Dialogue and general capability
Grok 4.1 develops the conversational side of xAI’s Grok family: natural dialogue, attention to nuanced intent and a more coherent conversational personality. Its development emphasizes helpfulness, style and collaborative interaction alongside core reasoning. The family has two configurations. Thinking works through the task before responding; Non-Thinking gives a direct response without thinking tokens. The general Grok 4.1 name describes this family without selecting either configuration.
Emotional understanding and collaboration
The conversational emphasis is useful when the task depends on how a response should sound as well as what it should say. Supply the situation, the intended reader and the facts that must remain unchanged. For example, ask for a considerate reply to a disappointed customer that acknowledges the concern, explains the next action and preserves the supplied terms. Then identify a specific revision: soften one sentence, make the explanation clearer or replace only the closing paragraph. These are illustrative editorial tasks, not promises that the model knows your company’s policies.
Creative writing
Grok 4.1’s announced capabilities include creative writing and maintaining a recognizable voice during interaction. Prepare a brief with the narrator or characters, the setting, the intended effect and the desired form. A useful task might request a short dialogue in which two characters disagree while each retains a distinct style, followed by a revision that changes the pacing. For business writing, provide the actual product details and ask for contrasting opening paragraphs for different audiences. Separate details that are fixed from those the model may invent within the creative assignment.
Factuality and information-seeking answers
The model’s post-training specifically addresses factual mistakes in information-seeking prompts. That focus does not make a fluent response a verified fact. When drafting from evidence, include the source material and ask the model to distinguish stated facts, interpretation and unanswered questions. A practical output could contain a concise answer, the supporting passage from the material you supplied and a question to resolve any gap. Search-based examples involve a model working with search tools; a text conversation alone should not be treated as a source of fresh retrieved information.
Training, output and edition scope
The training combines pre-training, targeted mid-training, supervised fine-tuning and reinforcement learning, including feedback about useful and appropriate responses. The work described here is text dialogue producing text: revisions, explanations, scenes and answers grounded in provided material. Keep the necessary conversation history and specify exactly which passage a follow-up should change. Evaluation of honesty, misleading premises and prompt injection underlines the importance of keeping supplied evidence distinct from instructions and checking factual conclusions. Grok 4.1 and Grok 4.1 Fast have separate identities; numerical context or output limits from the Fast line are not specifications for this family card.
", "aboutText": "Dialogue and general capability\n\nGrok 4.1 develops the conversational side of xAI’s Grok family: natural dialogue, attention to nuanced intent and a more coherent conversational personality. Its development emphasizes helpfulness, style and collaborative interaction alongside core reasoning. The family has two configurations. Thinking works through the task before responding; Non-Thinking gives a direct response without thinking tokens. The general Grok 4.1 name describes this family without selecting either configuration.\n\nEmotional understanding and collaboration\n\nThe conversational emphasis is useful when the task depends on how a response should sound as well as what it should say. Supply the situation, the intended reader and the facts that must remain unchanged. For example, ask for a considerate reply to a disappointed customer that acknowledges the concern, explains the next action and preserves the supplied terms. Then identify a specific revision: soften one sentence, make the explanation clearer or replace only the closing paragraph. These are illustrative editorial tasks, not promises that the model knows your company’s policies.\n\nCreative writing\n\nGrok 4.1’s announced capabilities include creative writing and maintaining a recognizable voice during interaction. Prepare a brief with the narrator or characters, the setting, the intended effect and the desired form. A useful task might request a short dialogue in which two characters disagree while each retains a distinct style, followed by a revision that changes the pacing. For business writing, provide the actual product details and ask for contrasting opening paragraphs for different audiences. Separate details that are fixed from those the model may invent within the creative assignment.\n\nFactuality and information-seeking answers\n\nThe model’s post-training specifically addresses factual mistakes in information-seeking prompts. That focus does not make a fluent response a verified fact. When drafting from evidence, include the source material and ask the model to distinguish stated facts, interpretation and unanswered questions. A practical output could contain a concise answer, the supporting passage from the material you supplied and a question to resolve any gap. Search-based examples involve a model working with search tools; a text conversation alone should not be treated as a source of fresh retrieved information.\n\nTraining, output and edition scope\n\nThe training combines pre-training, targeted mid-training, supervised fine-tuning and reinforcement learning, including feedback about useful and appropriate responses. The work described here is text dialogue producing text: revisions, explanations, scenes and answers grounded in provided material. Keep the necessary conversation history and specify exactly which passage a follow-up should change. Evaluation of honesty, misleading premises and prompt injection underlines the importance of keeping supplied evidence distinct from instructions and checking factual conclusions. Grok 4.1 and Grok 4.1 Fast have separate identities; numerical context or output limits from the Fast line are not specifications for this family card.", "editorialOverlay": { "copy_fields": { "practical_html": "Technical example in English
Soften a disappointed-customer reply\n\nRevise this reply to a disappointed customer: 'You missed the deadline, so we cannot change it. Read the policy.' Facts to preserve: changes close 48 hours before dispatch; this order dispatches tomorrow; support can update the address for future orders. Acknowledge the inconvenience, explain the current limit and offer the available next step without sounding accusatory.A considerate customer-ready reply preserving all supplied policy and timing facts.Check the reply offers only the future-order address change and never promises an exception.grok-4.1Grok 4.1 is xAI’s dialogue-focused model for nuanced conversation, collaborative editing and creative writing. Its Thinking and Non-Thinking configurations offer different response approaches while keeping tone and context central to the exchange.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
Dialogue and general capability
Grok 4.1 develops the conversational side of xAI’s Grok family: natural dialogue, attention to nuanced intent and a more coherent conversational personality. Its development emphasizes helpfulness, style and collaborative interaction alongside core reasoning. The family has two configurations. Thinking works through the task before responding; Non-Thinking gives a direct response without thinking tokens. The general Grok 4.1 name describes this family without selecting either configuration.
Emotional understanding and collaboration
The conversational emphasis is useful when the task depends on how a response should sound as well as what it should say. Supply the situation, the intended reader and the facts that must remain unchanged. For example, ask for a considerate reply to a disappointed customer that acknowledges the concern, explains the next action and preserves the supplied terms. Then identify a specific revision: soften one sentence, make the explanation clearer or replace only the closing paragraph. These are illustrative editorial tasks, not promises that the model knows your company’s policies.
Creative writing
Grok 4.1’s announced capabilities include creative writing and maintaining a recognizable voice during interaction. Prepare a brief with the narrator or characters, the setting, the intended effect and the desired form. A useful task might request a short dialogue in which two characters disagree while each retains a distinct style, followed by a revision that changes the pacing. For business writing, provide the actual product details and ask for contrasting opening paragraphs for different audiences. Separate details that are fixed from those the model may invent within the creative assignment.
Factuality and information-seeking answers
The model’s post-training specifically addresses factual mistakes in information-seeking prompts. That focus does not make a fluent response a verified fact. When drafting from evidence, include the source material and ask the model to distinguish stated facts, interpretation and unanswered questions. A practical output could contain a concise answer, the supporting passage from the material you supplied and a question to resolve any gap. Search-based examples involve a model working with search tools; a text conversation alone should not be treated as a source of fresh retrieved information.
Training, output and edition scope
The training combines pre-training, targeted mid-training, supervised fine-tuning and reinforcement learning, including feedback about useful and appropriate responses. The work described here is text dialogue producing text: revisions, explanations, scenes and answers grounded in provided material. Keep the necessary conversation history and specify exactly which passage a follow-up should change. Evaluation of honesty, misleading premises and prompt injection underlines the importance of keeping supplied evidence distinct from instructions and checking factual conclusions. Grok 4.1 and Grok 4.1 Fast have separate identities; numerical context or output limits from the Fast line are not specifications for this family card.
POST /v1/chat/completionstexttext| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits grok-4.1 | 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.
| Configuration | Rate schedule | Charge |
|---|---|---|
| ConfigurationBase token rates | Rate scheduleBase rate | Charge Input · Input tokens $0.004 / 1K tokens Output · Output tokens $0.02 / 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", "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',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',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
Use the method paths shown above.
grok-4.1Grok 4.1 is xAI’s dialogue-focused model for nuanced conversation, collaborative editing and creative writing. Its Thinking and Non-Thinking configurations offer different response approaches while keeping tone and context central to the exchange.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
Dialogue and general capability
Grok 4.1 develops the conversational side of xAI’s Grok family: natural dialogue, attention to nuanced intent and a more coherent conversational personality. Its development emphasizes helpfulness, style and collaborative interaction alongside core reasoning. The family has two configurations. Thinking works through the task before responding; Non-Thinking gives a direct response without thinking tokens. The general Grok 4.1 name describes this family without selecting either configuration.
Emotional understanding and collaboration
The conversational emphasis is useful when the task depends on how a response should sound as well as what it should say. Supply the situation, the intended reader and the facts that must remain unchanged. For example, ask for a considerate reply to a disappointed customer that acknowledges the concern, explains the next action and preserves the supplied terms. Then identify a specific revision: soften one sentence, make the explanation clearer or replace only the closing paragraph. These are illustrative editorial tasks, not promises that the model knows your company’s policies.
Creative writing
Grok 4.1’s announced capabilities include creative writing and maintaining a recognizable voice during interaction. Prepare a brief with the narrator or characters, the setting, the intended effect and the desired form. A useful task might request a short dialogue in which two characters disagree while each retains a distinct style, followed by a revision that changes the pacing. For business writing, provide the actual product details and ask for contrasting opening paragraphs for different audiences. Separate details that are fixed from those the model may invent within the creative assignment.
Factuality and information-seeking answers
The model’s post-training specifically addresses factual mistakes in information-seeking prompts. That focus does not make a fluent response a verified fact. When drafting from evidence, include the source material and ask the model to distinguish stated facts, interpretation and unanswered questions. A practical output could contain a concise answer, the supporting passage from the material you supplied and a question to resolve any gap. Search-based examples involve a model working with search tools; a text conversation alone should not be treated as a source of fresh retrieved information.
Training, output and edition scope
The training combines pre-training, targeted mid-training, supervised fine-tuning and reinforcement learning, including feedback about useful and appropriate responses. The work described here is text dialogue producing text: revisions, explanations, scenes and answers grounded in provided material. Keep the necessary conversation history and specify exactly which passage a follow-up should change. Evaluation of honesty, misleading premises and prompt injection underlines the importance of keeping supplied evidence distinct from instructions and checking factual conclusions. Grok 4.1 and Grok 4.1 Fast have separate identities; numerical context or output limits from the Fast line are not specifications for this family card.
POST /v1/messagestextjson, sse| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits grok-4.1 | 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.
| Configuration | Rate schedule | Charge |
|---|---|---|
| ConfigurationBase token rates | Rate scheduleBase rate | Charge Input · Input tokens $0.004 / 1K tokens Output · Output tokens $0.02 / 1K tokens Input · Cache read $0 / 1K tokens Input · Cache write $0 / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
{\n "model": "grok-4.1",\n "max_tokens": 128,\n "messages": [\n {\n "role": "user",\n "content": "Summarize the incident report."\n }\n ],\n "stream": false\n}set -eu\n: "${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', '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', '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
Use the method paths shown above.
Technical example in English
Soften a disappointed-customer reply\n\nRevise this reply to a disappointed customer: 'You missed the deadline, so we cannot change it. Read the policy.' Facts to preserve: changes close 48 hours before dispatch; this order dispatches tomorrow; support can update the address for future orders. Acknowledge the inconvenience, explain the current limit and offer the available next step without sounding accusatory.A considerate customer-ready reply preserving all supplied policy and timing facts.Check the reply offers only the future-order address change and never promises an exception.