OpenAI: GPT-5 Nano (batch)
The Nano edition of GPT-5 is designed for focused summarization and classification. It accepts text and images and returns text, with configurable reasoning and a clear result format for repeated or supporting tasks.
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 Nano is OpenAI’s Nano edition of the GPT-5 reasoning family. The manufacturer highlights summarization and classification as practical uses. It accepts text and images and produces text, making it a focused model choice for a defined operation within a larger workflow. Nano retains its own edition identity and specifications.
For summarization, provide the source material and the purpose of the summary. An illustrative request is to turn several messages about one issue into a brief containing the topic, established facts and the next question. Specify the length, the details that must survive and the distinction between a statement in the source and an inference. This gives the summary a concrete use rather than asking the model to shorten every part equally.
For classification, supply the permitted categories and the criterion that distinguishes them. Include examples when they clarify a difficult boundary, and define how to represent a case that does not fit the available categories. A useful result is the category and a concise explanation tied to the input. An image can provide relevant visual information when the task specifies which detail or label matters.
OpenAI 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 May 31, 2024. The GPT-5 developer release includes Nano among the three sizes supporting reasoning-effort, verbosity and custom-tool controls. The family’s documented effort choices are minimal, low, medium and high; verbosity controls answer detail separately from analysis. Reasoning and visible output share the generated-token budget.
The exact model card lists Responses, Chat Completions and Batch interfaces, streaming, Structured Outputs, function calling, file and image input, file search and prompt caching. Its listed tool set includes functions, web and file search, image generation, code interpretation and MCP connections. Tool operations are separate from the model’s written result: the application executes its actions and returns the actual outputs for continued work.
Define a narrow output contract, such as a summary, category, list of extracted fields or concise proposed reply. State how missing information should be handled and keep the rules consistent when processing similar inputs. For a multi-stage task, pass the relevant result to the next stage together with any unresolved question. Review the final artifact against the supplied source and criteria, keeping an executed operation distinct from one that has only been proposed.
Ailoot Ultra · Batch POST /v1/batches openai/gpt-5-nano:batch
openai/gpt-5-nano:batchOverview
The Nano edition of GPT-5 is designed for focused summarization and classification. It accepts text and images and returns text, with configurable reasoning and a clear result format for repeated or supporting tasks.
- 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-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-nano:batch, openai/gpt-5-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 $0.00005 / 1K tokens Output · Output tokens $0.0004 / 1K tokens Input · Cache read $0.000005 / 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 $0.00005 / 1K tokens Output · Output tokens $0.0004 / 1K tokens Input · Cache read $0.000005 / 1K tokens Input · Cache write — / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
Example requests
Ailoot Ultra · Batch
set -eu
: "${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-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-nano:batch', 'openai/gpt-5-nano']: raise ValueError("body.model must be an exact documented caller or wire ID")
return {"endpoint":"/v1/chat/completions", "model":"openai/gpt-5-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-nano:batch
Method path aliases
Use the method paths shown above.
Related caller IDs
FAQ · OpenAI: GPT-5 Nano (batch)
- What inputs and results are available?
POST /v1/batches·openai/gpt-5-nano:batch:text→text- Which fields does the request use?
POST /v1/batches·openai/gpt-5-nano:batch:endpoint,model,requests,completion_window,provider- What limits apply?
POST /v1/batches·openai/gpt-5-nano:batch:requests.min_items:1