GPT-5.2 is OpenAI’s reasoning model for complex professional work. In the producer’s GPT-5.2 naming, the API model corresponds to the Thinking edition, distinct from Chat/Instant and Pro. It accepts text and images and produces text. The release describes coding, long-document analysis, visual interpretation and tasks that connect several sources or tool results.
For a professional document, supply the material, the intended reader and the question the result should answer. An illustrative task is combining several project reports into a brief that distinguishes facts, dependencies and a recommendation. Spreadsheet or presentation work also needs the source data and the requirements of the artifact. The application’s tools create or edit the actual files, while the model’s response organizes or explains the work.
For coding, provide the intended behavior, relevant source files and the constraints a change must preserve. The release describes debugging, feature work and refactoring as practical applications. Visual inputs can add a dashboard, interface screenshot, technical diagram or report. Identify the detail that should inform the answer and provide the written context needed to interpret it. The model’s direct output remains text rather than a generated image.
OpenAI specifies a 400,000-token context window, a maximum output of 128,000 tokens and a knowledge cutoff of August 31, 2025. The reasoning-effort choices are none, low, medium, high and xhigh; none is the default. The release also documents compatibility with Responses compaction for a task that continues as its context grows. Compaction retains relevant state for later work; it does not turn a multi-step task into a single unlimited request.
The producer lists Responses, Chat Completions and Batch interfaces, streaming, Structured Outputs, function calling, file and image inputs, web and file search and prompt caching. The model’s tools include image generation, code interpretation, hosted shell and patch operations, skills and MCP connections. The application manages its operations and returns actual results so the model can use them in the next decision.
Prepare a clear goal, constraints and an output contract. For a document decision, name the criteria and request conclusions tied to the supplied evidence. For a proposed change, identify the behavior to verify and ask for the checks that have actually run to be distinguished from those still proposed. A structured set of findings, a code proposal or a document that follows the supplied requirements is a useful final result when its basis and remaining questions are explicit.
", "aboutText": "GPT-5.2 is OpenAI’s reasoning model for complex professional work. In the producer’s GPT-5.2 naming, the API model corresponds to the Thinking edition, distinct from Chat/Instant and Pro. It accepts text and images and produces text. The release describes coding, long-document analysis, visual interpretation and tasks that connect several sources or tool results.\n\nFor a professional document, supply the material, the intended reader and the question the result should answer. An illustrative task is combining several project reports into a brief that distinguishes facts, dependencies and a recommendation. Spreadsheet or presentation work also needs the source data and the requirements of the artifact. The application’s tools create or edit the actual files, while the model’s response organizes or explains the work.\n\nFor coding, provide the intended behavior, relevant source files and the constraints a change must preserve. The release describes debugging, feature work and refactoring as practical applications. Visual inputs can add a dashboard, interface screenshot, technical diagram or report. Identify the detail that should inform the answer and provide the written context needed to interpret it. The model’s direct output remains text rather than a generated image.\n\nOpenAI specifies a 400,000-token context window, a maximum output of 128,000 tokens and a knowledge cutoff of August 31, 2025. The reasoning-effort choices are none, low, medium, high and xhigh; none is the default. The release also documents compatibility with Responses compaction for a task that continues as its context grows. Compaction retains relevant state for later work; it does not turn a multi-step task into a single unlimited request.\n\nThe producer lists Responses, Chat Completions and Batch interfaces, streaming, Structured Outputs, function calling, file and image inputs, web and file search and prompt caching. The model’s tools include image generation, code interpretation, hosted shell and patch operations, skills and MCP connections. The application manages its operations and returns actual results so the model can use them in the next decision.\n\nPrepare a clear goal, constraints and an output contract. For a document decision, name the criteria and request conclusions tied to the supplied evidence. For a proposed change, identify the behavior to verify and ask for the checks that have actually run to be distinguished from those still proposed. A structured set of findings, a code proposal or a document that follows the supplied requirements is a useful final result when its basis and remaining questions are explicit.", "editorialOverlay": { "copy_fields": null, "copy_sha256": null, "copy_source": null, "html_sha256": "74750dec2d291dcf6ba18f884c9f686f7953aabe4e27461dc38124dc5f1bcc5d", "module_sha256": "ee9dc1c50b1e6a8fb7fa062030fcb4646b9aaaeb08be513b723a5a92a7290f06", "raw_model_unchanged": true, "schema": "ailoot-native-model-editorial-v1" }, "locale": "en", "methods": [ { "html": "openai/gpt-5.2:batchGPT-5.2 is a reasoning model for professional work, coding and multi-step tasks. It accepts text and images and can develop documents, analyze related material or work with application tools under a clear output brief.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
GPT-5.2 is OpenAI’s reasoning model for complex professional work. In the producer’s GPT-5.2 naming, the API model corresponds to the Thinking edition, distinct from Chat/Instant and Pro. It accepts text and images and produces text. The release describes coding, long-document analysis, visual interpretation and tasks that connect several sources or tool results.
For a professional document, supply the material, the intended reader and the question the result should answer. An illustrative task is combining several project reports into a brief that distinguishes facts, dependencies and a recommendation. Spreadsheet or presentation work also needs the source data and the requirements of the artifact. The application’s tools create or edit the actual files, while the model’s response organizes or explains the work.
For coding, provide the intended behavior, relevant source files and the constraints a change must preserve. The release describes debugging, feature work and refactoring as practical applications. Visual inputs can add a dashboard, interface screenshot, technical diagram or report. Identify the detail that should inform the answer and provide the written context needed to interpret it. The model’s direct output remains text rather than a generated image.
OpenAI specifies a 400,000-token context window, a maximum output of 128,000 tokens and a knowledge cutoff of August 31, 2025. The reasoning-effort choices are none, low, medium, high and xhigh; none is the default. The release also documents compatibility with Responses compaction for a task that continues as its context grows. Compaction retains relevant state for later work; it does not turn a multi-step task into a single unlimited request.
The producer lists Responses, Chat Completions and Batch interfaces, streaming, Structured Outputs, function calling, file and image inputs, web and file search and prompt caching. The model’s tools include image generation, code interpretation, hosted shell and patch operations, skills and MCP connections. The application manages its operations and returns actual results so the model can use them in the next decision.
Prepare a clear goal, constraints and an output contract. For a document decision, name the criteria and request conclusions tied to the supplied evidence. For a proposed change, identify the behavior to verify and ask for the checks that have actually run to be distinguished from those still proposed. A structured set of findings, a code proposal or a document that follows the supplied requirements is a useful final result when its basis and remaining questions are explicit.
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-5.2: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-5.2:batch, openai/gpt-5.2 | 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 $0.00175 / 1K tokens Output · Output tokens $0.014 / 1K tokens Input · Cache read $0.000175 / 1K tokens Input · Cache write — / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
| ConfigurationRequest-length tier 1 | Rate schedule— | Context tokens, more than0 | Context tokens, up to and includingNo upper bound | Charge Input · Input tokens $0.00175 / 1K tokens Output · Output tokens $0.014 / 1K tokens Input · Cache read $0.000175 / 1K tokens Input · Cache write — / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
set -eu\n: "${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-5.2: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-5.2:batch', 'openai/gpt-5.2']: raise ValueError("body.model must be an exact documented caller or wire ID")\n return {"endpoint":"/v1/chat/completions", "model":"openai/gpt-5.2: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-5.2:batch
Use the method paths shown above.