OpenAI: GPT-5.4 Nano (batch)
A compact GPT-5.4 model for classification, data extraction, ranking and simpler supporting agent tasks. It accepts text and images and returns text, with Structured Outputs and function calling in the producer’s documented feature set.
About the model
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
GPT-5.4 nano is OpenAI’s compact GPT-5.4-class model for simple, high-volume tasks. The manufacturer highlights classification, data extraction, ranking and supporting subagent work. It accepts text and images and produces text. This gives nano a focused role in a larger process: complete a defined operation and return the information the next step needs.
For classification, provide the allowed categories, the rule that separates them and the expected result format. An illustrative task is identifying the topic of a request and the facts that justify the category. Include examples when they clarify a difficult boundary, and state how to represent a request that does not fit the available categories. A useful result is the category plus a brief justification tied to the supplied material.
For extraction, specify the fields and their definitions rather than asking for every potentially interesting detail. You can supply written material or an image of a page and identify the values required. Preserve names and units that affect the meaning, and distinguish a missing field from a value inferred from surrounding text. For ranking, supply the candidates and the criterion, then request the ordered list with the relevant evidence for each position.
OpenAI’s GPT-5.4 nano specification lists 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. It supports none, low, medium, high and xhigh reasoning effort; none is the default. The selected effort and requested answer length should fit the task. When reasoning is used, allow space for analysis as well as the visible result.
The producer documents Responses, Chat Completions and Batch interfaces, together with streaming, Structured Outputs, function calling, web and file search, file inputs, image input and prompt caching. Its tool set includes image generation, code interpretation, hosted shell and patch operations, skills and MCP connections. An application exposes its operations and returns their actual results. A structured response describes the form of the output; a function request describes an action for a tool to perform.
In a multi-stage workflow, give nano a bounded supporting task and a clear completion criterion. For example, extract the required facts from several messages, then pass those facts to the next stage for a broader decision. Check the returned fields against the original input and refine the rule when a category boundary is unclear. A concise category, set of values, ranked list or proposed tool arguments is a useful final artifact when its purpose and source evidence remain explicit.
Ailoot Ultra · Batch POST /v1/batches openai/gpt-5.4-nano:batch
openai/gpt-5.4-nano:batchOverview
A compact GPT-5.4 model for classification, data extraction, ranking and simpler supporting agent tasks. It accepts text and images and returns text, with Structured Outputs and function calling in the producer’s documented feature set.
- Accepted inputs
- Text
- Results
- Text
Available in Ailoot · inputs & methods
Ailoot Ultra · Batch POST /v1/batches
- Input types
text- Output types
text- Output format
- inline results[].response.body JSON
| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldendpoint | Typestring | RequirementRequired | Values & limits /v1/chat/completions | Default— |
Fieldmodel | Typestring | RequirementRequired | Values & limits openai/gpt-5.4-nano: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-nano:batch, openai/gpt-5.4-nano | Default— |
Fieldprovider.only | Typestring[] | RequirementOptional | Values & limits openai | Default— |
Fieldprovider.allow_fallbacks | Typeboolean|null | RequirementOptional | Values & limits false, null | Default— |
Method parameters and lifecycle
| 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
Pricing
Tariff references do not add request options. Supported parameters are listed for the selected method.
- Fast mode
- Not specified When fast mode is selected
- Flex mode
- Not specified When flex mode is selected
- Maximum reasoning effort
- Not specified When maximum reasoning effort uses the default adjustment
- Explicit cache pricing
- Not enabled When explicit cache is requested
Ailoot rates
Prices by configuration
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 $0.0002 / 1K tokens Output · Output tokens $0.00125 / 1K tokens Input · Cache read $0.00002 / 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 $0.0002 / 1K tokens Output · Output tokens $0.00125 / 1K tokens Input · Cache read $0.00002 / 1K tokens Input · Cache write — / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
Example requests
Ailoot Ultra · Batch
set -eu
: "${AILOOT_API_KEY:?Set your Ailoot API key}"
python3 - <<'PY_REQUEST'
import json
from pathlib import Path
body=json.loads('{"endpoint":"/v1/chat/completions","model":"openai/gpt-5.4-nano:batch","requests":[{"custom_id":"req-0001","body":{"messages":[{"role":"user","content":"Hello!"}],"max_tokens":16}}]}')
Path("batch-request.json").write_text(json.dumps(body,ensure_ascii=False),encoding="utf-8")
PY_REQUEST
curl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/batches \
-H "Authorization: Bearer $AILOOT_API_KEY" -H 'Content-Type: application/json' \
--data-binary @batch-request.json -D batch-response-headers.txt -o batch-create-response.json
import json, os
from pathlib import Path
from urllib.request import Request, urlopen
def prepare_requests(customer_path):
path = Path(customer_path)
if not path.is_file(): raise ValueError("Customer JSONL file is missing")
items = [json.loads(line) for line in path.read_text(encoding="utf-8").splitlines() if line.strip()]
if not items: raise ValueError("Customer JSONL contains no requests")
ids = set()
for item in items:
if set(item) != {"custom_id", "body"}: raise ValueError("Each item needs custom_id and body")
cid = item["custom_id"]
if not isinstance(cid, str) or not cid.strip() or cid in ids: raise ValueError("custom_id must be nonempty and unique")
ids.add(cid)
if not isinstance(item["body"], dict) or not item["body"].get("messages"): raise ValueError("Supply actual text messages")
supplied_model = item["body"].get("model")
if supplied_model is not None and supplied_model not in ['openai/gpt-5.4-nano:batch', 'openai/gpt-5.4-nano']: raise ValueError("body.model must be an exact documented caller or wire ID")
return {"endpoint":"/v1/chat/completions", "model":"openai/gpt-5.4-nano:batch", "requests":items}
if __name__ == "__main__":
material = os.environ.get("AILOOT_BATCH_REQUESTS_JSONL", "")
if not material: raise SystemExit("Set AILOOT_BATCH_REQUESTS_JSONL to your actual customer file")
body = prepare_requests(material)
key = os.environ.get("AILOOT_API_KEY", "")
if not key: raise SystemExit("Set AILOOT_API_KEY")
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")
with urlopen(request) as response:
raw = response.read()
Path("batch-create-response.json").write_bytes(raw)
print(raw.decode("utf-8"))
import json
from pathlib import Path
raw = Path("batch-status-response.json").read_bytes()
response = json.loads(raw)
if response.get("status") != "completed":
raise SystemExit("Keep the original operation; read its status without resubmitting")
results = response.get("results")
if not isinstance(results, list) or not results:
raise SystemExit("Preserve the original response; completed results are missing")
Path("batch-results.jsonl").write_text("".join(json.dumps(item, ensure_ascii=False) + "\n" for item in results), encoding="utf-8")
# Preserve batch-status-response.json with aggregate usage and every result/error.
Receive and save results
Ailoot Ultra · Batch
inline results[].response.body JSON
GET /v1/batches/{operation_id}
Check job status
set -eu
: "${AILOOT_API_KEY:?Set your Ailoot API key}"
BATCH_OPERATION_ID="$(python3 - <<'PY_ID'
import json
from pathlib import Path
from urllib.parse import quote
response = json.loads(Path("batch-create-response.json").read_text(encoding="utf-8"))
operation = response.get("id")
if not isinstance(operation, str) or not operation: raise SystemExit("No actual original operation ID; preserve the response and do not resubmit")
print(quote(operation, safe=""))
PY_ID
)"
curl --fail-with-body --silent --show-error "https://api.ailoot.ai/v1/batches/$BATCH_OPERATION_ID" \
-H "Authorization: Bearer $AILOOT_API_KEY" -o batch-status-response.json
Keep all completed results
Keep 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.
If the original response needs checking
Preserve the original operation ID. 503 result_pending_check has X-Should-Retry:false; never resubmit an unknown original batch.
Response fields
| 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 |
Errors and next steps
| 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. |
Aliases
Model aliases
Ailoot Ultra openai/gpt-5.4-nano:batch
Method path aliases
Use the method paths shown above.
Related caller IDs
FAQ · OpenAI: GPT-5.4 Nano (batch)
- What inputs and results are available?
POST /v1/batches·openai/gpt-5.4-nano:batch:text→text- Which fields does the request use?
POST /v1/batches·openai/gpt-5.4-nano:batch:endpoint,model,requests,completion_window,provider- What limits apply?
POST /v1/batches·openai/gpt-5.4-nano:batch:requests.min_items:1