GPT-5.4 Mini is OpenAI’s compact GPT-5.4 edition for coding, computer use and supporting agent tasks. It brings the family’s capabilities to a model designed for focused, high-volume work. Its inputs are text and images and its output is text. The maker describes tasks such as targeted edits, codebase navigation, debugging and interpreting interface screenshots.
For a coding request, describe the behavior to implement, provide the relevant files or excerpts and state the constraints that must survive the change. An illustrative task is finding the part of a project that handles a particular input, proposing a targeted edit and identifying how the changed behavior should be checked. Keep the task’s scope explicit so that a local change is connected to the relevant dependencies without becoming an unrelated rewrite.
The manufacturer’s release also describes Mini as a supporting model in a larger agent system. A narrower assignment might be searching a codebase, reviewing a large file or processing supporting documents while another stage handles coordination and final judgment. Give that assignment its own input, completion criterion and output format. A useful result is the finding, its location in the supplied material and any uncertainty the next stage needs to resolve.
For computer-use work, the release emphasizes interpreting screenshots of dense interfaces. Supply the current visual state, the user’s goal and the relevant constraints. The model can propose an action or explain what the screenshot shows, while the connected environment carries out the operation and returns the resulting state. Keep the actual tool result separate from an action that has only been proposed.
OpenAI’s specification gives a 400,000-token context window, a maximum input of 272,000 tokens and a maximum output of 128,000 tokens, with a knowledge cutoff of August 31, 2025. The reasoning-effort choices are none, low, medium, high and xhigh; none is the default. The model lists Responses, Chat Completions and Batch interfaces, streaming, Structured Outputs, function calling, web and file search, file and image input and prompt caching.
Its producer-listed tools include tool search, image generation, code interpretation, hosted shell and patch operations, skills, computer use and MCP connections. The application provides the available operations and returns their actual results. For a final response, request the change or finding, the relevant evidence and the next action. A proposed patch, a focused review or a concise interface explanation is a useful artifact when it is tied to the exact task and can be checked against the supplied source or screen.
", "aboutText": "GPT-5.4 Mini is OpenAI’s compact GPT-5.4 edition for coding, computer use and supporting agent tasks. It brings the family’s capabilities to a model designed for focused, high-volume work. Its inputs are text and images and its output is text. The maker describes tasks such as targeted edits, codebase navigation, debugging and interpreting interface screenshots.\n\nFor a coding request, describe the behavior to implement, provide the relevant files or excerpts and state the constraints that must survive the change. An illustrative task is finding the part of a project that handles a particular input, proposing a targeted edit and identifying how the changed behavior should be checked. Keep the task’s scope explicit so that a local change is connected to the relevant dependencies without becoming an unrelated rewrite.\n\nThe manufacturer’s release also describes Mini as a supporting model in a larger agent system. A narrower assignment might be searching a codebase, reviewing a large file or processing supporting documents while another stage handles coordination and final judgment. Give that assignment its own input, completion criterion and output format. A useful result is the finding, its location in the supplied material and any uncertainty the next stage needs to resolve.\n\nFor computer-use work, the release emphasizes interpreting screenshots of dense interfaces. Supply the current visual state, the user’s goal and the relevant constraints. The model can propose an action or explain what the screenshot shows, while the connected environment carries out the operation and returns the resulting state. Keep the actual tool result separate from an action that has only been proposed.\n\nOpenAI’s specification gives a 400,000-token context window, a maximum input of 272,000 tokens and a maximum output of 128,000 tokens, with a knowledge cutoff of August 31, 2025. The reasoning-effort choices are none, low, medium, high and xhigh; none is the default. The model lists Responses, Chat Completions and Batch interfaces, streaming, Structured Outputs, function calling, web and file search, file and image input and prompt caching.\n\nIts producer-listed tools include tool search, image generation, code interpretation, hosted shell and patch operations, skills, computer use and MCP connections. The application provides the available operations and returns their actual results. For a final response, request the change or finding, the relevant evidence and the next action. A proposed patch, a focused review or a concise interface explanation is a useful artifact when it is tied to the exact task and can be checked against the supplied source or screen.", "editorialOverlay": { "copy_fields": null, "copy_sha256": null, "copy_source": null, "html_sha256": "2ed2b2e8f6f6feb93d1df3c98503790568894947cd36ed2b79fab0eb82b24511", "module_sha256": "ee9dc1c50b1e6a8fb7fa062030fcb4646b9aaaeb08be513b723a5a92a7290f06", "raw_model_unchanged": true, "schema": "ailoot-native-model-editorial-v1" }, "locale": "en", "methods": [ { "html": "openai/gpt-5.4-mini:batchA compact GPT-5.4 edition for coding, computer use and supporting agent work at high volume. It accepts text and images and can develop an edit, interpret an interface screenshot or complete a narrower task within a larger workflow.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
GPT-5.4 Mini is OpenAI’s compact GPT-5.4 edition for coding, computer use and supporting agent tasks. It brings the family’s capabilities to a model designed for focused, high-volume work. Its inputs are text and images and its output is text. The maker describes tasks such as targeted edits, codebase navigation, debugging and interpreting interface screenshots.
For a coding request, describe the behavior to implement, provide the relevant files or excerpts and state the constraints that must survive the change. An illustrative task is finding the part of a project that handles a particular input, proposing a targeted edit and identifying how the changed behavior should be checked. Keep the task’s scope explicit so that a local change is connected to the relevant dependencies without becoming an unrelated rewrite.
The manufacturer’s release also describes Mini as a supporting model in a larger agent system. A narrower assignment might be searching a codebase, reviewing a large file or processing supporting documents while another stage handles coordination and final judgment. Give that assignment its own input, completion criterion and output format. A useful result is the finding, its location in the supplied material and any uncertainty the next stage needs to resolve.
For computer-use work, the release emphasizes interpreting screenshots of dense interfaces. Supply the current visual state, the user’s goal and the relevant constraints. The model can propose an action or explain what the screenshot shows, while the connected environment carries out the operation and returns the resulting state. Keep the actual tool result separate from an action that has only been proposed.
OpenAI’s specification gives a 400,000-token context window, a maximum input of 272,000 tokens and a maximum output of 128,000 tokens, with a knowledge cutoff of August 31, 2025. The reasoning-effort choices are none, low, medium, high and xhigh; none is the default. The model lists Responses, Chat Completions and Batch interfaces, streaming, Structured Outputs, function calling, web and file search, file and image input and prompt caching.
Its producer-listed tools include tool search, image generation, code interpretation, hosted shell and patch operations, skills, computer use and MCP connections. The application provides the available operations and returns their actual results. For a final response, request the change or finding, the relevant evidence and the next action. A proposed patch, a focused review or a concise interface explanation is a useful artifact when it is tied to the exact task and can be checked against the supplied source or screen.
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-5.4-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-5.4-mini:batch, openai/gpt-5.4-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 $0.00075 / 1K tokens Output · Output tokens $0.0045 / 1K tokens Input · Cache read $0.000075 / 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 $0.00075 / 1K tokens Output · Output tokens $0.0045 / 1K tokens Input · Cache read $0.000075 / 1K tokens Input · Cache write — / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
set -eu\n: "${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-5.4-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-5.4-mini:batch', 'openai/gpt-5.4-mini']: raise ValueError("body.model must be an exact documented caller or wire ID")\n return {"endpoint":"/v1/chat/completions", "model":"openai/gpt-5.4-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-5.4-mini:batch
Use the method paths shown above.