GPT-5 Mini is OpenAI’s Mini edition of the GPT-5 reasoning family. The maker describes it as a choice for well-defined tasks with precise prompts and high-volume workloads. It accepts text and images and produces text. The Mini edition preserves its own model identity and specifications rather than using the limits of GPT-5 Pro or a later Mini model.
A practical application is a focused document or support operation. Supply the original material, the rules to apply and the expected result. An illustrative request is extracting a few required fields from a message and drafting a short response that asks for missing information. Define how to represent an absent value, which facts must remain unchanged and the tone appropriate to the intended reader.
For a constrained edit, specify the passage to change and the purpose of the change. For a coding step, provide the relevant source and the behavior that a proposed edit should affect. A screenshot or diagram can supply visual context, with written instructions identifying the detail that matters. A useful brief gives the model one operation and its completion criterion instead of making it infer the entire workflow from the input.
OpenAI specifies a 400,000-token context window, a maximum input of 272,000 tokens and a maximum output of 128,000 tokens for GPT-5 Mini, with a knowledge cutoff of May 31, 2024. The GPT-5 developer release includes Mini among the models supporting reasoning-effort and verbosity controls and custom tools. The family’s documented effort choices are minimal, low, medium and high, while verbosity controls the amount of detail in the answer separately from reasoning.
The exact Mini card lists Responses, Chat Completions and Batch interfaces, streaming, Structured Outputs, function calling, file inputs, image input and web/file search. Its listed tool set includes functions, web and file search, code interpretation and MCP connections. The application exposes its operations, executes its actions and returns actual results. A structured response describes the required output fields; a function request proposes an operation for a tool to perform.
Use a final format that fits the bounded task: extracted values, a revised passage, a concise reply or a proposed code change. For a multi-stage process, carry the relevant result into the next request and retain the rules that govern it. Check the important statements against the original material and make an unresolved condition explicit. The resulting artifact can then support a specific next action without turning every focused operation into a broad conversation.
", "aboutText": "GPT-5 Mini is OpenAI’s Mini edition of the GPT-5 reasoning family. The maker describes it as a choice for well-defined tasks with precise prompts and high-volume workloads. It accepts text and images and produces text. The Mini edition preserves its own model identity and specifications rather than using the limits of GPT-5 Pro or a later Mini model.\n\nA practical application is a focused document or support operation. Supply the original material, the rules to apply and the expected result. An illustrative request is extracting a few required fields from a message and drafting a short response that asks for missing information. Define how to represent an absent value, which facts must remain unchanged and the tone appropriate to the intended reader.\n\nFor a constrained edit, specify the passage to change and the purpose of the change. For a coding step, provide the relevant source and the behavior that a proposed edit should affect. A screenshot or diagram can supply visual context, with written instructions identifying the detail that matters. A useful brief gives the model one operation and its completion criterion instead of making it infer the entire workflow from the input.\n\nOpenAI specifies a 400,000-token context window, a maximum input of 272,000 tokens and a maximum output of 128,000 tokens for GPT-5 Mini, with a knowledge cutoff of May 31, 2024. The GPT-5 developer release includes Mini among the models supporting reasoning-effort and verbosity controls and custom tools. The family’s documented effort choices are minimal, low, medium and high, while verbosity controls the amount of detail in the answer separately from reasoning.\n\nThe exact Mini card lists Responses, Chat Completions and Batch interfaces, streaming, Structured Outputs, function calling, file inputs, image input and web/file search. Its listed tool set includes functions, web and file search, code interpretation and MCP connections. The application exposes its operations, executes its actions and returns actual results. A structured response describes the required output fields; a function request proposes an operation for a tool to perform.\n\nUse a final format that fits the bounded task: extracted values, a revised passage, a concise reply or a proposed code change. For a multi-stage process, carry the relevant result into the next request and retain the rules that govern it. Check the important statements against the original material and make an unresolved condition explicit. The resulting artifact can then support a specific next action without turning every focused operation into a broad conversation.", "editorialOverlay": { "copy_fields": null, "copy_sha256": null, "copy_source": null, "html_sha256": "549f991010bc6ba2783e59b88ecc21eb83acea3854448642eed67573689ee7f9", "module_sha256": "ee9dc1c50b1e6a8fb7fa062030fcb4646b9aaaeb08be513b723a5a92a7290f06", "raw_model_unchanged": true, "schema": "ailoot-native-model-editorial-v1" }, "locale": "en", "methods": [ { "html": "openai/gpt-5-mini:batchThe Mini edition of GPT-5 is designed for well-defined tasks and precise prompts. It accepts text and images and returns text, making it useful for focused extraction, constrained edits, support responses and narrower steps in an agent workflow.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
GPT-5 Mini is OpenAI’s Mini edition of the GPT-5 reasoning family. The maker describes it as a choice for well-defined tasks with precise prompts and high-volume workloads. It accepts text and images and produces text. The Mini edition preserves its own model identity and specifications rather than using the limits of GPT-5 Pro or a later Mini model.
A practical application is a focused document or support operation. Supply the original material, the rules to apply and the expected result. An illustrative request is extracting a few required fields from a message and drafting a short response that asks for missing information. Define how to represent an absent value, which facts must remain unchanged and the tone appropriate to the intended reader.
For a constrained edit, specify the passage to change and the purpose of the change. For a coding step, provide the relevant source and the behavior that a proposed edit should affect. A screenshot or diagram can supply visual context, with written instructions identifying the detail that matters. A useful brief gives the model one operation and its completion criterion instead of making it infer the entire workflow from the input.
OpenAI specifies a 400,000-token context window, a maximum input of 272,000 tokens and a maximum output of 128,000 tokens for GPT-5 Mini, with a knowledge cutoff of May 31, 2024. The GPT-5 developer release includes Mini among the models supporting reasoning-effort and verbosity controls and custom tools. The family’s documented effort choices are minimal, low, medium and high, while verbosity controls the amount of detail in the answer separately from reasoning.
The exact Mini card lists Responses, Chat Completions and Batch interfaces, streaming, Structured Outputs, function calling, file inputs, image input and web/file search. Its listed tool set includes functions, web and file search, code interpretation and MCP connections. The application exposes its operations, executes its actions and returns actual results. A structured response describes the required output fields; a function request proposes an operation for a tool to perform.
Use a final format that fits the bounded task: extracted values, a revised passage, a concise reply or a proposed code change. For a multi-stage process, carry the relevant result into the next request and retain the rules that govern it. Check the important statements against the original material and make an unresolved condition explicit. The resulting artifact can then support a specific next action without turning every focused operation into a broad conversation.
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-mini: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-mini:batch, openai/gpt-5-mini | 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 $0.00025 / 1K tokens Output · Output tokens $0.002 / 1K tokens Input · Cache read $0.000025 / 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 $0.00025 / 1K tokens Output · Output tokens $0.002 / 1K tokens Input · Cache read $0.000025 / 1K tokens Input · Cache write — / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
set -eu\n: "${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-mini: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-mini:batch', 'openai/gpt-5-mini']: raise ValueError("body.model must be an exact documented caller or wire ID")\n return {"endpoint":"/v1/chat/completions", "model":"openai/gpt-5-mini: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-mini:batch
Use the method paths shown above.