GPT-4.1 Nano is OpenAI’s fastest member of the GPT-4.1 family, designed for instruction following and tool calling without a separate reasoning step. It accepts text and images and produces text. Nano suits a well-defined operation where the application needs a compact answer, a set of fields or the next tool request rather than an expansive investigation.
A useful example is sorting incoming messages against a supplied set of categories. Explain each category, show how ambiguous or out-of-scope messages should be represented, and ask for the category together with the source phrase that supports it. For extraction, define field names, permitted values and the treatment of missing information. For a short writing task, supply the facts and ask for the specific passage that the next application stage needs. The examples and constraints should be small enough to remain clear even when the source collection is large.
The producer lists a 1,047,576-token context window, a maximum output of 32,768 tokens and a knowledge cutoff of June 1, 2024. These specifications match the window of GPT-4.1 Mini while preserving Nano’s separate model identity and responsiveness-oriented role. Label documents and conversation sections, include only the context relevant to the operation, and reserve space for the complete answer. Retrieval results or supplied reference text can provide facts that are newer than the model’s learned knowledge.
Nano supports streaming and Structured Outputs. A schema-constrained final response serves a different purpose from a function call: one gives the application a completed artifact, while the other asks it to perform an operation. Structured Outputs supports a subset of JSON Schema; define required properties and a clear representation for unknown values. Handle refusals and incomplete responses separately, and check the extracted values against the input rather than equating a valid shape with a correct conclusion.
The documented tool set includes function calling, file search, code interpretation, MCP connections and a separate image-generation tool. The application supplies tool descriptions and schemas, executes custom functions and returns their actual results. For the April 14, 2025 Nano snapshot, OpenAI specifically recommends disabling parallel tool calls because the model can request the same tool several times in one turn. This is a snapshot-specific execution consideration, not a reason to interpret several proposed calls as completed actions.
Image input adds visual evidence to a text task, such as reading a visible label or describing the state shown in a screenshot. Use supported PNG, JPEG, WebP or non-animated GIF images, keep text legible and explain which observation matters. The vision guide notes difficulties with small or rotated text, precise spatial relationships, object counts and some non-Latin writing. Give surrounding context where a crop alone would make a label or diagram ambiguous. Nano’s own response is text; an image-generation tool returns its own separate artifact. In OpenAI’s documented vision workflow, the total request payload is limited to 512 MB and a request can include up to 1,500 images; the images and text must also fit the model’s context.
In the producer’s file workflow, a vision-capable model receives both extracted text and page images from PDFs. Other document formats contribute extracted text, and spreadsheet augmentation considers up to the first 1,000 rows per sheet. A file must be under 50 MB, with 50 MB combined across the request. Chat Completions supports PDF file parts, while the documented Responses workflow accepts additional file types. Embedded charts in non-PDF documents are not passed as image content, so a PDF representation is useful when the visual evidence matters.
OpenAI documents prompt caching for Nano and Predicted Outputs for the GPT-4.1 Nano series. Prompt caching reuses an eligible input prefix in the producer service. Predicted Outputs instead supplies largely known output text for an edit, such as revising a document or code file while preserving its unchanged portions. The prediction path is text-only and excludes function calling, logprobs and max_completion_tokens, among other controls. Choose the path for the actual operation and keep its result associated with the original source.
The producer also documents supervised fine-tuning and direct preference optimisation for Nano’s named GPT-4.1 snapshot. Training examples and preferred-versus-rejected responses adapt a model to a recurring task; they are distinct from the context supplied to an ordinary request. For a practical extraction or classification stage, start with clear categories, examples and an explicit final format, then inspect whether the returned fields preserve the facts that the next stage will use.
", "aboutText": "GPT-4.1 Nano is OpenAI’s fastest member of the GPT-4.1 family, designed for instruction following and tool calling without a separate reasoning step. It accepts text and images and produces text. Nano suits a well-defined operation where the application needs a compact answer, a set of fields or the next tool request rather than an expansive investigation.\n\nA useful example is sorting incoming messages against a supplied set of categories. Explain each category, show how ambiguous or out-of-scope messages should be represented, and ask for the category together with the source phrase that supports it. For extraction, define field names, permitted values and the treatment of missing information. For a short writing task, supply the facts and ask for the specific passage that the next application stage needs. The examples and constraints should be small enough to remain clear even when the source collection is large.\n\nThe producer lists a 1,047,576-token context window, a maximum output of 32,768 tokens and a knowledge cutoff of June 1, 2024. These specifications match the window of GPT-4.1 Mini while preserving Nano’s separate model identity and responsiveness-oriented role. Label documents and conversation sections, include only the context relevant to the operation, and reserve space for the complete answer. Retrieval results or supplied reference text can provide facts that are newer than the model’s learned knowledge.\n\nNano supports streaming and Structured Outputs. A schema-constrained final response serves a different purpose from a function call: one gives the application a completed artifact, while the other asks it to perform an operation. Structured Outputs supports a subset of JSON Schema; define required properties and a clear representation for unknown values. Handle refusals and incomplete responses separately, and check the extracted values against the input rather than equating a valid shape with a correct conclusion.\n\nThe documented tool set includes function calling, file search, code interpretation, MCP connections and a separate image-generation tool. The application supplies tool descriptions and schemas, executes custom functions and returns their actual results. For the April 14, 2025 Nano snapshot, OpenAI specifically recommends disabling parallel tool calls because the model can request the same tool several times in one turn. This is a snapshot-specific execution consideration, not a reason to interpret several proposed calls as completed actions.\n\nImage input adds visual evidence to a text task, such as reading a visible label or describing the state shown in a screenshot. Use supported PNG, JPEG, WebP or non-animated GIF images, keep text legible and explain which observation matters. The vision guide notes difficulties with small or rotated text, precise spatial relationships, object counts and some non-Latin writing. Give surrounding context where a crop alone would make a label or diagram ambiguous. Nano’s own response is text; an image-generation tool returns its own separate artifact. In OpenAI’s documented vision workflow, the total request payload is limited to 512 MB and a request can include up to 1,500 images; the images and text must also fit the model’s context.\n\nIn the producer’s file workflow, a vision-capable model receives both extracted text and page images from PDFs. Other document formats contribute extracted text, and spreadsheet augmentation considers up to the first 1,000 rows per sheet. A file must be under 50 MB, with 50 MB combined across the request. Chat Completions supports PDF file parts, while the documented Responses workflow accepts additional file types. Embedded charts in non-PDF documents are not passed as image content, so a PDF representation is useful when the visual evidence matters.\n\nOpenAI documents prompt caching for Nano and Predicted Outputs for the GPT-4.1 Nano series. Prompt caching reuses an eligible input prefix in the producer service. Predicted Outputs instead supplies largely known output text for an edit, such as revising a document or code file while preserving its unchanged portions. The prediction path is text-only and excludes function calling, logprobs and max_completion_tokens, among other controls. Choose the path for the actual operation and keep its result associated with the original source.\n\nThe producer also documents supervised fine-tuning and direct preference optimisation for Nano’s named GPT-4.1 snapshot. Training examples and preferred-versus-rejected responses adapt a model to a recurring task; they are distinct from the context supplied to an ordinary request. For a practical extraction or classification stage, start with clear categories, examples and an explicit final format, then inspect whether the returned fields preserve the facts that the next stage will use.", "editorialOverlay": { "copy_fields": null, "copy_sha256": null, "copy_source": null, "html_sha256": "52a1a80250d9c549a162e6e7236382648b45388efb6b2af5903cff61df0c4945", "module_sha256": "ee9dc1c50b1e6a8fb7fa062030fcb4646b9aaaeb08be513b723a5a92a7290f06", "raw_model_unchanged": true, "schema": "ailoot-native-model-editorial-v1" }, "locale": "en", "methods": [ { "html": "openai/gpt-4.1-nano:batchGPT-4.1 Nano is the compact, responsive member of the GPT-4.1 family for tightly specified text and image tasks. Its large context, Structured Outputs and function calling support classification, extraction and focused application steps.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
GPT-4.1 Nano is OpenAI’s fastest member of the GPT-4.1 family, designed for instruction following and tool calling without a separate reasoning step. It accepts text and images and produces text. Nano suits a well-defined operation where the application needs a compact answer, a set of fields or the next tool request rather than an expansive investigation.
A useful example is sorting incoming messages against a supplied set of categories. Explain each category, show how ambiguous or out-of-scope messages should be represented, and ask for the category together with the source phrase that supports it. For extraction, define field names, permitted values and the treatment of missing information. For a short writing task, supply the facts and ask for the specific passage that the next application stage needs. The examples and constraints should be small enough to remain clear even when the source collection is large.
The producer lists a 1,047,576-token context window, a maximum output of 32,768 tokens and a knowledge cutoff of June 1, 2024. These specifications match the window of GPT-4.1 Mini while preserving Nano’s separate model identity and responsiveness-oriented role. Label documents and conversation sections, include only the context relevant to the operation, and reserve space for the complete answer. Retrieval results or supplied reference text can provide facts that are newer than the model’s learned knowledge.
Nano supports streaming and Structured Outputs. A schema-constrained final response serves a different purpose from a function call: one gives the application a completed artifact, while the other asks it to perform an operation. Structured Outputs supports a subset of JSON Schema; define required properties and a clear representation for unknown values. Handle refusals and incomplete responses separately, and check the extracted values against the input rather than equating a valid shape with a correct conclusion.
The documented tool set includes function calling, file search, code interpretation, MCP connections and a separate image-generation tool. The application supplies tool descriptions and schemas, executes custom functions and returns their actual results. For the April 14, 2025 Nano snapshot, OpenAI specifically recommends disabling parallel tool calls because the model can request the same tool several times in one turn. This is a snapshot-specific execution consideration, not a reason to interpret several proposed calls as completed actions.
Image input adds visual evidence to a text task, such as reading a visible label or describing the state shown in a screenshot. Use supported PNG, JPEG, WebP or non-animated GIF images, keep text legible and explain which observation matters. The vision guide notes difficulties with small or rotated text, precise spatial relationships, object counts and some non-Latin writing. Give surrounding context where a crop alone would make a label or diagram ambiguous. Nano’s own response is text; an image-generation tool returns its own separate artifact. In OpenAI’s documented vision workflow, the total request payload is limited to 512 MB and a request can include up to 1,500 images; the images and text must also fit the model’s context.
In the producer’s file workflow, a vision-capable model receives both extracted text and page images from PDFs. Other document formats contribute extracted text, and spreadsheet augmentation considers up to the first 1,000 rows per sheet. A file must be under 50 MB, with 50 MB combined across the request. Chat Completions supports PDF file parts, while the documented Responses workflow accepts additional file types. Embedded charts in non-PDF documents are not passed as image content, so a PDF representation is useful when the visual evidence matters.
OpenAI documents prompt caching for Nano and Predicted Outputs for the GPT-4.1 Nano series. Prompt caching reuses an eligible input prefix in the producer service. Predicted Outputs instead supplies largely known output text for an edit, such as revising a document or code file while preserving its unchanged portions. The prediction path is text-only and excludes function calling, logprobs and max_completion_tokens, among other controls. Choose the path for the actual operation and keep its result associated with the original source.
The producer also documents supervised fine-tuning and direct preference optimisation for Nano’s named GPT-4.1 snapshot. Training examples and preferred-versus-rejected responses adapt a model to a recurring task; they are distinct from the context supplied to an ordinary request. For a practical extraction or classification stage, start with clear categories, examples and an explicit final format, then inspect whether the returned fields preserve the facts that the next stage will use.
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-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-4.1-nano:batch, openai/gpt-4.1-nano | 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 $0.0001 / 1K tokens Output · Output tokens $0.0004 / 1K tokens Input · Cache read $0.000025 / 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 $0.0001 / 1K tokens Output · Output tokens $0.0004 / 1K tokens Input · Cache read $0.000025 / 1K tokens Input · Cache write — / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
set -eu\n: "${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-nano: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-nano:batch', 'openai/gpt-4.1-nano']: raise ValueError("body.model must be an exact documented caller or wire ID")\n return {"endpoint":"/v1/chat/completions", "model":"openai/gpt-4.1-nano: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-nano:batch
Use the method paths shown above.