Grok 4.3 is the producer’s general-purpose model emphasizing instruction following and tool use. Its model card accepts text and images, generates text and specifies a 1,000,000-token context window. The listed capabilities include function calling, structured outputs and configurable reasoning, with batch processing supported in the manufacturer’s service.
The current detailed model specification lists none, low, medium, high and xhigh reasoning effort, with low as the default. None skips the reasoning stage; the other choices allocate thinking before the answer. These are settings of the exact producer model card, distinct from the effort lists and defaults of later Grok releases.
For long-document analysis, provide the relevant material and define the sections or findings the answer should return. The context can hold a large working set, but the task still benefits from priorities and an explicit deliverable. Ask for the conclusion, the supporting passage and any unresolved condition instead of treating a long context as a substitute for a clear question.
For visual material, identify the object, chart feature or page detail to inspect and explain how it relates to the written task. A useful result ties the observation to the question. If a conclusion also depends on external information, provide that evidence separately so the model can distinguish what the image shows from what another source establishes.
In a function-based workflow, the model generates a proposed call from the tool definitions. The application executes the function and returns the result. Keep the function’s inputs, the returned observation and the next model step connected. For a code or data task, request a result that states what was proposed and what the application actually observed.
Structured output can turn a document extraction or analytical report into defined fields. The producer’s schema support covers common data types and bounded constraints, while some advanced constraints depend on model behavior. Give each field a clear meaning and validate the result against the application’s needs. A schema-shaped result still needs evidence for its content; a proposed action still needs execution by the surrounding workflow.
", "aboutText": "Grok 4.3 is the producer’s general-purpose model emphasizing instruction following and tool use. Its model card accepts text and images, generates text and specifies a 1,000,000-token context window. The listed capabilities include function calling, structured outputs and configurable reasoning, with batch processing supported in the manufacturer’s service.\n\nThe current detailed model specification lists none, low, medium, high and xhigh reasoning effort, with low as the default. None skips the reasoning stage; the other choices allocate thinking before the answer. These are settings of the exact producer model card, distinct from the effort lists and defaults of later Grok releases.\n\nFor long-document analysis, provide the relevant material and define the sections or findings the answer should return. The context can hold a large working set, but the task still benefits from priorities and an explicit deliverable. Ask for the conclusion, the supporting passage and any unresolved condition instead of treating a long context as a substitute for a clear question.\n\nFor visual material, identify the object, chart feature or page detail to inspect and explain how it relates to the written task. A useful result ties the observation to the question. If a conclusion also depends on external information, provide that evidence separately so the model can distinguish what the image shows from what another source establishes.\n\nIn a function-based workflow, the model generates a proposed call from the tool definitions. The application executes the function and returns the result. Keep the function’s inputs, the returned observation and the next model step connected. For a code or data task, request a result that states what was proposed and what the application actually observed.\n\nStructured output can turn a document extraction or analytical report into defined fields. The producer’s schema support covers common data types and bounded constraints, while some advanced constraints depend on model behavior. Give each field a clear meaning and validate the result against the application’s needs. A schema-shaped result still needs evidence for its content; a proposed action still needs execution by the surrounding workflow.", "editorialOverlay": { "copy_fields": null, "copy_sha256": null, "copy_source": null, "html_sha256": "ab14cc2a65673688ea0728a51bcbb00e6bb5b4555752405eb9f4c6fb2ea7fde3", "module_sha256": "ee9dc1c50b1e6a8fb7fa062030fcb4646b9aaaeb08be513b723a5a92a7290f06", "raw_model_unchanged": true, "schema": "ailoot-native-model-editorial-v1" }, "locale": "en", "methods": [ { "html": "x-ai/grok-4.3:batchGrok 4.3 is a text-and-image model for instruction-driven work, reasoning and tool-connected tasks, with a 1,000,000-token context. It provides function calling, structured output and selectable reasoning effort in the producer’s documented configuration.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
Grok 4.3 is the producer’s general-purpose model emphasizing instruction following and tool use. Its model card accepts text and images, generates text and specifies a 1,000,000-token context window. The listed capabilities include function calling, structured outputs and configurable reasoning, with batch processing supported in the manufacturer’s service.
The current detailed model specification lists none, low, medium, high and xhigh reasoning effort, with low as the default. None skips the reasoning stage; the other choices allocate thinking before the answer. These are settings of the exact producer model card, distinct from the effort lists and defaults of later Grok releases.
For long-document analysis, provide the relevant material and define the sections or findings the answer should return. The context can hold a large working set, but the task still benefits from priorities and an explicit deliverable. Ask for the conclusion, the supporting passage and any unresolved condition instead of treating a long context as a substitute for a clear question.
For visual material, identify the object, chart feature or page detail to inspect and explain how it relates to the written task. A useful result ties the observation to the question. If a conclusion also depends on external information, provide that evidence separately so the model can distinguish what the image shows from what another source establishes.
In a function-based workflow, the model generates a proposed call from the tool definitions. The application executes the function and returns the result. Keep the function’s inputs, the returned observation and the next model step connected. For a code or data task, request a result that states what was proposed and what the application actually observed.
Structured output can turn a document extraction or analytical report into defined fields. The producer’s schema support covers common data types and bounded constraints, while some advanced constraints depend on model behavior. Give each field a clear meaning and validate the result against the application’s needs. A schema-shaped result still needs evidence for its content; a proposed action still needs execution by the surrounding workflow.
POST /v1/batchestexttext| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldendpoint | Typestring | RequirementRequired | Values & limits /v1/chat/completions | Default— |
Fieldmodel | Typestring | RequirementRequired | Values & limits x-ai/grok-4.3: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 x-ai/grok-4.3:batch, x-ai/grok-4.3 | Default— |
Fieldprovider.only | Typestring[] | RequirementOptional | Values & limits xai | 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 $0.002 / 1K tokens Output · Output tokens $0.004 / 1K tokens Input · Cache read $0.00032 / 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 including200,000 | Charge Input · Input tokens $0.002 / 1K tokens Output · Output tokens $0.004 / 1K tokens Input · Cache read $0.00032 / 1K tokens Input · Cache write — / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
| ConfigurationRequest-length tier 2 | Rate schedule— | Context tokens, more than200,000 | Context tokens, up to and includingNo upper bound | Charge Input · Input tokens $0.004 / 1K tokens Output · Output tokens $0.008 / 1K tokens Input · Cache read $0.00064 / 1K tokens Input · Cache write — / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
set -eu\n: "${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":"x-ai/grok-4.3: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 ['x-ai/grok-4.3:batch', 'x-ai/grok-4.3']: raise ValueError("body.model must be an exact documented caller or wire ID")\n return {"endpoint":"/v1/chat/completions", "model":"x-ai/grok-4.3: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 x-ai/grok-4.3:batch
Use the method paths shown above.