GPT-4.1 is OpenAI’s non-reasoning model for instruction following and tool calling, with broad knowledge across domains. It accepts text and images and returns text without a dedicated reasoning step. Its large context makes it useful when the task is clearly defined but the relevant material spans substantial documents or code. State the operation you want, the source material it applies to and the form of the final result.
For document processing, separate the instructions from the material using headings or other clear boundaries. Label documents and preserve identifiers that the answer needs to reference. Define the fields for extraction, the criteria for comparison or the audience and length for a rewrite. An illustrative output can be a table of requirements with their source locations, a summary organized by a specified set of questions, or a structured record that distinguishes missing information from an explicit value.
For coding, provide the relevant files, the requested change and the behavior that must remain intact. Ask for the actual artifact you need: a revised function, a complete file, proposed tool arguments or an explanation of the affected logic. Examples can clarify a formatting rule or an edge case, but should follow the same requirements as the real task. OpenAI’s prompting guidance emphasizes direct instructions, roles, source boundaries and representative examples rather than assuming that a long prompt is automatically precise.
The exact GPT-4.1 specification lists a 1,047,576-token context window, a maximum output of 32,768 tokens and a knowledge cutoff of June 1, 2024. The large input context does not imply equally large output, so a request to rewrite extensive material should identify the portion and final artifact required. Keep enough room for the answer and supply current information when the task depends on facts after the cutoff. GPT-4.1’s documented model type is chat; reasoning-effort settings from newer reasoning models are not part of this profile.
OpenAI documents Chat Completions, Responses, Assistants, Batch and fine-tuning interfaces for GPT-4.1. The listed features include streaming, Structured Outputs, function calling, file uploads, image input, web and file search, fine-tuning and prompt caching. Its tools include code interpretation, image generation and MCP connections as well as search and functions. The model’s own output is text, while a tool can return a separate artifact or observation. Define the purpose and arguments of an application operation and use its actual returned result; a model-generated request is not confirmation that an external operation completed.
A distinctive feature is Predicted Outputs in OpenAI’s Chat Completions interface. When most of a desired text or code result is already known, the existing content can be supplied as a prediction to help regenerate the complete result with a small change. For example, provide a current file and ask for a narrowly specified property rename while retaining the surrounding code. The prediction is expected output content, not an instruction or a guarantee that the unchanged parts will remain correct. Inspect the regenerated artifact against the requested modification and the original material.
Predicted Outputs have their own compatibility limits. OpenAI documents text-only use, with no audio inputs or outputs and no function calling in that request. They do not support multiple choices with n greater than 1, log probabilities, positive presence or frequency penalties, or the max_completion_tokens parameter. Keep this regeneration workflow distinct from a task that needs tool calls. Prediction text can match portions of the output, and the producer reports accepted and rejected prediction tokens; an unsuitable prediction can be partly unused, so the feature does not guarantee a particular speedup or result.
For ordinary instruction-following tasks, a useful GPT-4.1 result is a document or code artifact that follows a defined contract, an extraction with explicit fields, or a tool request whose arguments fit the supplied operation. For a small change to a mostly unchanged file, the predicted-output workflow gives a more specific way to prepare the expected result. In both cases, preserve the source material needed to check the answer and make ambiguity explicit. The model’s context capacity and tools support the workflow; the task brief still determines what a complete, usable answer contains.
", "aboutText": "GPT-4.1 is OpenAI’s non-reasoning model for instruction following and tool calling, with broad knowledge across domains. It accepts text and images and returns text without a dedicated reasoning step. Its large context makes it useful when the task is clearly defined but the relevant material spans substantial documents or code. State the operation you want, the source material it applies to and the form of the final result.\n\nFor document processing, separate the instructions from the material using headings or other clear boundaries. Label documents and preserve identifiers that the answer needs to reference. Define the fields for extraction, the criteria for comparison or the audience and length for a rewrite. An illustrative output can be a table of requirements with their source locations, a summary organized by a specified set of questions, or a structured record that distinguishes missing information from an explicit value.\n\nFor coding, provide the relevant files, the requested change and the behavior that must remain intact. Ask for the actual artifact you need: a revised function, a complete file, proposed tool arguments or an explanation of the affected logic. Examples can clarify a formatting rule or an edge case, but should follow the same requirements as the real task. OpenAI’s prompting guidance emphasizes direct instructions, roles, source boundaries and representative examples rather than assuming that a long prompt is automatically precise.\n\nThe exact GPT-4.1 specification lists a 1,047,576-token context window, a maximum output of 32,768 tokens and a knowledge cutoff of June 1, 2024. The large input context does not imply equally large output, so a request to rewrite extensive material should identify the portion and final artifact required. Keep enough room for the answer and supply current information when the task depends on facts after the cutoff. GPT-4.1’s documented model type is chat; reasoning-effort settings from newer reasoning models are not part of this profile.\n\nOpenAI documents Chat Completions, Responses, Assistants, Batch and fine-tuning interfaces for GPT-4.1. The listed features include streaming, Structured Outputs, function calling, file uploads, image input, web and file search, fine-tuning and prompt caching. Its tools include code interpretation, image generation and MCP connections as well as search and functions. The model’s own output is text, while a tool can return a separate artifact or observation. Define the purpose and arguments of an application operation and use its actual returned result; a model-generated request is not confirmation that an external operation completed.\n\nA distinctive feature is Predicted Outputs in OpenAI’s Chat Completions interface. When most of a desired text or code result is already known, the existing content can be supplied as a prediction to help regenerate the complete result with a small change. For example, provide a current file and ask for a narrowly specified property rename while retaining the surrounding code. The prediction is expected output content, not an instruction or a guarantee that the unchanged parts will remain correct. Inspect the regenerated artifact against the requested modification and the original material.\n\nPredicted Outputs have their own compatibility limits. OpenAI documents text-only use, with no audio inputs or outputs and no function calling in that request. They do not support multiple choices with n greater than 1, log probabilities, positive presence or frequency penalties, or the max_completion_tokens parameter. Keep this regeneration workflow distinct from a task that needs tool calls. Prediction text can match portions of the output, and the producer reports accepted and rejected prediction tokens; an unsuitable prediction can be partly unused, so the feature does not guarantee a particular speedup or result.\n\nFor ordinary instruction-following tasks, a useful GPT-4.1 result is a document or code artifact that follows a defined contract, an extraction with explicit fields, or a tool request whose arguments fit the supplied operation. For a small change to a mostly unchanged file, the predicted-output workflow gives a more specific way to prepare the expected result. In both cases, preserve the source material needed to check the answer and make ambiguity explicit. The model’s context capacity and tools support the workflow; the task brief still determines what a complete, usable answer contains.", "editorialOverlay": { "copy_fields": null, "copy_sha256": null, "copy_source": null, "html_sha256": "403c642bf1d658fd2fbfa61d148ee6733f69c83b1ea5aad33a41c687d68d72fa", "module_sha256": "ee9dc1c50b1e6a8fb7fa062030fcb4646b9aaaeb08be513b723a5a92a7290f06", "raw_model_unchanged": true, "schema": "ailoot-native-model-editorial-v1" }, "locale": "en", "methods": [ { "html": "openai/gpt-4.1:batchOpenAI GPT-4.1 is a non-reasoning model for precise instruction following, coding and tool calling. It accepts text and images and returns text, with a large context window, Structured Outputs and Predicted Outputs for regenerating mostly unchanged text or code.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
GPT-4.1 is OpenAI’s non-reasoning model for instruction following and tool calling, with broad knowledge across domains. It accepts text and images and returns text without a dedicated reasoning step. Its large context makes it useful when the task is clearly defined but the relevant material spans substantial documents or code. State the operation you want, the source material it applies to and the form of the final result.
For document processing, separate the instructions from the material using headings or other clear boundaries. Label documents and preserve identifiers that the answer needs to reference. Define the fields for extraction, the criteria for comparison or the audience and length for a rewrite. An illustrative output can be a table of requirements with their source locations, a summary organized by a specified set of questions, or a structured record that distinguishes missing information from an explicit value.
For coding, provide the relevant files, the requested change and the behavior that must remain intact. Ask for the actual artifact you need: a revised function, a complete file, proposed tool arguments or an explanation of the affected logic. Examples can clarify a formatting rule or an edge case, but should follow the same requirements as the real task. OpenAI’s prompting guidance emphasizes direct instructions, roles, source boundaries and representative examples rather than assuming that a long prompt is automatically precise.
The exact GPT-4.1 specification lists a 1,047,576-token context window, a maximum output of 32,768 tokens and a knowledge cutoff of June 1, 2024. The large input context does not imply equally large output, so a request to rewrite extensive material should identify the portion and final artifact required. Keep enough room for the answer and supply current information when the task depends on facts after the cutoff. GPT-4.1’s documented model type is chat; reasoning-effort settings from newer reasoning models are not part of this profile.
OpenAI documents Chat Completions, Responses, Assistants, Batch and fine-tuning interfaces for GPT-4.1. The listed features include streaming, Structured Outputs, function calling, file uploads, image input, web and file search, fine-tuning and prompt caching. Its tools include code interpretation, image generation and MCP connections as well as search and functions. The model’s own output is text, while a tool can return a separate artifact or observation. Define the purpose and arguments of an application operation and use its actual returned result; a model-generated request is not confirmation that an external operation completed.
A distinctive feature is Predicted Outputs in OpenAI’s Chat Completions interface. When most of a desired text or code result is already known, the existing content can be supplied as a prediction to help regenerate the complete result with a small change. For example, provide a current file and ask for a narrowly specified property rename while retaining the surrounding code. The prediction is expected output content, not an instruction or a guarantee that the unchanged parts will remain correct. Inspect the regenerated artifact against the requested modification and the original material.
Predicted Outputs have their own compatibility limits. OpenAI documents text-only use, with no audio inputs or outputs and no function calling in that request. They do not support multiple choices with n greater than 1, log probabilities, positive presence or frequency penalties, or the max_completion_tokens parameter. Keep this regeneration workflow distinct from a task that needs tool calls. Prediction text can match portions of the output, and the producer reports accepted and rejected prediction tokens; an unsuitable prediction can be partly unused, so the feature does not guarantee a particular speedup or result.
For ordinary instruction-following tasks, a useful GPT-4.1 result is a document or code artifact that follows a defined contract, an extraction with explicit fields, or a tool request whose arguments fit the supplied operation. For a small change to a mostly unchanged file, the predicted-output workflow gives a more specific way to prepare the expected result. In both cases, preserve the source material needed to check the answer and make ambiguity explicit. The model’s context capacity and tools support the workflow; the task brief still determines what a complete, usable answer contains.
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-4.1: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-4.1:batch, openai/gpt-4.1 | 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 $0.002 / 1K tokens Output · Output tokens $0.008 / 1K tokens Input · Cache read $0.0005 / 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 $0.002 / 1K tokens Output · Output tokens $0.008 / 1K tokens Input · Cache read $0.0005 / 1K tokens Input · Cache write — / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
set -eu\n: "${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-4.1: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-4.1:batch', 'openai/gpt-4.1']: raise ValueError("body.model must be an exact documented caller or wire ID")\n return {"endpoint":"/v1/chat/completions", "model":"openai/gpt-4.1: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-4.1:batch
Use the method paths shown above.