tokens Output · Output tokens $0.02 / 1K tokens Input · Cache read $0.0002 / 1K tokens Input · Cache write $0.005 / 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 $0.004 / 1K tokens Output · Output tokens $0.02 / 1K tokens Input · Cache read $0.0002 / 1K tokens Input · Cache write $0.005 / 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 $0.008 / 1K tokens Output · Output tokens $0.03 / 1K tokens Input · Cache read $0.0004 / 1K tokens Input · Cache write $0.01 / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
{\n "model": "openai/gpt-6.1-sol-pro",\n "max_tokens": 128,\n "messages": [\n {\n "role": "user",\n "content": "Summarize the incident report."\n }\n ],\n "stream": false\n}set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\n: "${AILOOT_TEXT_FILE:?Set your actual UTF-8 document}"\nexport AILOOT_TEXT_FILE\npython3 - <<'PY_REQUEST'\nimport json, os\nfrom pathlib import Path\n\nsource_file = Path(os.environ['AILOOT_TEXT_FILE'])\nif not source_file.is_file():\n raise SystemExit('Set AILOOT_TEXT_FILE to your actual UTF-8 document')\ntext = source_file.read_text(encoding='utf-8')\nif not text.strip():\n raise SystemExit('Choose a document containing text')\npayload = {'model': 'openai/gpt-6.1-sol-pro', 'max_tokens': 128, 'messages': [{'role': 'user', 'content': 'Summarize the incident report.'}]}\npayload['messages'][0]['content'] = 'Summarize this material and quote supporting source passages.\\n\\n' + text\nPath('request.json').write_text(json.dumps(payload, ensure_ascii=False), encoding='utf-8')\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/messages \\\n -H "x-api-key: $AILOOT_API_KEY" -H 'anthropic-version: 2023-06-01' -H 'Content-Type: application/json' \\\n --data-binary @request.json -D response-headers.txt -o result.json\npython3 - <<'PY_RESULT'\nimport json\nfrom pathlib import Path\n\nresult = json.loads(Path('result.json').read_text(encoding='utf-8'))\nprint(json.dumps(result, ensure_ascii=False, indent=2))\nPY_RESULT\nimport json, os\nfrom pathlib import Path\n\nsource_file = Path(os.environ['AILOOT_TEXT_FILE'])\nif not source_file.is_file():\n raise SystemExit('Set AILOOT_TEXT_FILE to your actual UTF-8 document')\ntext = source_file.read_text(encoding='utf-8')\nif not text.strip():\n raise SystemExit('Choose a document containing text')\npayload = {'model': 'openai/gpt-6.1-sol-pro', 'max_tokens': 128, 'messages': [{'role': 'user', 'content': 'Summarize the incident report.'}]}\npayload['messages'][0]['content'] = 'Summarize this material and quote supporting source passages.\\n\\n' + text\nfrom urllib.request import Request, urlopen\nfrom urllib.error import HTTPError\n\napi_key = os.environ['AILOOT_API_KEY']\nif not api_key.strip():\n raise SystemExit('Set AILOOT_API_KEY before making a request')\nheaders = {'x-api-key': 'PLACEHOLDER_KEY', 'anthropic-version': '2023-06-01', 'Content-Type': 'application/json'}\nheaders['x-api-key'] = api_key\nrequest = Request('https://api.ailoot.ai/v1/messages', data=json.dumps(payload, ensure_ascii=False).encode('utf-8'), headers=headers, method='POST')\ntry:\n with urlopen(request, timeout=120) as response:\n raw = response.read()\n Path('response-headers.txt').write_text(str(response.headers), encoding='utf-8')\nexcept HTTPError as error:\n Path('result.json').write_bytes(error.read())\n Path('response-headers.txt').write_text(str(error.headers), encoding='utf-8')\n raise\nPath('result.json').write_bytes(raw)\nimport json\nfrom pathlib import Path\n\nresult = json.loads(Path('result.json').read_text(encoding='utf-8'))\nprint(json.dumps(result, ensure_ascii=False, indent=2))\nJSON or SSE
Keep every complete returned response, file and content part.
Keep all content blocks, thinking signatures and actual tool-use IDs. Assemble all SSE block deltas; a first text block is not the complete output.
Keep request/response IDs and headers. No generic text job retrieval URL is supplied.
| Field | Type |
|---|---|
Fieldid | Typestring |
Fieldmodel | Typestring |
Fieldcontent[] | Typetext|thinking|tool_use|other content block |
Fieldstop_reason | Typestring|null |
Fieldusage | Typeobject |
| Response | Condition | Next step |
|---|---|---|
Response400 · invalid_request_error | ConditionInvalid required fields, material representation or parameter value | Next stepCorrect the indicated fields before a new creation request. |
Ailoot Ultra openai/gpt-6.1-sol-pro
Use the method paths shown above.
Technical example in English
Compare duplicate-prevention designs\n\nCompare two job designs using these supplied facts. A records completion after a remote send. B records 'started' before the send and skips all started jobs. The process may crash before or after the send, and the receiver supports an idempotency key. Propose a recoverable design, explain the failure windows and give scenario checks without performing any sends.A design comparison, proposed state transitions and crash-window checks using the stated receiver capability.Check the proposal handles both unsent started jobs and sends completed before local recording.