o3 is OpenAI’s general-purpose o-series reasoning model for complex tasks. The manufacturer highlights mathematics, science, coding, visual reasoning, technical writing and instruction following. It accepts text and images and produces text, so a task can combine a written problem, code and a diagram rather than treating each source as an isolated question. The intended result is a considered answer to a multi-step problem, with the information required to assess that answer.
For a mathematical or scientific question, provide the complete problem, definitions, known quantities, units and constraints. State whether you need a derivation, a numerical result, a comparison of hypotheses or an explanation for a particular audience. An illustrative output brief is: identify the relevant assumptions, present the solution with its units, and explain which supplied facts support it. Ask for a useful explanation in the final answer; OpenAI’s internal reasoning tokens are not exposed as the model’s raw working trace.
For software work, supply the relevant code, the expected behavior and the observed failure, including the exact error or input that reveals it. Ask o3 to connect the symptoms to a plausible cause and propose a focused change with an explanation. A visual task can include a diagram, screenshot or plotted result: identify the question and the labels or region that matter, retain a legible scale, and request a conclusion tied to what is actually visible. These preparation steps give the model a defined problem and a concrete output instead of an unrestricted request to inspect everything.
OpenAI lists a 200,000-token context window, a maximum output of 100,000 tokens and a knowledge cutoff of June 1, 2024. Internal reasoning uses context and output capacity alongside the visible answer. The maximum output is therefore not a promise of 100,000 visible answer tokens, and the context is not an additional independent output allowance. Leave room after the input material for analysis and the requested result. The producer’s reasoning guide describes incomplete responses when a context or output limit is reached; this can happen before a visible answer appears.
The exact o3 specification documents Chat Completions, Responses and Batch interfaces, together with streaming, Structured Outputs, function calling, file uploads, image input, file search and prompt caching. Its documented tool set includes web and file search, code interpretation, image generation and MCP connections. A search result or calculation is additional evidence for the task, while a function call requests an operation from the surrounding application. Keep the returned evidence associated with the question it answers, and distinguish a proposed action from a confirmed result. The model’s own output remains text even when an image-generation tool creates a separate artifact.
OpenAI’s reasoning guidance favors simple, direct instructions with clear boundaries between instructions and source material. State the goal, constraints and required answer format instead of prescribing the model’s private thought process. Use examples when they clarify a difficult format or decision rule, and keep those examples consistent with the actual requirements. For a long technical document, label sections and ask for the passages or relationships relevant to a specific question; for code review, specify the behavior and risk you want investigated.
A useful o3 deliverable can be a worked technical explanation, an analysis of a diagram, a documented code correction or a comparison grounded in the supplied material. Make unsupported assumptions and missing information explicit, and check decisive calculations or code behavior against the original problem. Its knowledge cutoff means that recent facts need supplied material or an appropriate information tool. Clear evidence, an explicit completion criterion and sufficient output space help turn a broad reasoning task into a result that another person can inspect and use.
", "aboutText": "o3 is OpenAI’s general-purpose o-series reasoning model for complex tasks. The manufacturer highlights mathematics, science, coding, visual reasoning, technical writing and instruction following. It accepts text and images and produces text, so a task can combine a written problem, code and a diagram rather than treating each source as an isolated question. The intended result is a considered answer to a multi-step problem, with the information required to assess that answer.\n\nFor a mathematical or scientific question, provide the complete problem, definitions, known quantities, units and constraints. State whether you need a derivation, a numerical result, a comparison of hypotheses or an explanation for a particular audience. An illustrative output brief is: identify the relevant assumptions, present the solution with its units, and explain which supplied facts support it. Ask for a useful explanation in the final answer; OpenAI’s internal reasoning tokens are not exposed as the model’s raw working trace.\n\nFor software work, supply the relevant code, the expected behavior and the observed failure, including the exact error or input that reveals it. Ask o3 to connect the symptoms to a plausible cause and propose a focused change with an explanation. A visual task can include a diagram, screenshot or plotted result: identify the question and the labels or region that matter, retain a legible scale, and request a conclusion tied to what is actually visible. These preparation steps give the model a defined problem and a concrete output instead of an unrestricted request to inspect everything.\n\nOpenAI lists a 200,000-token context window, a maximum output of 100,000 tokens and a knowledge cutoff of June 1, 2024. Internal reasoning uses context and output capacity alongside the visible answer. The maximum output is therefore not a promise of 100,000 visible answer tokens, and the context is not an additional independent output allowance. Leave room after the input material for analysis and the requested result. The producer’s reasoning guide describes incomplete responses when a context or output limit is reached; this can happen before a visible answer appears.\n\nThe exact o3 specification documents Chat Completions, Responses and Batch interfaces, together with streaming, Structured Outputs, function calling, file uploads, image input, file search and prompt caching. Its documented tool set includes web and file search, code interpretation, image generation and MCP connections. A search result or calculation is additional evidence for the task, while a function call requests an operation from the surrounding application. Keep the returned evidence associated with the question it answers, and distinguish a proposed action from a confirmed result. The model’s own output remains text even when an image-generation tool creates a separate artifact.\n\nOpenAI’s reasoning guidance favors simple, direct instructions with clear boundaries between instructions and source material. State the goal, constraints and required answer format instead of prescribing the model’s private thought process. Use examples when they clarify a difficult format or decision rule, and keep those examples consistent with the actual requirements. For a long technical document, label sections and ask for the passages or relationships relevant to a specific question; for code review, specify the behavior and risk you want investigated.\n\nA useful o3 deliverable can be a worked technical explanation, an analysis of a diagram, a documented code correction or a comparison grounded in the supplied material. Make unsupported assumptions and missing information explicit, and check decisive calculations or code behavior against the original problem. Its knowledge cutoff means that recent facts need supplied material or an appropriate information tool. Clear evidence, an explicit completion criterion and sufficient output space help turn a broad reasoning task into a result that another person can inspect and use.", "editorialOverlay": { "copy_fields": null, "copy_sha256": null, "copy_source": null, "html_sha256": "19a2339beb948b87370375f1c103d1a71a226b7483efa3e92f41aa5f61a7709c", "module_sha256": "ee9dc1c50b1e6a8fb7fa062030fcb4646b9aaaeb08be513b723a5a92a7290f06", "raw_model_unchanged": true, "schema": "ailoot-native-model-editorial-v1" }, "locale": "en", "methods": [ { "html": "openai/o3:batchOpenAI o3 is a reasoning model for multi-step work across mathematics, science, code and images. It returns text and supports structured answers and tool use, making it useful for investigating a technical problem and producing an evidence-based explanation or proposed solution.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
o3 is OpenAI’s general-purpose o-series reasoning model for complex tasks. The manufacturer highlights mathematics, science, coding, visual reasoning, technical writing and instruction following. It accepts text and images and produces text, so a task can combine a written problem, code and a diagram rather than treating each source as an isolated question. The intended result is a considered answer to a multi-step problem, with the information required to assess that answer.
For a mathematical or scientific question, provide the complete problem, definitions, known quantities, units and constraints. State whether you need a derivation, a numerical result, a comparison of hypotheses or an explanation for a particular audience. An illustrative output brief is: identify the relevant assumptions, present the solution with its units, and explain which supplied facts support it. Ask for a useful explanation in the final answer; OpenAI’s internal reasoning tokens are not exposed as the model’s raw working trace.
For software work, supply the relevant code, the expected behavior and the observed failure, including the exact error or input that reveals it. Ask o3 to connect the symptoms to a plausible cause and propose a focused change with an explanation. A visual task can include a diagram, screenshot or plotted result: identify the question and the labels or region that matter, retain a legible scale, and request a conclusion tied to what is actually visible. These preparation steps give the model a defined problem and a concrete output instead of an unrestricted request to inspect everything.
OpenAI lists a 200,000-token context window, a maximum output of 100,000 tokens and a knowledge cutoff of June 1, 2024. Internal reasoning uses context and output capacity alongside the visible answer. The maximum output is therefore not a promise of 100,000 visible answer tokens, and the context is not an additional independent output allowance. Leave room after the input material for analysis and the requested result. The producer’s reasoning guide describes incomplete responses when a context or output limit is reached; this can happen before a visible answer appears.
The exact o3 specification documents Chat Completions, Responses and Batch interfaces, together with streaming, Structured Outputs, function calling, file uploads, image input, file search and prompt caching. Its documented tool set includes web and file search, code interpretation, image generation and MCP connections. A search result or calculation is additional evidence for the task, while a function call requests an operation from the surrounding application. Keep the returned evidence associated with the question it answers, and distinguish a proposed action from a confirmed result. The model’s own output remains text even when an image-generation tool creates a separate artifact.
OpenAI’s reasoning guidance favors simple, direct instructions with clear boundaries between instructions and source material. State the goal, constraints and required answer format instead of prescribing the model’s private thought process. Use examples when they clarify a difficult format or decision rule, and keep those examples consistent with the actual requirements. For a long technical document, label sections and ask for the passages or relationships relevant to a specific question; for code review, specify the behavior and risk you want investigated.
A useful o3 deliverable can be a worked technical explanation, an analysis of a diagram, a documented code correction or a comparison grounded in the supplied material. Make unsupported assumptions and missing information explicit, and check decisive calculations or code behavior against the original problem. Its knowledge cutoff means that recent facts need supplied material or an appropriate information tool. Clear evidence, an explicit completion criterion and sufficient output space help turn a broad reasoning task into a result that another person can inspect and 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/o3: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/o3:batch, openai/o3 | 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/o3: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/o3:batch', 'openai/o3']: raise ValueError("body.model must be an exact documented caller or wire ID")\n return {"endpoint":"/v1/chat/completions", "model":"openai/o3: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/o3:batch
Use the method paths shown above.