Built for agent workflows
Grok 4.1 Fast (Reasoning) is the reasoning configuration of xAI’s Grok 4.1 Fast family. Its emphasis is completing a task through a sequence of decisions and tool interactions, particularly in support and research. The family’s announced context window is 2 million tokens, making it relevant when instructions, reference material and the results of earlier actions need to remain together. Reasoning and Non-Reasoning are separate named configurations with different response approaches.
Training for real tasks
The model’s development uses reinforcement learning in simulated environments containing varied tools and task domains. The practical purpose is to select an appropriate action from its description, supply the required arguments and continue from the information returned. A support application might ask it to identify a customer request, retrieve the relevant service record, compare the request with supplied rules and prepare the next step. Give the model actual policies and records rather than asking it to invent missing operating rules.
Tool calling and long conversations
Long-horizon, multi-turn training addresses a specific agent problem: maintaining the task as the conversation and tool history grow. Prepare concise tool descriptions, clear argument fields and a statement of what counts as completion. Keep returned observations attached to the action that produced them. For example, an agent investigating a service issue can compare a log excerpt with a known troubleshooting procedure and select the next diagnostic function. The application executes the selected function; the model interprets its result and updates the response.
Retrieval, computation and connected tools
The manufacturer’s tool-enabled workflows combine web retrieval, search over uploaded documents, code execution and connections to external tools through MCP. Each tool supplies information or performs an operation beyond text generation. A document-research task can use retrieved passages to compare product requirements; a computation task can use a code result to explain a numerical trend. Those capabilities depend on the tools actually connected to the process. Describe the retrieval source, allowed action and expected result so the model can connect its reasoning to the available evidence.
Research output and factuality
The announced research use focuses on finding information, combining evidence across steps and delivering an answer to a defined question. A useful illustrative brief is to compare several supplied technical proposals, identify conflicting requirements and return a recommendation with the supporting passages and open questions. Specify the comparison criteria and the date or scope of relevant information. The manufacturer’s factuality work addresses unsupported claims, but the result still benefits from checking which conclusions are supported by the retrieved material.
Mode and output preparation
Choose this named Reasoning configuration when the task calls for analysis before the final response, and define the final artifact: a support reply, evidence table, diagnostic plan or research explanation. The model outputs text; external actions belong to the attached application tools. The announced context capacity describes the original named model’s input-and-history scope, not the size of a single answer or a promise that every integration accepts that amount. Preserve the exact model identity and the tool history rather than substituting another Grok family on the basis of a similar name.
", "aboutText": "Built for agent workflows\n\nGrok 4.1 Fast (Reasoning) is the reasoning configuration of xAI’s Grok 4.1 Fast family. Its emphasis is completing a task through a sequence of decisions and tool interactions, particularly in support and research. The family’s announced context window is 2 million tokens, making it relevant when instructions, reference material and the results of earlier actions need to remain together. Reasoning and Non-Reasoning are separate named configurations with different response approaches.\n\nTraining for real tasks\n\nThe model’s development uses reinforcement learning in simulated environments containing varied tools and task domains. The practical purpose is to select an appropriate action from its description, supply the required arguments and continue from the information returned. A support application might ask it to identify a customer request, retrieve the relevant service record, compare the request with supplied rules and prepare the next step. Give the model actual policies and records rather than asking it to invent missing operating rules.\n\nTool calling and long conversations\n\nLong-horizon, multi-turn training addresses a specific agent problem: maintaining the task as the conversation and tool history grow. Prepare concise tool descriptions, clear argument fields and a statement of what counts as completion. Keep returned observations attached to the action that produced them. For example, an agent investigating a service issue can compare a log excerpt with a known troubleshooting procedure and select the next diagnostic function. The application executes the selected function; the model interprets its result and updates the response.\n\nRetrieval, computation and connected tools\n\nThe manufacturer’s tool-enabled workflows combine web retrieval, search over uploaded documents, code execution and connections to external tools through MCP. Each tool supplies information or performs an operation beyond text generation. A document-research task can use retrieved passages to compare product requirements; a computation task can use a code result to explain a numerical trend. Those capabilities depend on the tools actually connected to the process. Describe the retrieval source, allowed action and expected result so the model can connect its reasoning to the available evidence.\n\nResearch output and factuality\n\nThe announced research use focuses on finding information, combining evidence across steps and delivering an answer to a defined question. A useful illustrative brief is to compare several supplied technical proposals, identify conflicting requirements and return a recommendation with the supporting passages and open questions. Specify the comparison criteria and the date or scope of relevant information. The manufacturer’s factuality work addresses unsupported claims, but the result still benefits from checking which conclusions are supported by the retrieved material.\n\nMode and output preparation\n\nChoose this named Reasoning configuration when the task calls for analysis before the final response, and define the final artifact: a support reply, evidence table, diagnostic plan or research explanation. The model outputs text; external actions belong to the attached application tools. The announced context capacity describes the original named model’s input-and-history scope, not the size of a single answer or a promise that every integration accepts that amount. Preserve the exact model identity and the tool history rather than substituting another Grok family on the basis of a similar name.", "editorialOverlay": { "copy_fields": { "practical_html": "Technical example in English
Choose the next support diagnostic\n\nUse this troubleshooting procedure: if the service process is stopped, restart is the next diagnostic; if running, inspect its last error. Returned observation: "process running; port unreachable; error log not yet read." Identify the next evidence to request and draft a support update. Do not claim that any diagnostic command has run.A proposed next diagnostic with its reason and a brief support update separating known facts from pending evidence.Verify the running-process branch is used and an unread error log does not become a known cause.grok-4-1-fast-reasoningGrok 4.1 Fast (Reasoning) is xAI’s tool-focused model for multi-step support, retrieval and research workflows. Its announced 2-million-token context helps connect instructions, a long conversation and successive tool results while reasoning through the task.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
Built for agent workflows
Grok 4.1 Fast (Reasoning) is the reasoning configuration of xAI’s Grok 4.1 Fast family. Its emphasis is completing a task through a sequence of decisions and tool interactions, particularly in support and research. The family’s announced context window is 2 million tokens, making it relevant when instructions, reference material and the results of earlier actions need to remain together. Reasoning and Non-Reasoning are separate named configurations with different response approaches.
Training for real tasks
The model’s development uses reinforcement learning in simulated environments containing varied tools and task domains. The practical purpose is to select an appropriate action from its description, supply the required arguments and continue from the information returned. A support application might ask it to identify a customer request, retrieve the relevant service record, compare the request with supplied rules and prepare the next step. Give the model actual policies and records rather than asking it to invent missing operating rules.
Tool calling and long conversations
Long-horizon, multi-turn training addresses a specific agent problem: maintaining the task as the conversation and tool history grow. Prepare concise tool descriptions, clear argument fields and a statement of what counts as completion. Keep returned observations attached to the action that produced them. For example, an agent investigating a service issue can compare a log excerpt with a known troubleshooting procedure and select the next diagnostic function. The application executes the selected function; the model interprets its result and updates the response.
Retrieval, computation and connected tools
The manufacturer’s tool-enabled workflows combine web retrieval, search over uploaded documents, code execution and connections to external tools through MCP. Each tool supplies information or performs an operation beyond text generation. A document-research task can use retrieved passages to compare product requirements; a computation task can use a code result to explain a numerical trend. Those capabilities depend on the tools actually connected to the process. Describe the retrieval source, allowed action and expected result so the model can connect its reasoning to the available evidence.
Research output and factuality
The announced research use focuses on finding information, combining evidence across steps and delivering an answer to a defined question. A useful illustrative brief is to compare several supplied technical proposals, identify conflicting requirements and return a recommendation with the supporting passages and open questions. Specify the comparison criteria and the date or scope of relevant information. The manufacturer’s factuality work addresses unsupported claims, but the result still benefits from checking which conclusions are supported by the retrieved material.
Mode and output preparation
Choose this named Reasoning configuration when the task calls for analysis before the final response, and define the final artifact: a support reply, evidence table, diagnostic plan or research explanation. The model outputs text; external actions belong to the attached application tools. The announced context capacity describes the original named model’s input-and-history scope, not the size of a single answer or a promise that every integration accepts that amount. Preserve the exact model identity and the tool history rather than substituting another Grok family on the basis of a similar name.
POST /v1/chat/completionstexttext| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits grok-4-1-fast-reasoning | 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.
| Configuration | Rate schedule | Charge |
|---|---|---|
| ConfigurationBase token rates | Rate scheduleBase rate | Charge Input · Input tokens $0.0004 / 1K tokens Output · Output tokens $0.001 / 1K tokens Input · Cache read $0 / 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": "grok-4-1-fast-reasoning", "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='grok-4-1-fast-reasoning',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='grok-4-1-fast-reasoning',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 grok-4-1-fast-reasoning
Use the method paths shown above.
grok-4-1-fast-reasoningGrok 4.1 Fast (Reasoning) is xAI’s tool-focused model for multi-step support, retrieval and research workflows. Its announced 2-million-token context helps connect instructions, a long conversation and successive tool results while reasoning through the task.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
Built for agent workflows
Grok 4.1 Fast (Reasoning) is the reasoning configuration of xAI’s Grok 4.1 Fast family. Its emphasis is completing a task through a sequence of decisions and tool interactions, particularly in support and research. The family’s announced context window is 2 million tokens, making it relevant when instructions, reference material and the results of earlier actions need to remain together. Reasoning and Non-Reasoning are separate named configurations with different response approaches.
Training for real tasks
The model’s development uses reinforcement learning in simulated environments containing varied tools and task domains. The practical purpose is to select an appropriate action from its description, supply the required arguments and continue from the information returned. A support application might ask it to identify a customer request, retrieve the relevant service record, compare the request with supplied rules and prepare the next step. Give the model actual policies and records rather than asking it to invent missing operating rules.
Tool calling and long conversations
Long-horizon, multi-turn training addresses a specific agent problem: maintaining the task as the conversation and tool history grow. Prepare concise tool descriptions, clear argument fields and a statement of what counts as completion. Keep returned observations attached to the action that produced them. For example, an agent investigating a service issue can compare a log excerpt with a known troubleshooting procedure and select the next diagnostic function. The application executes the selected function; the model interprets its result and updates the response.
Retrieval, computation and connected tools
The manufacturer’s tool-enabled workflows combine web retrieval, search over uploaded documents, code execution and connections to external tools through MCP. Each tool supplies information or performs an operation beyond text generation. A document-research task can use retrieved passages to compare product requirements; a computation task can use a code result to explain a numerical trend. Those capabilities depend on the tools actually connected to the process. Describe the retrieval source, allowed action and expected result so the model can connect its reasoning to the available evidence.
Research output and factuality
The announced research use focuses on finding information, combining evidence across steps and delivering an answer to a defined question. A useful illustrative brief is to compare several supplied technical proposals, identify conflicting requirements and return a recommendation with the supporting passages and open questions. Specify the comparison criteria and the date or scope of relevant information. The manufacturer’s factuality work addresses unsupported claims, but the result still benefits from checking which conclusions are supported by the retrieved material.
Mode and output preparation
Choose this named Reasoning configuration when the task calls for analysis before the final response, and define the final artifact: a support reply, evidence table, diagnostic plan or research explanation. The model outputs text; external actions belong to the attached application tools. The announced context capacity describes the original named model’s input-and-history scope, not the size of a single answer or a promise that every integration accepts that amount. Preserve the exact model identity and the tool history rather than substituting another Grok family on the basis of a similar name.
POST /v1/messagestextjson, sse| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits grok-4-1-fast-reasoning | 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.
| Configuration | Rate schedule | Charge |
|---|---|---|
| ConfigurationBase token rates | Rate scheduleBase rate | Charge Input · Input tokens $0.0004 / 1K tokens Output · Output tokens $0.001 / 1K tokens Input · Cache read $0 / 1K tokens Input · Cache write $0 / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
{\n "model": "grok-4-1-fast-reasoning",\n "max_tokens": 128,\n "messages": [\n {\n "role": "user",\n "content": "Summarize the incident report."\n }\n ],\n "stream": false\n}set -eu\n: "${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': 'grok-4-1-fast-reasoning', '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': 'grok-4-1-fast-reasoning', '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 grok-4-1-fast-reasoning
Use the method paths shown above.
Technical example in English
Choose the next support diagnostic\n\nUse this troubleshooting procedure: if the service process is stopped, restart is the next diagnostic; if running, inspect its last error. Returned observation: "process running; port unreachable; error log not yet read." Identify the next evidence to request and draft a support update. Do not claim that any diagnostic command has run.A proposed next diagnostic with its reason and a brief support update separating known facts from pending evidence.Verify the running-process branch is used and an unread error log does not become a known cause.