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": null, "copy_sha256": null, "copy_source": null, "html_sha256": "442563e334da12272c017b320b8001ff31a50224d4b02ecf0035ef713ff8c5e5", "module_sha256": "ee9dc1c50b1e6a8fb7fa062030fcb4646b9aaaeb08be513b723a5a92a7290f06", "raw_model_unchanged": true, "schema": "ailoot-native-model-editorial-v1" }, "locale": "en", "methods": [ { "html": "openai/gpt-6-sol-pro:batchGPT-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/batchestexttext| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldendpoint | Typestring | RequirementRequired | Values & limits /v1/chat/completions | Default— |
Fieldmodel | Typestring | RequirementRequired | Values & limits openai/gpt-6-sol-pro:batch | Default— |
Fieldrequests | Typebatch_item[] | RequirementRequired | Values & limits Minimum items: 1 | Default— |
Fieldrequests[].custom_id | Typestring | RequirementRequired | Values & limits | Default— |
Fieldrequests[].body.messages | Typemessage[] | RequirementRequired | Values & limits | Default— |
Fieldcompletion_window | Typestring | RequirementOptional | Values & limits 24h | Default— |
Fieldprovider | Typeobject|null | RequirementOptional | Values & limits | Default— |
Fieldrequests[].body.model | Typestring | RequirementOptional | Values & limits openai/gpt-6-sol-pro:batch, openai/gpt-6-sol-pro | Default— |
Fieldprovider.only | Typestring[] | RequirementOptional | Values & limits openai | Default— |
Fieldprovider.allow_fallbacks | Typeboolean|null | RequirementOptional | Values & limits false, null | Default— |
| Parameter | Contract |
|---|---|
Parametercontent_type | Contractapplication/json |
Parameterrequired | Contractendpoint, model, requests |
Parametermaterial_types | Contracttext |
Parameterrepresentation | Contractinline requests[]; each item has custom_id and body |
Parameterrequest_endpoint_profile | Contract/v1/chat/completions |
Parameterfile_material | ContractCaller may read actual UTF-8 JSONL locally into requests[]. No upload purpose, input_file_id or file reference is used. Missing/empty customer material stops preparation before POST. |
Parameterserialization_order | Contractendpoint, model, provider (if supplied), completion_window (if supplied), requests last |
Parameteroptional_controls | Contractprovider.only, provider.allow_fallbacks, completion_window |
Parameterschema_scope | ContractText Chat Completions bodies in the documented inline Batch envelope. |
Lifecycle: asynchronous
GET /v1/batches/{operation_id} · actual original create response.id; same user/key/group
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 $0.002 / 1K tokens Output · Output tokens $0.01 / 1K tokens Input · Cache read $0.0002 / 1K tokens Input · Cache write $0.0025 / 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 $0.002 / 1K tokens Output · Output tokens $0.01 / 1K tokens Input · Cache read $0.0002 / 1K tokens Input · Cache write $0.0025 / 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 $0.004 / 1K tokens Output · Output tokens $0.015 / 1K tokens Input · Cache read $0.0004 / 1K tokens Input · Cache write $0.005 / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\npython3 - <<'PY_REQUEST'\nimport json\nfrom pathlib import Path\nbody=json.loads('{"endpoint":"/v1/chat/completions","model":"openai/gpt-6-sol-pro:batch","requests":[{"custom_id":"req-0001","body":{"messages":[{"role":"user","content":"Hello!"}],"max_tokens":16}}]}')\nPath("batch-request.json").write_text(json.dumps(body,ensure_ascii=False),encoding="utf-8")\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/batches \\\n -H "Authorization: Bearer $AILOOT_API_KEY" -H 'Content-Type: application/json' \\\n --data-binary @batch-request.json -D batch-response-headers.txt -o batch-create-response.json\nimport json, os\nfrom pathlib import Path\nfrom urllib.request import Request, urlopen\n\ndef prepare_requests(customer_path):\n path = Path(customer_path)\n if not path.is_file(): raise ValueError("Customer JSONL file is missing")\n items = [json.loads(line) for line in path.read_text(encoding="utf-8").splitlines() if line.strip()]\n if not items: raise ValueError("Customer JSONL contains no requests")\n ids = set()\n for item in items:\n if set(item) != {"custom_id", "body"}: raise ValueError("Each item needs custom_id and body")\n cid = item["custom_id"]\n if not isinstance(cid, str) or not cid.strip() or cid in ids: raise ValueError("custom_id must be nonempty and unique")\n ids.add(cid)\n if not isinstance(item["body"], dict) or not item["body"].get("messages"): raise ValueError("Supply actual text messages")\n supplied_model = item["body"].get("model")\n if supplied_model is not None and supplied_model not in ['openai/gpt-6-sol-pro:batch', 'openai/gpt-6-sol-pro']: raise ValueError("body.model must be an exact documented caller or wire ID")\n return {"endpoint":"/v1/chat/completions", "model":"openai/gpt-6-sol-pro:batch", "requests":items}\n\nif __name__ == "__main__":\n material = os.environ.get("AILOOT_BATCH_REQUESTS_JSONL", "")\n if not material: raise SystemExit("Set AILOOT_BATCH_REQUESTS_JSONL to your actual customer file")\n body = prepare_requests(material)\n key = os.environ.get("AILOOT_API_KEY", "")\n if not key: raise SystemExit("Set AILOOT_API_KEY")\n request = Request("https://api.ailoot.ai/v1/batches", data=json.dumps(body, ensure_ascii=False).encode("utf-8"), headers={"Authorization":"Bearer " + key, "Content-Type":"application/json"}, method="POST")\n with urlopen(request) as response:\n raw = response.read()\n Path("batch-create-response.json").write_bytes(raw)\n print(raw.decode("utf-8"))\nimport json\nfrom pathlib import Path\nraw = Path("batch-status-response.json").read_bytes()\nresponse = json.loads(raw)\nif response.get("status") != "completed":\n raise SystemExit("Keep the original operation; read its status without resubmitting")\nresults = response.get("results")\nif not isinstance(results, list) or not results:\n raise SystemExit("Preserve the original response; completed results are missing")\nPath("batch-results.jsonl").write_text("".join(json.dumps(item, ensure_ascii=False) + "\\n" for item in results), encoding="utf-8")\n# Preserve batch-status-response.json with aggregate usage and every result/error.\ninline results[].response.body JSON
GET /v1/batches/{operation_id}set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\nBATCH_OPERATION_ID="$(python3 - <<'PY_ID'\nimport json\nfrom pathlib import Path\nfrom urllib.parse import quote\nresponse = json.loads(Path("batch-create-response.json").read_text(encoding="utf-8"))\noperation = response.get("id")\nif not isinstance(operation, str) or not operation: raise SystemExit("No actual original operation ID; preserve the response and do not resubmit")\nprint(quote(operation, safe=""))\nPY_ID\n)"\ncurl --fail-with-body --silent --show-error "https://api.ailoot.ai/v1/batches/$BATCH_OPERATION_ID" \\\n -H "Authorization: Bearer $AILOOT_API_KEY" -o batch-status-response.json\nKeep every complete returned response, file and content part.
Preserve the full original status JSON, every result/custom_id and complete response or error body. Save completed results[] as JSONL without filtering.
Preserve the original operation ID. 503 result_pending_check has X-Should-Retry:false; never resubmit an unknown original batch.
| Field | Type |
|---|---|
Fieldresults[].custom_id | Typestring |
Fieldresults[].response.body.choices[].message.content | Typestring|null |
Fieldresults[].error | Typeobject|null |
Fieldusage | Typeobject|null |
Fieldid | Typestring |
Fieldmodel | Typestring |
Fieldendpoint | Typestring |
Fieldstatus | Typestring |
Fieldrequest_counts | Typeobject |
Fieldresults[].id | Typestring |
Fieldresults[].response.status_code | Typeinteger |
Fieldresults[].response.request_id | Typestring |
| Response | Condition | Next step |
|---|---|---|
Response400 · invalid_request_error | ConditionInvalid documented inline envelope, duplicate keys, item identity, or configured model capabilities conflict. | Next stepCorrect the indicated fields before any new submission. |
Response401 · authentication_error | ConditionInvalid API key. | Next stepUse the authorized Ailoot key. |
Response403 · permission_error | ConditionBatch group unavailable. | Next stepCheck key/group entitlement. |
Response422 · invalid_request_error | ConditionBatch uses balance billing; subscription creation is rejected. | Next stepUse the authorized balance-billed group. |
Response422 · price_unavailable | ConditionBatch input cannot be quoted. | Next stepPreserve response; use documented family input. |
Response402 · billing_error | ConditionBatch reserve unavailable. | Next stepResolve admission before a new request. |
Response503 · price_unavailable | ConditionBatch method price/native rates unavailable. | Next stepPreserve response; do not substitute prices. |
Response503 · api_error | ConditionExact supplier route unavailable. | Next stepPreserve response for route repair. |
Response503 · result_pending_check | ConditionOriginal result requires checking; X-Should-Retry:false. | Next stepKeep original operation_id/id and read its status; never repeat original POST. |
Response404 · not_found | ConditionBatch status operation absent or owned by another identity. | Next stepUse exact original id and same user/key/group. |
Ailoot Ultra openai/gpt-6-sol-pro:batch
Use the method paths shown above.