models

OpenAI: GPT-6 Luna Pro (batch)

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OpenAI: GPT-6 Luna Pro (batch)

GPT-6 Luna with Pro reasoning for focused tasks that need additional analysis before a final answer. The base model accepts text and images, making it useful for rule-based document handling, visual questions and concise written results.

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-6 Luna Pro denotes OpenAI’s GPT-6 Luna with Pro reasoning. Luna is designed for focused tasks and high-volume workloads; its inputs are text and images and its output is text. Pro adds model work when developing the final answer. It is an execution mode applied to the chosen base model, distinct from the reasoning-effort setting that controls the amount of analysis within that mode.

A practical application is processing a narrowly defined request with several rules or exceptions. Supply the request, the categories it may belong to and the information that determines the next action. Ask for the category, the relevant facts and a short proposed response. An illustrative task is to classify a document request, identify a missing field and draft a message asking for that field. The task remains focused even when its conditions require careful consideration.

For an image-based question, provide a clear screenshot or other visual material and identify the detail to inspect. You can request an explanation of a visible interface state together with a short instruction for the reader. For written material, define the fields or statements that must survive the transformation, the allowed categories and the expected answer format. A small set of representative examples can clarify the boundary between similar categories without making the model infer your rules.

OpenAI specifies a 1,050,000-token context window for GPT-6 Luna, with a maximum input of 922,000 tokens and a maximum output of 128,000 tokens. The documented knowledge cutoff is May 18, 2026. The base model supports none, low, medium, high, xhigh and max reasoning effort, with medium as the default. Reasoning occupies token space as well as the final answer, so the task brief and supplied material should leave room for analysis and the requested result.

The producer documents Pro in the Responses API for difficult requests that allow additional latency and model activity. GPT-6 Luna’s built-in tools and reasoning with function calls use Responses; in the producer’s Chat Completions interface, function calling is supported only with reasoning effort set to none. The base model lists streaming, Structured Outputs, web and file search, function calling and prompt caching. Its tool set also includes image generation, code interpretation, shell and patch operations, skills, computer use, MCP connections and tool search. Your application determines and executes the operations it exposes.

Use a precise output contract for the final task: the decision, the fields supporting it and any information still missing. For example, ask for one category, a brief justification tied to the supplied rule and the next question to send to the reader. Review the result against those rules rather than adding an unrestricted discussion. This workflow uses Luna’s focused-task role while reserving Pro’s extra work for a request whose dependencies or exceptions need fuller examination.

Ailoot Ultra · Batch POST /v1/batches openai/gpt-6-luna-pro:batch

openai/gpt-6-luna-pro:batch

OpenAI /openai/gpt-6-luna-pro:batch

Overview

GPT-6 Luna with Pro reasoning for focused tasks that need additional analysis before a final answer. The base model accepts text and images, making it useful for rule-based document handling, visual questions and concise written results.

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
FieldTypeRequirementValues & limitsDefault
FieldendpointTypestringRequirementRequiredValues & limits
/v1/chat/completions
Default—
FieldmodelTypestringRequirementRequiredValues & limits
openai/gpt-6-luna-pro:batch
Default—
FieldrequestsTypebatch_item[]RequirementRequiredValues & limits
Minimum items: 1
Default—
Fieldrequests[].custom_idTypestringRequirementRequiredValues & limitsDefault—
Fieldrequests[].body.messagesTypemessage[]RequirementRequiredValues & limitsDefault—
Fieldcompletion_windowTypestringRequirementOptionalValues & limits
24h
Default—
FieldproviderTypeobject|nullRequirementOptionalValues & limitsDefault—
Fieldrequests[].body.modelTypestringRequirementOptionalValues & limits
openai/gpt-6-luna-pro:batch, openai/gpt-6-luna-pro
Default—
Fieldprovider.onlyTypestring[]RequirementOptionalValues & limits
openai
Default—
Fieldprovider.allow_fallbacksTypeboolean|nullRequirementOptionalValues & limits
false, null
Default—
Method parameters and lifecycle
ParameterContract
Parametercontent_typeContractapplication/json
ParameterrequiredContractendpoint, model, requests
Parametermaterial_typesContracttext
ParameterrepresentationContractinline requests[]; each item has custom_id and body
Parameterrequest_endpoint_profileContract/v1/chat/completions
Parameterfile_materialContractCaller 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_orderContractendpoint, model, provider (if supplied), completion_window (if supplied), requests last
Parameteroptional_controlsContractprovider.only, provider.allow_fallbacks, completion_window
Parameterschema_scopeContractText 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.

ConfigurationRate scheduleContext tokens, more thanContext tokens, up to and includingCharge
ConfigurationBase token ratesRate scheduleBase rateContext tokens, more than—Context tokens, up to and includingNo upper boundCharge
Input · Input tokens $0.0001 / 1K tokens
Output · Output tokens $0.0005 / 1K tokens
Input · Cache read $0.00001 / 1K tokens
Input · Cache write $0.000125 / 1K tokens
Input · Cache write, 1 hour — / 1K tokens
ConfigurationRequest-length tier 1Rate schedule—Context tokens, more than0Context tokens, up to and including272,000Charge
Input · Input tokens $0.0001 / 1K tokens
Output · Output tokens $0.0005 / 1K tokens
Input · Cache read $0.00001 / 1K tokens
Input · Cache write $0.000125 / 1K tokens
Input · Cache write, 1 hour — / 1K tokens
ConfigurationRequest-length tier 2Rate schedule—Context tokens, more than272,000Context tokens, up to and includingNo upper boundCharge
Input · Input tokens $0.0002 / 1K tokens
Output · Output tokens $0.00075 / 1K tokens
Input · Cache read $0.00002 / 1K tokens
Input · Cache write $0.00025 / 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

curl · retained fixturePython HTTP · actual customer JSONLPython · save every completed result
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-6-luna-pro: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-6-luna-pro:batch', 'openai/gpt-6-luna-pro']: raise ValueError("body.model must be an exact documented caller or wire ID")
    return {"endpoint":"/v1/chat/completions", "model":"openai/gpt-6-luna-pro: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
curl · original operation 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
FieldType
Fieldresults[].custom_idTypestring
Fieldresults[].response.body.choices[].message.contentTypestring|null
Fieldresults[].errorTypeobject|null
FieldusageTypeobject|null
FieldidTypestring
FieldmodelTypestring
FieldendpointTypestring
FieldstatusTypestring
Fieldrequest_countsTypeobject
Fieldresults[].idTypestring
Fieldresults[].response.status_codeTypeinteger
Fieldresults[].response.request_idTypestring
Errors and next steps
ResponseConditionNext step
Response400 · invalid_request_errorConditionInvalid documented inline envelope, duplicate keys, item identity, or configured model capabilities conflict.Next stepCorrect the indicated fields before any new submission.
Response401 · authentication_errorConditionInvalid API key.Next stepUse the authorized Ailoot key.
Response403 · permission_errorConditionBatch group unavailable.Next stepCheck key/group entitlement.
Response422 · invalid_request_errorConditionBatch uses balance billing; subscription creation is rejected.Next stepUse the authorized balance-billed group.
Response422 · price_unavailableConditionBatch input cannot be quoted.Next stepPreserve response; use documented family input.
Response402 · billing_errorConditionBatch reserve unavailable.Next stepResolve admission before a new request.
Response503 · price_unavailableConditionBatch method price/native rates unavailable.Next stepPreserve response; do not substitute prices.
Response503 · api_errorConditionExact supplier route unavailable.Next stepPreserve response for route repair.
Response503 · result_pending_checkConditionOriginal result requires checking; X-Should-Retry:false.Next stepKeep original operation_id/id and read its status; never repeat original POST.
Response404 · not_foundConditionBatch 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-6-luna-pro:batch

Method path aliases

Use the method paths shown above.

Related caller IDs

openai/gpt-6-luna-pro:batch

FAQ · OpenAI: GPT-6 Luna Pro (batch)
What inputs and results are available?

POST /v1/batches · openai/gpt-6-luna-pro:batch: text → text

Which fields does the request use?

POST /v1/batches · openai/gpt-6-luna-pro:batch: endpoint, model, requests, completion_window, provider

What limits apply?

POST /v1/batches · openai/gpt-6-luna-pro:batch: requests.min_items: 1

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