GPT-5.1 is OpenAI’s general-purpose model for coding and agentic tasks with configurable reasoning. It accepts text and images and produces text. The developer release describes adaptive reasoning: the model varies the work it spends on analysis with the task’s complexity. The base GPT-5.1 edition is distinct from the Chat and Codex editions in the same family.
For coding, provide the intended behavior, relevant files and the constraints a proposed change must preserve. An illustrative request is to inspect how a component handles an input, identify the dependency that needs attention and develop a targeted change with a clear explanation. For a document task, supply the original material, the reader and the required facts, then ask for an edited passage or an analysis with specified sections.
The producer documents a no-reasoning setting for tasks that do not require deeper analysis, alongside low, medium and high reasoning effort; none is the default. Its release recommends choosing a reasoning level according to the task’s complexity. This gives an application a way to distinguish a straightforward extraction or brief reply from a task that involves several dependent conditions, without changing the base model’s identity.
OpenAI’s model card specifies a 400,000-token context window, a maximum output of 128,000 tokens and a knowledge cutoff of September 30, 2024. Reasoning occupies space along with the input and final answer. Organize a long request around the useful evidence and success criteria. If an image informs the task, identify the relevant diagram, interface state or detail and include the written context needed to interpret it.
The model card lists Responses, Chat Completions and Batch interfaces, streaming, Structured Outputs, function calling, file and image inputs, web and file search and prompt caching. Current caching documentation includes optional extended retention of up to 24 hours for this model. The producer also lists image generation, code interpretation, patch operations and MCP connections. The developer release documents a patch tool that proposes structured file changes and a shell tool that proposes commands. The application applies or executes these operations and returns their actual results, supporting a continued editing and inspection workflow.
The release also emphasizes coding steerability and useful updates during sequences of tool calls. Define what the task should complete, which actions are available and the evidence that would verify the result. For a change, request the affected behavior, significant edits and checks actually performed. For a document, request a concrete version tied to the supplied facts. A useful final artifact makes the result and its basis explicit while keeping proposed actions distinct from completed operations.
", "aboutText": "GPT-5.1 is OpenAI’s general-purpose model for coding and agentic tasks with configurable reasoning. It accepts text and images and produces text. The developer release describes adaptive reasoning: the model varies the work it spends on analysis with the task’s complexity. The base GPT-5.1 edition is distinct from the Chat and Codex editions in the same family.\n\nFor coding, provide the intended behavior, relevant files and the constraints a proposed change must preserve. An illustrative request is to inspect how a component handles an input, identify the dependency that needs attention and develop a targeted change with a clear explanation. For a document task, supply the original material, the reader and the required facts, then ask for an edited passage or an analysis with specified sections.\n\nThe producer documents a no-reasoning setting for tasks that do not require deeper analysis, alongside low, medium and high reasoning effort; none is the default. Its release recommends choosing a reasoning level according to the task’s complexity. This gives an application a way to distinguish a straightforward extraction or brief reply from a task that involves several dependent conditions, without changing the base model’s identity.\n\nOpenAI’s model card specifies a 400,000-token context window, a maximum output of 128,000 tokens and a knowledge cutoff of September 30, 2024. Reasoning occupies space along with the input and final answer. Organize a long request around the useful evidence and success criteria. If an image informs the task, identify the relevant diagram, interface state or detail and include the written context needed to interpret it.\n\nThe model card lists Responses, Chat Completions and Batch interfaces, streaming, Structured Outputs, function calling, file and image inputs, web and file search and prompt caching. Current caching documentation includes optional extended retention of up to 24 hours for this model. The producer also lists image generation, code interpretation, patch operations and MCP connections. The developer release documents a patch tool that proposes structured file changes and a shell tool that proposes commands. The application applies or executes these operations and returns their actual results, supporting a continued editing and inspection workflow.\n\nThe release also emphasizes coding steerability and useful updates during sequences of tool calls. Define what the task should complete, which actions are available and the evidence that would verify the result. For a change, request the affected behavior, significant edits and checks actually performed. For a document, request a concrete version tied to the supplied facts. A useful final artifact makes the result and its basis explicit while keeping proposed actions distinct from completed operations.", "editorialOverlay": { "copy_fields": { "practical_html": "Technical example in English
Await asynchronous file writes\n\nFix this JavaScript function so it resolves only after all writes finish and rejects if any write fails: async function saveAll(items) { items.forEach(async item => { await writeItem(item); }); }. Ordering between writes is not required. Return a focused correction and tests for completion timing, empty input and one rejected write.A corrected async function and test scenarios for awaited completion, empty input and error propagation.Check that the returned promise remains pending until writes finish and a rejected write is observable by the caller.gpt-5.1GPT-5.1 combines coding and agentic work with adaptive reasoning. It accepts text and images and can develop a code change, document or tool-connected result, with a no-reasoning setting for tasks that do not need deeper analysis.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
GPT-5.1 is OpenAI’s general-purpose model for coding and agentic tasks with configurable reasoning. It accepts text and images and produces text. The developer release describes adaptive reasoning: the model varies the work it spends on analysis with the task’s complexity. The base GPT-5.1 edition is distinct from the Chat and Codex editions in the same family.
For coding, provide the intended behavior, relevant files and the constraints a proposed change must preserve. An illustrative request is to inspect how a component handles an input, identify the dependency that needs attention and develop a targeted change with a clear explanation. For a document task, supply the original material, the reader and the required facts, then ask for an edited passage or an analysis with specified sections.
The producer documents a no-reasoning setting for tasks that do not require deeper analysis, alongside low, medium and high reasoning effort; none is the default. Its release recommends choosing a reasoning level according to the task’s complexity. This gives an application a way to distinguish a straightforward extraction or brief reply from a task that involves several dependent conditions, without changing the base model’s identity.
OpenAI’s model card specifies a 400,000-token context window, a maximum output of 128,000 tokens and a knowledge cutoff of September 30, 2024. Reasoning occupies space along with the input and final answer. Organize a long request around the useful evidence and success criteria. If an image informs the task, identify the relevant diagram, interface state or detail and include the written context needed to interpret it.
The model card lists Responses, Chat Completions and Batch interfaces, streaming, Structured Outputs, function calling, file and image inputs, web and file search and prompt caching. Current caching documentation includes optional extended retention of up to 24 hours for this model. The producer also lists image generation, code interpretation, patch operations and MCP connections. The developer release documents a patch tool that proposes structured file changes and a shell tool that proposes commands. The application applies or executes these operations and returns their actual results, supporting a continued editing and inspection workflow.
The release also emphasizes coding steerability and useful updates during sequences of tool calls. Define what the task should complete, which actions are available and the evidence that would verify the result. For a change, request the affected behavior, significant edits and checks actually performed. For a document, request a concrete version tied to the supplied facts. A useful final artifact makes the result and its basis explicit while keeping proposed actions distinct from completed operations.
POST /v1/chat/completionstexttext| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits gpt-5.1 | Default— |
Fieldmessages | Typemessage[] | RequirementRequired | Values & limits Minimum items: 1 | Default— |
Fieldmessages[].role | Typestring | RequirementRequired | Values & limits Model-specific | Default— |
Fieldmessages[].content | Typestring | RequirementOptional | Values & limits Applies when: Text conversation | Default— |
Fieldstream | Typeboolean | RequirementOptional | Values & limits | Defaultfalse |
Text · UTF8 text
messages[].content
| Parameter | Contract |
|---|---|
Parametercontent_type | Contractapplication/json |
Parameterrequired | Contractmodel, messages |
Parametermaterial_types | Contracttext |
Parametermessages | Contractmessage array with role/content shape |
Parameterfile_material | Contractactual UTF-8 document inserted as literal text; no file ID inferred |
Parameteroptional_controls | Contractstream |
Parametercontrol_scope | ContractText conversation; stream is a boolean and defaults to false. |
Parameteroutput_modalities | Contracttext |
Parameterhosted_operations | Contractfalse |
Parametermultimodal_input_binding | Contractnull |
Method aliases: /chat/completions
Lifecycle: synchronous_or_streamed
Send exact model ID with messages.
Context length is not an output cap.
A text file may supply literal text; image/audio/file-object inputs need exact binding.
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 $0.0025 / 1K tokens Output · Output tokens $0.02 / 1K tokens Input · Cache read $0.00025 / 1K tokens Input · Cache write $0 / 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 $0.0025 / 1K tokens Output · Output tokens $0.02 / 1K tokens Input · Cache read $0.00025 / 1K tokens Input · Cache write $0 / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{"model":"gpt-5.1","messages":[{"role":"user","content":"hello"}],"stream":false}')\n\nPath("request.json").write_text(json.dumps(p,ensure_ascii=False))\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/chat/completions \\\n -H "Authorization: Bearer $AILOOT_API_KEY" -H 'Content-Type: application/json' \\\n --data-binary @request.json -D response-headers.txt -o result.json\npython3 - <<'PY_RESULT'\nimport json\nfrom pathlib import Path\nr=json.loads(Path("result.json").read_text())\nfor x in r.get("choices",[]):\n if x.get("message",{}).get("content"): print(x["message"]["content"])\nPY_RESULT\nfrom openai import OpenAI\nimport os\n\nif not os.environ.get("AILOOT_API_KEY", "").strip(): raise SystemExit("Set AILOOT_API_KEY before making a request")\nclient=OpenAI(base_url="https://api.ailoot.ai/v1",api_key=os.environ["AILOOT_API_KEY"],max_retries=0)\nresult=client.chat.completions.create(model="gpt-5.1", messages=[{"role":"user","content":"hello"}], stream=False)\nimport base64, json\nfrom pathlib import Path\n\ndef save_result(data, stem="result"):\n # Keep every field, including usage, IDs, reasoning and tool requests.\n Path(stem + ".json").write_text(json.dumps(data, ensure_ascii=False, indent=2), encoding="utf-8")\n print(json.dumps(data, ensure_ascii=False, indent=2))\n def save_blob(encoded, suffix, label):\n raw = base64.b64decode(encoded, altchars=b"-_", validate=True)\n ext = "png" if raw.startswith(b"\\x89PNG\\r\\n\\x1a\\n") else "jpg" if raw.startswith(b"\\xff\\xd8") else "webp" if raw[:4] == b"RIFF" and raw[8:12] == b"WEBP" else suffix\n file = Path(stem + "-" + label + "." + ext)\n file.write_bytes(raw)\n print("Saved", file)\n for index, block in enumerate(data.get("content") or []):\n print("Content", index, json.dumps(block, ensure_ascii=False))\n for choice in data.get("choices") or []:\n print("Choice", choice.get("index"), json.dumps(choice.get("message", choice.get("delta", {})), ensure_ascii=False))\n for index, item in enumerate(data.get("output") or []):\n print("Output", index, json.dumps(item, ensure_ascii=False))\n if item.get("type") == "image_generation_call" and item.get("result"):\n save_blob(item["result"], item.get("output_format") or "bin", "output-" + str(index))\n for ci, candidate in enumerate(data.get("candidates") or []):\n for pi, part in enumerate((candidate.get("content") or {}).get("parts") or []):\n print("Candidate/part", ci, pi, json.dumps(part, ensure_ascii=False))\n blob = part.get("inlineData") or part.get("inline_data")\n if blob and blob.get("data"):\n mime = blob.get("mimeType") or blob.get("mime_type")\n suffix = {"image/png":"png", "image/jpeg":"jpg", "image/webp":"webp", "audio/wav":"wav", "audio/mpeg":"mp3", "video/mp4":"mp4"}.get(mime, "bin")\n save_blob(blob["data"], suffix, "candidate-" + str(ci) + "-part-" + str(pi))\n\nsave_result(result.to_dict(mode="json"))\njson or SSE
Keep every complete returned response, file and content part.
Keep every choice and its content/tool calls/reasoning. In SSE assemble deltas by choice/tool index, retain the final finish reason and usage.
Keep request/response IDs and headers. No generic text job retrieval URL is supplied.
| Field | Type |
|---|---|
Fieldchoices[].index | Typeinteger |
Fieldchoices[].message.content | Typestring|null |
Fieldchoices[].message.tool_calls | Typetool_call[] |
Fieldchoices[].message.reasoning_content | Typestring |
Fieldchoices[].message.reasoning_details | Typeobject|array |
Fieldchoices[].finish_reason | Typestring|null |
Fieldusage | Typeobject |
| Response | Condition | Next step |
|---|---|---|
Response400 · invalid_request_error | ConditionInvalid required fields, material representation or parameter value | Next stepCorrect the indicated fields before a new creation request. |
Ailoot Ultra gpt-5.1, openai/gpt-5.1
/chat/completions
gpt-5.1GPT-5.1 combines coding and agentic work with adaptive reasoning. It accepts text and images and can develop a code change, document or tool-connected result, with a no-reasoning setting for tasks that do not need deeper analysis.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
GPT-5.1 is OpenAI’s general-purpose model for coding and agentic tasks with configurable reasoning. It accepts text and images and produces text. The developer release describes adaptive reasoning: the model varies the work it spends on analysis with the task’s complexity. The base GPT-5.1 edition is distinct from the Chat and Codex editions in the same family.
For coding, provide the intended behavior, relevant files and the constraints a proposed change must preserve. An illustrative request is to inspect how a component handles an input, identify the dependency that needs attention and develop a targeted change with a clear explanation. For a document task, supply the original material, the reader and the required facts, then ask for an edited passage or an analysis with specified sections.
The producer documents a no-reasoning setting for tasks that do not require deeper analysis, alongside low, medium and high reasoning effort; none is the default. Its release recommends choosing a reasoning level according to the task’s complexity. This gives an application a way to distinguish a straightforward extraction or brief reply from a task that involves several dependent conditions, without changing the base model’s identity.
OpenAI’s model card specifies a 400,000-token context window, a maximum output of 128,000 tokens and a knowledge cutoff of September 30, 2024. Reasoning occupies space along with the input and final answer. Organize a long request around the useful evidence and success criteria. If an image informs the task, identify the relevant diagram, interface state or detail and include the written context needed to interpret it.
The model card lists Responses, Chat Completions and Batch interfaces, streaming, Structured Outputs, function calling, file and image inputs, web and file search and prompt caching. Current caching documentation includes optional extended retention of up to 24 hours for this model. The producer also lists image generation, code interpretation, patch operations and MCP connections. The developer release documents a patch tool that proposes structured file changes and a shell tool that proposes commands. The application applies or executes these operations and returns their actual results, supporting a continued editing and inspection workflow.
The release also emphasizes coding steerability and useful updates during sequences of tool calls. Define what the task should complete, which actions are available and the evidence that would verify the result. For a change, request the affected behavior, significant edits and checks actually performed. For a document, request a concrete version tied to the supplied facts. A useful final artifact makes the result and its basis explicit while keeping proposed actions distinct from completed operations.
POST /v1/messagestextjson, sse| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits gpt-5.1 | Default— |
Fieldmessages | Typemessage[] | RequirementRequired | Values & limits | Default— |
Fieldmessages[].role | Typestring | RequirementRequired | Values & limits user, assistant | Default— |
Fieldmessages[].content | Typestring | RequirementRequired | Values & limits | Default— |
Fieldmax_tokens | Typeinteger | RequirementRequired | Values & limits | Default— |
Fieldstream | Typeboolean | RequirementOptional | Values & limits | Defaultfalse |
Text · UTF-8 text
messages[].content string
| Parameter | Contract |
|---|---|
Parametercontent_type | Contractapplication/json |
Parameterrequired | Contractmodel, messages, max_tokens |
Parameteroptional_controls | Contractstream |
Parametermaterial_types | Contracttext |
Parametertext_file_material | ContractRead the actual UTF-8 document and put its text in the indicated input or message content string. |
Lifecycle: synchronous_or_streamed
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 $0.0025 / 1K tokens Output · Output tokens $0.02 / 1K tokens Input · Cache read $0.00025 / 1K tokens Input · Cache write $0 / 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 $0.0025 / 1K tokens Output · Output tokens $0.02 / 1K tokens Input · Cache read $0.00025 / 1K tokens Input · Cache write $0 / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
{\n "model": "gpt-5.1",\n "max_tokens": 128,\n "messages": [\n {\n "role": "user",\n "content": "Summarize the incident report."\n }\n ],\n "stream": false\n}set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\n: "${AILOOT_TEXT_FILE:?Set your actual UTF-8 document}"\nexport AILOOT_TEXT_FILE\npython3 - <<'PY_REQUEST'\nimport json, os\nfrom pathlib import Path\n\nsource_file = Path(os.environ['AILOOT_TEXT_FILE'])\nif not source_file.is_file():\n raise SystemExit('Set AILOOT_TEXT_FILE to your actual UTF-8 document')\ntext = source_file.read_text(encoding='utf-8')\nif not text.strip():\n raise SystemExit('Choose a document containing text')\npayload = {'model': 'gpt-5.1', 'max_tokens': 128, 'messages': [{'role': 'user', 'content': 'Summarize the incident report.'}]}\npayload['messages'][0]['content'] = 'Summarize this material and quote supporting source passages.\\n\\n' + text\nPath('request.json').write_text(json.dumps(payload, ensure_ascii=False), encoding='utf-8')\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/messages \\\n -H "x-api-key: $AILOOT_API_KEY" -H 'anthropic-version: 2023-06-01' -H 'Content-Type: application/json' \\\n --data-binary @request.json -D response-headers.txt -o result.json\npython3 - <<'PY_RESULT'\nimport json\nfrom pathlib import Path\n\nresult = json.loads(Path('result.json').read_text(encoding='utf-8'))\nprint(json.dumps(result, ensure_ascii=False, indent=2))\nPY_RESULT\nimport json, os\nfrom pathlib import Path\n\nsource_file = Path(os.environ['AILOOT_TEXT_FILE'])\nif not source_file.is_file():\n raise SystemExit('Set AILOOT_TEXT_FILE to your actual UTF-8 document')\ntext = source_file.read_text(encoding='utf-8')\nif not text.strip():\n raise SystemExit('Choose a document containing text')\npayload = {'model': 'gpt-5.1', 'max_tokens': 128, 'messages': [{'role': 'user', 'content': 'Summarize the incident report.'}]}\npayload['messages'][0]['content'] = 'Summarize this material and quote supporting source passages.\\n\\n' + text\nfrom urllib.request import Request, urlopen\nfrom urllib.error import HTTPError\n\napi_key = os.environ['AILOOT_API_KEY']\nif not api_key.strip():\n raise SystemExit('Set AILOOT_API_KEY before making a request')\nheaders = {'x-api-key': 'PLACEHOLDER_KEY', 'anthropic-version': '2023-06-01', 'Content-Type': 'application/json'}\nheaders['x-api-key'] = api_key\nrequest = Request('https://api.ailoot.ai/v1/messages', data=json.dumps(payload, ensure_ascii=False).encode('utf-8'), headers=headers, method='POST')\ntry:\n with urlopen(request, timeout=120) as response:\n raw = response.read()\n Path('response-headers.txt').write_text(str(response.headers), encoding='utf-8')\nexcept HTTPError as error:\n Path('result.json').write_bytes(error.read())\n Path('response-headers.txt').write_text(str(error.headers), encoding='utf-8')\n raise\nPath('result.json').write_bytes(raw)\nimport json\nfrom pathlib import Path\n\nresult = json.loads(Path('result.json').read_text(encoding='utf-8'))\nprint(json.dumps(result, ensure_ascii=False, indent=2))\nJSON or SSE
Keep every complete returned response, file and content part.
Keep all content blocks, thinking signatures and actual tool-use IDs. Assemble all SSE block deltas; a first text block is not the complete output.
Keep request/response IDs and headers. No generic text job retrieval URL is supplied.
| Field | Type |
|---|---|
Fieldid | Typestring |
Fieldmodel | Typestring |
Fieldcontent[] | Typetext|thinking|tool_use|other content block |
Fieldstop_reason | Typestring|null |
Fieldusage | Typeobject |
| Response | Condition | Next step |
|---|---|---|
Response400 · invalid_request_error | ConditionInvalid required fields, material representation or parameter value | Next stepCorrect the indicated fields before a new creation request. |
Ailoot Ultra gpt-5.1, openai/gpt-5.1
/chat/completions
openai/gpt-5.1GPT-5.1 combines coding and agentic work with adaptive reasoning. It accepts text and images and can develop a code change, document or tool-connected result, with a no-reasoning setting for tasks that do not need deeper analysis.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
GPT-5.1 is OpenAI’s general-purpose model for coding and agentic tasks with configurable reasoning. It accepts text and images and produces text. The developer release describes adaptive reasoning: the model varies the work it spends on analysis with the task’s complexity. The base GPT-5.1 edition is distinct from the Chat and Codex editions in the same family.
For coding, provide the intended behavior, relevant files and the constraints a proposed change must preserve. An illustrative request is to inspect how a component handles an input, identify the dependency that needs attention and develop a targeted change with a clear explanation. For a document task, supply the original material, the reader and the required facts, then ask for an edited passage or an analysis with specified sections.
The producer documents a no-reasoning setting for tasks that do not require deeper analysis, alongside low, medium and high reasoning effort; none is the default. Its release recommends choosing a reasoning level according to the task’s complexity. This gives an application a way to distinguish a straightforward extraction or brief reply from a task that involves several dependent conditions, without changing the base model’s identity.
OpenAI’s model card specifies a 400,000-token context window, a maximum output of 128,000 tokens and a knowledge cutoff of September 30, 2024. Reasoning occupies space along with the input and final answer. Organize a long request around the useful evidence and success criteria. If an image informs the task, identify the relevant diagram, interface state or detail and include the written context needed to interpret it.
The model card lists Responses, Chat Completions and Batch interfaces, streaming, Structured Outputs, function calling, file and image inputs, web and file search and prompt caching. Current caching documentation includes optional extended retention of up to 24 hours for this model. The producer also lists image generation, code interpretation, patch operations and MCP connections. The developer release documents a patch tool that proposes structured file changes and a shell tool that proposes commands. The application applies or executes these operations and returns their actual results, supporting a continued editing and inspection workflow.
The release also emphasizes coding steerability and useful updates during sequences of tool calls. Define what the task should complete, which actions are available and the evidence that would verify the result. For a change, request the affected behavior, significant edits and checks actually performed. For a document, request a concrete version tied to the supplied facts. A useful final artifact makes the result and its basis explicit while keeping proposed actions distinct from completed operations.
POST /v1/chat/completionstexttext| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits openai/gpt-5.1 | Default— |
Fieldmessages | Typemessage[] | RequirementRequired | Values & limits Minimum items: 1 | Default— |
Fieldmessages[].role | Typestring | RequirementRequired | Values & limits Examples: system, user, assistant, tool, function Conditions: These roles are explicitly represented by Source compatibility types; the exact model role set remains model-specific. Model-specific | Default— |
Fieldmessages[].content | Typestring|content_part[]|null | RequirementOptional | Values & limits | Default— |
Fieldmessages[].tool_calls | Typetool_call[] | RequirementOptional | Values & limits | Default— |
Fieldmessages[].tool_call_id | Typestring | RequirementOptional | Values & limits Applies when: role=tool; use actual returned ID | Default— |
Fieldmessages[].name | Typestring | RequirementOptional | Values & limits | Default— |
Fieldmessages[].reasoning_content | Typestring | RequirementOptional | Values & limits Applies when: Round-trip actual returned reasoning where this model uses it | Default— |
Fieldmessages[].reasoning_details | Typeobject|array | RequirementOptional | Values & limits Model-specific | Default— |
Fieldmax_tokens | Typeinteger | RequirementOptional | Values & limits Minimum: 1 Model-specific | Default— |
Fieldmax_completion_tokens | Typeinteger | RequirementOptional | Values & limits Minimum: 1 Model-specific | Default— |
Fieldstream | Typeboolean | RequirementOptional | Values & limits | Defaultfalse |
Fieldstream_options.include_usage | Typeboolean | RequirementOptional | Values & limits Model-specific | Default— |
Fieldtemperature | Typenumber | RequirementOptional | Values & limits Model-specific | Default— |
Fieldtop_p | Typenumber | RequirementOptional | Values & limits Model-specific | Default— |
Fieldstop | Typestring|string[] | RequirementOptional | Values & limits Model-specific | Default— |
Fieldtools | Typefunction_tool[] | RequirementOptional | Values & limits Model-specific | Default— |
Fieldtools[].type | Typestring | RequirementOptional | Values & limits function Model-specific | Default— |
Fieldtools[].function.name | Typestring | RequirementRequired | Values & limits Applies when: function tool supplied | Default— |
Fieldtools[].function.description | Typestring | RequirementOptional | Values & limits | Default— |
Fieldtools[].function.parameters | TypeJSON_schema | RequirementOptional | Values & limits | Default— |
Fieldtool_choice | Typestring|object | RequirementOptional | Values & limits Model-specific | Default— |
Fieldparallel_tool_calls | Typeboolean | RequirementOptional | Values & limits Model-specific | Default— |
Fieldresponse_format | Typeobject | RequirementOptional | Values & limits Model-specific | Default— |
Fieldreasoning_effort | Typestring | RequirementOptional | Values & limits Model-specific | Default— |
Fieldreasoning | Typeobject | RequirementOptional | Values & limits Model-specific | Default— |
Fieldservice_tier | Typestring | RequirementOptional | Values & limits Model-specific | Default— |
Fieldinstructions | Typestring | RequirementOptional | Values & limits Compatibility field | Default— |
Fieldfunctions | Typefunction[] | RequirementOptional | Values & limits Model-specific | Default— |
Fieldfunction_call | Typestring|object | RequirementOptional | Values & limits Model-specific | Default— |
Fieldprompt_cache_options | Typeobject | RequirementOptional | Values & limits Model-specific | Default— |
Fieldmodalities | Typestring[] | RequirementOptional | Values & limits text | Default— |
Fieldprovider | Typeobject | RequirementOptional | Values & limits Conditions: provider.options is rejected on this Ultra text route | Default— |
Fieldcache_control.ttl | Typestring | RequirementOptional | Values & limits Conditions: 1h is rejected in actual prompt-cache locations | Default— |
Text · UTF8 text
messages[].content
| Parameter | Contract |
|---|---|
Parametercontent_type | Contractapplication/json |
Parameterrequired | Contractmodel, messages |
Parametermaterial_types | Contracttext |
Parametermessages | Contractmessage array with role/content shape |
Parameterfile_material | Contractactual UTF-8 document inserted as literal text; no file ID inferred |
Parameteroptional_controls | Contractstream, temperature, top_p, stop, max_tokens, max_completion_tokens, reasoning, response_format, tools, tool_choice |
Parametercontrol_scope | Contractmodel-specific; user function tools differ from hosted tools |
Parameteroutput_modalities | Contracttext |
Parameterhosted_operations | Contractfalse |
Parametermultimodal_input_binding | Contractnull |
Lifecycle: synchronous_or_streamed
Send exact model ID with messages.
Context length is not an output cap.
A text file may supply literal text; image/audio/file-object inputs need exact binding.
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 $0.0025 / 1K tokens Output · Output tokens $0.02 / 1K tokens Input · Cache read $0.00025 / 1K tokens Input · Cache write $0 / 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 $0.0025 / 1K tokens Output · Output tokens $0.02 / 1K tokens Input · Cache read $0.00025 / 1K tokens Input · Cache write $0 / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{"model": "openai/gpt-5.1", "messages": [{"role": "user", "content": "Explain the difference between an observation and a hypothesis in three sentences."}]}')\n\nPath("request.json").write_text(json.dumps(p,ensure_ascii=False))\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/chat/completions \\\n -H "Authorization: Bearer $AILOOT_API_KEY" -H 'Content-Type: application/json' \\\n --data-binary @request.json -D response-headers.txt -o result.json\npython3 - <<'PY_RESULT'\nimport json\nfrom pathlib import Path\nr=json.loads(Path("result.json").read_text())\nfor x in r.get("choices",[]):\n if x.get("message",{}).get("content"): print(x["message"]["content"])\nPY_RESULT\nfrom openai import OpenAI\nimport os\n\nif not os.environ.get("AILOOT_API_KEY", "").strip(): raise SystemExit("Set AILOOT_API_KEY before making a request")\nclient=OpenAI(base_url="https://api.ailoot.ai/v1",api_key=os.environ["AILOOT_API_KEY"],max_retries=0)\nresult=client.chat.completions.create(model='openai/gpt-5.1',messages=[{"role":"user","content":"Explain observations and hypotheses in three sentences."}])\nimport base64, json\nfrom pathlib import Path\n\ndef save_result(data, stem="result"):\n # Keep every field, including usage, IDs, reasoning and tool requests.\n Path(stem + ".json").write_text(json.dumps(data, ensure_ascii=False, indent=2), encoding="utf-8")\n print(json.dumps(data, ensure_ascii=False, indent=2))\n def save_blob(encoded, suffix, label):\n raw = base64.b64decode(encoded, altchars=b"-_", validate=True)\n ext = "png" if raw.startswith(b"\\x89PNG\\r\\n\\x1a\\n") else "jpg" if raw.startswith(b"\\xff\\xd8") else "webp" if raw[:4] == b"RIFF" and raw[8:12] == b"WEBP" else suffix\n file = Path(stem + "-" + label + "." + ext)\n file.write_bytes(raw)\n print("Saved", file)\n for index, block in enumerate(data.get("content") or []):\n print("Content", index, json.dumps(block, ensure_ascii=False))\n for choice in data.get("choices") or []:\n print("Choice", choice.get("index"), json.dumps(choice.get("message", choice.get("delta", {})), ensure_ascii=False))\n for index, item in enumerate(data.get("output") or []):\n print("Output", index, json.dumps(item, ensure_ascii=False))\n if item.get("type") == "image_generation_call" and item.get("result"):\n save_blob(item["result"], item.get("output_format") or "bin", "output-" + str(index))\n for ci, candidate in enumerate(data.get("candidates") or []):\n for pi, part in enumerate((candidate.get("content") or {}).get("parts") or []):\n print("Candidate/part", ci, pi, json.dumps(part, ensure_ascii=False))\n blob = part.get("inlineData") or part.get("inline_data")\n if blob and blob.get("data"):\n mime = blob.get("mimeType") or blob.get("mime_type")\n suffix = {"image/png":"png", "image/jpeg":"jpg", "image/webp":"webp", "audio/wav":"wav", "audio/mpeg":"mp3", "video/mp4":"mp4"}.get(mime, "bin")\n save_blob(blob["data"], suffix, "candidate-" + str(ci) + "-part-" + str(pi))\n\nsave_result(result.to_dict(mode="json"))\nfrom openai import OpenAI\nimport os\nfrom pathlib import Path\n\nif not os.environ.get("AILOOT_API_KEY", "").strip(): raise SystemExit("Set AILOOT_API_KEY before making a request")\nclient=OpenAI(base_url="https://api.ailoot.ai/v1",api_key=os.environ["AILOOT_API_KEY"],max_retries=0)\nimport base64, json\nfrom pathlib import Path\n\ndef save_result(data, stem="result"):\n # Keep every field, including usage, IDs, reasoning and tool requests.\n Path(stem + ".json").write_text(json.dumps(data, ensure_ascii=False, indent=2), encoding="utf-8")\n print(json.dumps(data, ensure_ascii=False, indent=2))\n def save_blob(encoded, suffix, label):\n raw = base64.b64decode(encoded, altchars=b"-_", validate=True)\n ext = "png" if raw.startswith(b"\\x89PNG\\r\\n\\x1a\\n") else "jpg" if raw.startswith(b"\\xff\\xd8") else "webp" if raw[:4] == b"RIFF" and raw[8:12] == b"WEBP" else suffix\n file = Path(stem + "-" + label + "." + ext)\n file.write_bytes(raw)\n print("Saved", file)\n for index, block in enumerate(data.get("content") or []):\n print("Content", index, json.dumps(block, ensure_ascii=False))\n for choice in data.get("choices") or []:\n print("Choice", choice.get("index"), json.dumps(choice.get("message", choice.get("delta", {})), ensure_ascii=False))\n for index, item in enumerate(data.get("output") or []):\n print("Output", index, json.dumps(item, ensure_ascii=False))\n if item.get("type") == "image_generation_call" and item.get("result"):\n save_blob(item["result"], item.get("output_format") or "bin", "output-" + str(index))\n for ci, candidate in enumerate(data.get("candidates") or []):\n for pi, part in enumerate((candidate.get("content") or {}).get("parts") or []):\n print("Candidate/part", ci, pi, json.dumps(part, ensure_ascii=False))\n blob = part.get("inlineData") or part.get("inline_data")\n if blob and blob.get("data"):\n mime = blob.get("mimeType") or blob.get("mime_type")\n suffix = {"image/png":"png", "image/jpeg":"jpg", "image/webp":"webp", "audio/wav":"wav", "audio/mpeg":"mp3", "video/mp4":"mp4"}.get(mime, "bin")\n save_blob(blob["data"], suffix, "candidate-" + str(ci) + "-part-" + str(pi))\n\nwith client.chat.completions.stream(model='openai/gpt-5.1',messages=[{"role":"user","content":"Explain observations and hypotheses in three sentences."}]) as stream:\n with Path("stream-events.jsonl").open("w",encoding="utf-8") as log:\n for event in stream:\n payload=event.to_dict(mode="json")\n log.write(json.dumps(payload,ensure_ascii=False)+"\\n"); log.flush()\n print(json.dumps(payload,ensure_ascii=False))\n final=stream.get_final_completion()\n save_result(final.to_dict(mode="json"),"stream-result")\njson or SSE
Keep every complete returned response, file and content part.
Keep every choice and its content/tool calls/reasoning. In SSE assemble deltas by choice/tool index, retain the final finish reason and usage.
Keep request/response IDs and headers. No generic text job retrieval URL is supplied.
| Field | Type |
|---|---|
Fieldchoices[].index | Typeinteger |
Fieldchoices[].message.content | Typestring|null |
Fieldchoices[].message.tool_calls | Typetool_call[] |
Fieldchoices[].message.reasoning_content | Typestring |
Fieldchoices[].message.reasoning_details | Typeobject|array |
Fieldchoices[].finish_reason | Typestring|null |
Fieldusage | Typeobject |
| Response | Condition | Next step |
|---|---|---|
Response400 · invalid_request_error | ConditionInvalid required fields, material representation or parameter value | Next stepCorrect the indicated fields before a new creation request. |
Ailoot Ultra gpt-5.1, openai/gpt-5.1
/chat/completions
openai/gpt-5.1GPT-5.1 combines coding and agentic work with adaptive reasoning. It accepts text and images and can develop a code change, document or tool-connected result, with a no-reasoning setting for tasks that do not need deeper analysis.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
GPT-5.1 is OpenAI’s general-purpose model for coding and agentic tasks with configurable reasoning. It accepts text and images and produces text. The developer release describes adaptive reasoning: the model varies the work it spends on analysis with the task’s complexity. The base GPT-5.1 edition is distinct from the Chat and Codex editions in the same family.
For coding, provide the intended behavior, relevant files and the constraints a proposed change must preserve. An illustrative request is to inspect how a component handles an input, identify the dependency that needs attention and develop a targeted change with a clear explanation. For a document task, supply the original material, the reader and the required facts, then ask for an edited passage or an analysis with specified sections.
The producer documents a no-reasoning setting for tasks that do not require deeper analysis, alongside low, medium and high reasoning effort; none is the default. Its release recommends choosing a reasoning level according to the task’s complexity. This gives an application a way to distinguish a straightforward extraction or brief reply from a task that involves several dependent conditions, without changing the base model’s identity.
OpenAI’s model card specifies a 400,000-token context window, a maximum output of 128,000 tokens and a knowledge cutoff of September 30, 2024. Reasoning occupies space along with the input and final answer. Organize a long request around the useful evidence and success criteria. If an image informs the task, identify the relevant diagram, interface state or detail and include the written context needed to interpret it.
The model card lists Responses, Chat Completions and Batch interfaces, streaming, Structured Outputs, function calling, file and image inputs, web and file search and prompt caching. Current caching documentation includes optional extended retention of up to 24 hours for this model. The producer also lists image generation, code interpretation, patch operations and MCP connections. The developer release documents a patch tool that proposes structured file changes and a shell tool that proposes commands. The application applies or executes these operations and returns their actual results, supporting a continued editing and inspection workflow.
The release also emphasizes coding steerability and useful updates during sequences of tool calls. Define what the task should complete, which actions are available and the evidence that would verify the result. For a change, request the affected behavior, significant edits and checks actually performed. For a document, request a concrete version tied to the supplied facts. A useful final artifact makes the result and its basis explicit while keeping proposed actions distinct from completed operations.
POST /v1/messagestextjson, sse| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits openai/gpt-5.1 | Default— |
Fieldmessages | Typemessage[] | RequirementRequired | Values & limits | Default— |
Fieldmessages[].role | Typestring | RequirementRequired | Values & limits user, assistant | Default— |
Fieldmessages[].content | Typestring | RequirementRequired | Values & limits | Default— |
Fieldmax_tokens | Typeinteger | RequirementRequired | Values & limits | Default— |
Fieldstream | Typeboolean | RequirementOptional | Values & limits | Defaultfalse |
Text · UTF-8 text
messages[].content string
| Parameter | Contract |
|---|---|
Parametercontent_type | Contractapplication/json |
Parameterrequired | Contractmodel, messages, max_tokens |
Parameteroptional_controls | Contractstream |
Parametermaterial_types | Contracttext |
Parametertext_file_material | ContractRead the actual UTF-8 document and put its text in the indicated input or message content string. |
Lifecycle: synchronous_or_streamed
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 $0.0025 / 1K tokens Output · Output tokens $0.02 / 1K tokens Input · Cache read $0.00025 / 1K tokens Input · Cache write $0 / 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 $0.0025 / 1K tokens Output · Output tokens $0.02 / 1K tokens Input · Cache read $0.00025 / 1K tokens Input · Cache write $0 / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
{\n "model": "openai/gpt-5.1",\n "max_tokens": 128,\n "messages": [\n {\n "role": "user",\n "content": "Summarize the incident report."\n }\n ],\n "stream": false\n}set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\n: "${AILOOT_TEXT_FILE:?Set your actual UTF-8 document}"\nexport AILOOT_TEXT_FILE\npython3 - <<'PY_REQUEST'\nimport json, os\nfrom pathlib import Path\n\nsource_file = Path(os.environ['AILOOT_TEXT_FILE'])\nif not source_file.is_file():\n raise SystemExit('Set AILOOT_TEXT_FILE to your actual UTF-8 document')\ntext = source_file.read_text(encoding='utf-8')\nif not text.strip():\n raise SystemExit('Choose a document containing text')\npayload = {'model': 'openai/gpt-5.1', 'max_tokens': 128, 'messages': [{'role': 'user', 'content': 'Summarize the incident report.'}]}\npayload['messages'][0]['content'] = 'Summarize this material and quote supporting source passages.\\n\\n' + text\nPath('request.json').write_text(json.dumps(payload, ensure_ascii=False), encoding='utf-8')\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/messages \\\n -H "x-api-key: $AILOOT_API_KEY" -H 'anthropic-version: 2023-06-01' -H 'Content-Type: application/json' \\\n --data-binary @request.json -D response-headers.txt -o result.json\npython3 - <<'PY_RESULT'\nimport json\nfrom pathlib import Path\n\nresult = json.loads(Path('result.json').read_text(encoding='utf-8'))\nprint(json.dumps(result, ensure_ascii=False, indent=2))\nPY_RESULT\nimport json, os\nfrom pathlib import Path\n\nsource_file = Path(os.environ['AILOOT_TEXT_FILE'])\nif not source_file.is_file():\n raise SystemExit('Set AILOOT_TEXT_FILE to your actual UTF-8 document')\ntext = source_file.read_text(encoding='utf-8')\nif not text.strip():\n raise SystemExit('Choose a document containing text')\npayload = {'model': 'openai/gpt-5.1', 'max_tokens': 128, 'messages': [{'role': 'user', 'content': 'Summarize the incident report.'}]}\npayload['messages'][0]['content'] = 'Summarize this material and quote supporting source passages.\\n\\n' + text\nfrom urllib.request import Request, urlopen\nfrom urllib.error import HTTPError\n\napi_key = os.environ['AILOOT_API_KEY']\nif not api_key.strip():\n raise SystemExit('Set AILOOT_API_KEY before making a request')\nheaders = {'x-api-key': 'PLACEHOLDER_KEY', 'anthropic-version': '2023-06-01', 'Content-Type': 'application/json'}\nheaders['x-api-key'] = api_key\nrequest = Request('https://api.ailoot.ai/v1/messages', data=json.dumps(payload, ensure_ascii=False).encode('utf-8'), headers=headers, method='POST')\ntry:\n with urlopen(request, timeout=120) as response:\n raw = response.read()\n Path('response-headers.txt').write_text(str(response.headers), encoding='utf-8')\nexcept HTTPError as error:\n Path('result.json').write_bytes(error.read())\n Path('response-headers.txt').write_text(str(error.headers), encoding='utf-8')\n raise\nPath('result.json').write_bytes(raw)\nimport json\nfrom pathlib import Path\n\nresult = json.loads(Path('result.json').read_text(encoding='utf-8'))\nprint(json.dumps(result, ensure_ascii=False, indent=2))\nJSON or SSE
Keep every complete returned response, file and content part.
Keep all content blocks, thinking signatures and actual tool-use IDs. Assemble all SSE block deltas; a first text block is not the complete output.
Keep request/response IDs and headers. No generic text job retrieval URL is supplied.
| Field | Type |
|---|---|
Fieldid | Typestring |
Fieldmodel | Typestring |
Fieldcontent[] | Typetext|thinking|tool_use|other content block |
Fieldstop_reason | Typestring|null |
Fieldusage | Typeobject |
| Response | Condition | Next step |
|---|---|---|
Response400 · invalid_request_error | ConditionInvalid required fields, material representation or parameter value | Next stepCorrect the indicated fields before a new creation request. |
Ailoot Ultra gpt-5.1, openai/gpt-5.1
/chat/completions
Technical example in English
Await asynchronous file writes\n\nFix this JavaScript function so it resolves only after all writes finish and rejects if any write fails: async function saveAll(items) { items.forEach(async item => { await writeItem(item); }); }. Ordering between writes is not required. Return a focused correction and tests for completion timing, empty input and one rejected write.A corrected async function and test scenarios for awaited completion, empty input and error propagation.Check that the returned promise remains pending until writes finish and a rejected write is observable by the caller.