GPT-6 Sol Pro denotes OpenAI’s GPT-6 Sol with Pro reasoning. The base Sol model is built for complex coding and agentic workflows, accepts text and images and produces text. Pro adds model work to develop the final answer. It is separate from reasoning effort, which controls how much analysis the model applies within the selected execution mode. GPT-6 Sol remains the base edition for this card, distinct from GPT-6.1 Sol.
For a development task, describe the behavior to implement and provide the relevant source files, examples of the input and the constraints the change must respect. An illustrative request is to trace how an input reaches a result, identify the dependencies involved and propose a change that preserves the required behavior. For a review, include the intended behavior and the code to inspect, then ask for concrete issues with an explanation of how each issue affects that behavior.
Agentic work connects several decisions through evidence from the working environment. A tool may retrieve a file, run a calculation or return the result of a check; that result becomes material for the next decision. OpenAI lists web and file search, image-generation tooling, a code interpreter, hosted shell and patch operations, skills, computer use, MCP connections and tool search for the base model. It also supports function calling, Structured Outputs, streaming and prompt caching. Applications control their available operations and return actual tool results; model-generated action text alone is not evidence that an operation has completed.
The producer’s GPT-6 Sol specification gives a 1,050,000-token context window, a maximum input of 922,000 tokens and a maximum output of 128,000 tokens. Its documented knowledge cutoff is April 20, 2026. The base reasoning-effort choices are none, low, medium, high, xhigh and max, with medium as the default. The context must accommodate the task, relevant tool results and reasoning, as well as the visible answer. Keep definitions and completion criteria near the material they govern, especially in a long engineering request.
OpenAI documents Pro in the Responses API for difficult work that can accommodate additional latency and model activity. For GPT-6 Sol, built-in tools and reasoning with function calls use Responses. In the producer’s Chat Completions interface, function calling is supported only when reasoning effort is none. The GPT-6 guide also describes asynchronous tool work, steering an active turn and retaining reasoning between calls. These are workflow capabilities with their own interface requirements.
Specify a concrete final artifact and how it should be checked. You might request a proposed patch, the behavior it changes and the evidence needed to verify it, or a comparison of implementation options tied to the supplied constraints. If an image supplies interface context, state which visual detail matters. The final response should connect the significant choices to the engineering goal and distinguish a proposed action from a result already obtained.
", "aboutText": "GPT-6 Sol Pro denotes OpenAI’s GPT-6 Sol with Pro reasoning. The base Sol model is built for complex coding and agentic workflows, accepts text and images and produces text. Pro adds model work to develop the final answer. It is separate from reasoning effort, which controls how much analysis the model applies within the selected execution mode. GPT-6 Sol remains the base edition for this card, distinct from GPT-6.1 Sol.\n\nFor a development task, describe the behavior to implement and provide the relevant source files, examples of the input and the constraints the change must respect. An illustrative request is to trace how an input reaches a result, identify the dependencies involved and propose a change that preserves the required behavior. For a review, include the intended behavior and the code to inspect, then ask for concrete issues with an explanation of how each issue affects that behavior.\n\nAgentic work connects several decisions through evidence from the working environment. A tool may retrieve a file, run a calculation or return the result of a check; that result becomes material for the next decision. OpenAI lists web and file search, image-generation tooling, a code interpreter, hosted shell and patch operations, skills, computer use, MCP connections and tool search for the base model. It also supports function calling, Structured Outputs, streaming and prompt caching. Applications control their available operations and return actual tool results; model-generated action text alone is not evidence that an operation has completed.\n\nThe producer’s GPT-6 Sol specification gives a 1,050,000-token context window, a maximum input of 922,000 tokens and a maximum output of 128,000 tokens. Its documented knowledge cutoff is April 20, 2026. The base reasoning-effort choices are none, low, medium, high, xhigh and max, with medium as the default. The context must accommodate the task, relevant tool results and reasoning, as well as the visible answer. Keep definitions and completion criteria near the material they govern, especially in a long engineering request.\n\nOpenAI documents Pro in the Responses API for difficult work that can accommodate additional latency and model activity. For GPT-6 Sol, built-in tools and reasoning with function calls use Responses. In the producer’s Chat Completions interface, function calling is supported only when reasoning effort is none. The GPT-6 guide also describes asynchronous tool work, steering an active turn and retaining reasoning between calls. These are workflow capabilities with their own interface requirements.\n\nSpecify a concrete final artifact and how it should be checked. You might request a proposed patch, the behavior it changes and the evidence needed to verify it, or a comparison of implementation options tied to the supplied constraints. If an image supplies interface context, state which visual detail matters. The final response should connect the significant choices to the engineering goal and distinguish a proposed action from a result already obtained.", "editorialOverlay": { "copy_fields": { "practical_html": "Technical example in English
Review a cache invalidation contract\n\nReview this design: reads cache user profiles for one hour; updates write the database but never evict the cache; the API promises that a successful update is visible to the next read. Identify the contract violation and compare evict-after-commit with versioned cache keys. Recommend one approach and specify the failure-path tests needed.A focused design review with a comparison, recommendation and tests for update and cache failure paths.The recommendation addresses the next-read promise and explains the remaining race or failure condition.openai/gpt-6-sol-proGPT-6 Sol with Pro reasoning for complex coding and agentic work. Supply the code, requirements and relevant evidence to request an implementation proposal, a focused review or an explanation of a multi-step solution.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
GPT-6 Sol Pro denotes OpenAI’s GPT-6 Sol with Pro reasoning. The base Sol model is built for complex coding and agentic workflows, accepts text and images and produces text. Pro adds model work to develop the final answer. It is separate from reasoning effort, which controls how much analysis the model applies within the selected execution mode. GPT-6 Sol remains the base edition for this card, distinct from GPT-6.1 Sol.
For a development task, describe the behavior to implement and provide the relevant source files, examples of the input and the constraints the change must respect. An illustrative request is to trace how an input reaches a result, identify the dependencies involved and propose a change that preserves the required behavior. For a review, include the intended behavior and the code to inspect, then ask for concrete issues with an explanation of how each issue affects that behavior.
Agentic work connects several decisions through evidence from the working environment. A tool may retrieve a file, run a calculation or return the result of a check; that result becomes material for the next decision. OpenAI lists web and file search, image-generation tooling, a code interpreter, hosted shell and patch operations, skills, computer use, MCP connections and tool search for the base model. It also supports function calling, Structured Outputs, streaming and prompt caching. Applications control their available operations and return actual tool results; model-generated action text alone is not evidence that an operation has completed.
The producer’s GPT-6 Sol specification gives a 1,050,000-token context window, a maximum input of 922,000 tokens and a maximum output of 128,000 tokens. Its documented knowledge cutoff is April 20, 2026. The base reasoning-effort choices are none, low, medium, high, xhigh and max, with medium as the default. The context must accommodate the task, relevant tool results and reasoning, as well as the visible answer. Keep definitions and completion criteria near the material they govern, especially in a long engineering request.
OpenAI documents Pro in the Responses API for difficult work that can accommodate additional latency and model activity. For GPT-6 Sol, built-in tools and reasoning with function calls use Responses. In the producer’s Chat Completions interface, function calling is supported only when reasoning effort is none. The GPT-6 guide also describes asynchronous tool work, steering an active turn and retaining reasoning between calls. These are workflow capabilities with their own interface requirements.
Specify a concrete final artifact and how it should be checked. You might request a proposed patch, the behavior it changes and the evidence needed to verify it, or a comparison of implementation options tied to the supplied constraints. If an image supplies interface context, state which visual detail matters. The final response should connect the significant choices to the engineering goal and distinguish a proposed action from a result already obtained.
POST /v1/chat/completionstexttext| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits openai/gpt-6-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 $0.0004 / 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 $0.0004 / 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 $0.0008 / 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-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-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-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-sol-pro
Use the method paths shown above.
openai/gpt-6-sol-proGPT-6 Sol with Pro reasoning for complex coding and agentic work. Supply the code, requirements and relevant evidence to request an implementation proposal, a focused review or an explanation of a multi-step solution.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
GPT-6 Sol Pro denotes OpenAI’s GPT-6 Sol with Pro reasoning. The base Sol model is built for complex coding and agentic workflows, accepts text and images and produces text. Pro adds model work to develop the final answer. It is separate from reasoning effort, which controls how much analysis the model applies within the selected execution mode. GPT-6 Sol remains the base edition for this card, distinct from GPT-6.1 Sol.
For a development task, describe the behavior to implement and provide the relevant source files, examples of the input and the constraints the change must respect. An illustrative request is to trace how an input reaches a result, identify the dependencies involved and propose a change that preserves the required behavior. For a review, include the intended behavior and the code to inspect, then ask for concrete issues with an explanation of how each issue affects that behavior.
Agentic work connects several decisions through evidence from the working environment. A tool may retrieve a file, run a calculation or return the result of a check; that result becomes material for the next decision. OpenAI lists web and file search, image-generation tooling, a code interpreter, hosted shell and patch operations, skills, computer use, MCP connections and tool search for the base model. It also supports function calling, Structured Outputs, streaming and prompt caching. Applications control their available operations and return actual tool results; model-generated action text alone is not evidence that an operation has completed.
The producer’s GPT-6 Sol specification gives a 1,050,000-token context window, a maximum input of 922,000 tokens and a maximum output of 128,000 tokens. Its documented knowledge cutoff is April 20, 2026. The base reasoning-effort choices are none, low, medium, high, xhigh and max, with medium as the default. The context must accommodate the task, relevant tool results and reasoning, as well as the visible answer. Keep definitions and completion criteria near the material they govern, especially in a long engineering request.
OpenAI documents Pro in the Responses API for difficult work that can accommodate additional latency and model activity. For GPT-6 Sol, built-in tools and reasoning with function calls use Responses. In the producer’s Chat Completions interface, function calling is supported only when reasoning effort is none. The GPT-6 guide also describes asynchronous tool work, steering an active turn and retaining reasoning between calls. These are workflow capabilities with their own interface requirements.
Specify a concrete final artifact and how it should be checked. You might request a proposed patch, the behavior it changes and the evidence needed to verify it, or a comparison of implementation options tied to the supplied constraints. If an image supplies interface context, state which visual detail matters. The final response should connect the significant choices to the engineering goal and distinguish a proposed action from a result already obtained.
POST /v1/messagestexttext| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits openai/gpt-6-sol-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 $0.004 / 1K tokens Output · Output tokens $0.02 / 1K tokens Input · Cache read $0.0004 / 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 $0.0004 / 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 $0.0008 / 1K tokens Input · Cache write $0.01 / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
{\n "model": "openai/gpt-6-sol-pro",\n "max_tokens": 4096,\n "messages": [\n {\n "role": "user",\n "content": "Summarize the incident report."\n }\n ]\n}set -eu\n: "${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-sol-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-sol-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-sol-pro
Use the method paths shown above.
openai/gpt-6-sol-proGPT-6 Sol with Pro reasoning for complex coding and agentic work. Supply the code, requirements and relevant evidence to request an implementation proposal, a focused review or an explanation of a multi-step solution.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
GPT-6 Sol Pro denotes OpenAI’s GPT-6 Sol with Pro reasoning. The base Sol model is built for complex coding and agentic workflows, accepts text and images and produces text. Pro adds model work to develop the final answer. It is separate from reasoning effort, which controls how much analysis the model applies within the selected execution mode. GPT-6 Sol remains the base edition for this card, distinct from GPT-6.1 Sol.
For a development task, describe the behavior to implement and provide the relevant source files, examples of the input and the constraints the change must respect. An illustrative request is to trace how an input reaches a result, identify the dependencies involved and propose a change that preserves the required behavior. For a review, include the intended behavior and the code to inspect, then ask for concrete issues with an explanation of how each issue affects that behavior.
Agentic work connects several decisions through evidence from the working environment. A tool may retrieve a file, run a calculation or return the result of a check; that result becomes material for the next decision. OpenAI lists web and file search, image-generation tooling, a code interpreter, hosted shell and patch operations, skills, computer use, MCP connections and tool search for the base model. It also supports function calling, Structured Outputs, streaming and prompt caching. Applications control their available operations and return actual tool results; model-generated action text alone is not evidence that an operation has completed.
The producer’s GPT-6 Sol specification gives a 1,050,000-token context window, a maximum input of 922,000 tokens and a maximum output of 128,000 tokens. Its documented knowledge cutoff is April 20, 2026. The base reasoning-effort choices are none, low, medium, high, xhigh and max, with medium as the default. The context must accommodate the task, relevant tool results and reasoning, as well as the visible answer. Keep definitions and completion criteria near the material they govern, especially in a long engineering request.
OpenAI documents Pro in the Responses API for difficult work that can accommodate additional latency and model activity. For GPT-6 Sol, built-in tools and reasoning with function calls use Responses. In the producer’s Chat Completions interface, function calling is supported only when reasoning effort is none. The GPT-6 guide also describes asynchronous tool work, steering an active turn and retaining reasoning between calls. These are workflow capabilities with their own interface requirements.
Specify a concrete final artifact and how it should be checked. You might request a proposed patch, the behavior it changes and the evidence needed to verify it, or a comparison of implementation options tied to the supplied constraints. If an image supplies interface context, state which visual detail matters. The final response should connect the significant choices to the engineering goal and distinguish a proposed action from a result already obtained.
POST /v1/responsestexttext| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits openai/gpt-6-sol-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 $0.004 / 1K tokens Output · Output tokens $0.02 / 1K tokens Input · Cache read $0.0004 / 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 $0.0004 / 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 $0.0008 / 1K tokens Input · Cache write $0.01 / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
{\n "model": "openai/gpt-6-sol-pro",\n "input": [\n {\n "content": "Hello, how are you?",\n "role": "user",\n "type": "message"\n }\n ]\n}set -eu\n: "${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-sol-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-sol-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-sol-pro
Use the method paths shown above.
Technical example in English
Review a cache invalidation contract\n\nReview this design: reads cache user profiles for one hour; updates write the database but never evict the cache; the API promises that a successful update is visible to the next read. Identify the contract violation and compare evict-after-commit with versioned cache keys. Recommend one approach and specify the failure-path tests needed.A focused design review with a comparison, recommendation and tests for update and cache failure paths.The recommendation addresses the next-read promise and explains the remaining race or failure condition.