SenseChat-Turbo is the lightweight general-dialogue entry in SenseTime’s SenseNova model lineup. The maker positions it for quick question answering and model-tuning scenarios. Use it for a clearly bounded text task: answering a question from supplied information, rewriting a short message, identifying the point of a passage or continuing a focused conversation.
State what the answer should accomplish and include the information needed to produce it. For a customer-facing explanation, supply the relevant product facts and ask for a concise answer to the actual question. For a rewrite, give the original wording, audience and desired tone. Separate material to analyze from instructions about how to present the result.
A useful brief is: “Use the supplied policy excerpt to explain which step applies to this situation, then list the information still needed.” The resulting text should distinguish a supported answer from a question that the excerpt does not resolve. A short response is easier to use when it answers one clear issue rather than mixing several unrelated objectives.
The producer’s text-conversation workflow uses role-labeled messages and supports a sequence of user requests and assistant answers. Retain the earlier turn when a follow-up refers to it, along with any fact or constraint that still matters. In a workflow without server-held conversation history, the application supplies the relevant history with the next request.
For a continuing support exchange, preserve the question already clarified and the observation the user supplied. Ask the next response to continue from that state. Remove unrelated material that distracts from the present task, and reserve space for the answer within the configuration used for the conversation.
SenseTime lists model-tuning scenarios as part of Turbo’s positioning. For task-specific adaptation, prepare representative input-and-answer examples that show the behavior wanted: vocabulary, response form, level of detail and handling of missing information. Keep the expected answer consistent with the source facts in each example.
Useful examples might distinguish a factual response from a request for clarification, or a short explanation from a structured field list. Include difficult or ambiguous cases as well as straightforward ones. Check the examples against the intended task before treating their wording as a pattern to repeat. This preparation describes the desired dialogue behavior; the surrounding adaptation workflow determines how the examples are used.
The maker’s conversation interfaces support completed text and streamed text. A completed response gives the answer as one result; a stream supplies successive portions that the application assembles. Preserve the finished answer for review or reuse rather than judging the task only from the first visible words.
For extraction or classification, check the actual fields or labels against the source. For a rewrite, compare whether the meaning survived the requested tone change. For a question-and-answer exchange, check that the response addresses the current issue and that any follow-up request is relevant.
Turbo and SenseChat-5 are separately named general-dialogue models. SenseChat-5-Cantonese, the character-dialogue models and the vision or fusion-modality models also have their own profiles. Keep the selected edition and the supplied text with the final answer when comparing a task or continuing an application conversation.
Choose Turbo’s bounded-dialogue role when the result should be a concise, usable piece of text. Give the answer an explicit destination—a reply, a revised paragraph, a field list or a clarification question—and inspect that artifact for factual fit, wording and completeness before using it in the next step.
", "aboutText": "### Quick questions and tailored dialogue\n\nSenseChat-Turbo is the lightweight general-dialogue entry in SenseTime’s SenseNova model lineup. The maker positions it for quick question answering and model-tuning scenarios. Use it for a clearly bounded text task: answering a question from supplied information, rewriting a short message, identifying the point of a passage or continuing a focused conversation.\n\n### Give the model a precise request\n\nState what the answer should accomplish and include the information needed to produce it. For a customer-facing explanation, supply the relevant product facts and ask for a concise answer to the actual question. For a rewrite, give the original wording, audience and desired tone. Separate material to analyze from instructions about how to present the result.\n\nA useful brief is: “Use the supplied policy excerpt to explain which step applies to this situation, then list the information still needed.” The resulting text should distinguish a supported answer from a question that the excerpt does not resolve. A short response is easier to use when it answers one clear issue rather than mixing several unrelated objectives.\n\n### Keep conversation context relevant\n\nThe producer’s text-conversation workflow uses role-labeled messages and supports a sequence of user requests and assistant answers. Retain the earlier turn when a follow-up refers to it, along with any fact or constraint that still matters. In a workflow without server-held conversation history, the application supplies the relevant history with the next request.\n\nFor a continuing support exchange, preserve the question already clarified and the observation the user supplied. Ask the next response to continue from that state. Remove unrelated material that distracts from the present task, and reserve space for the answer within the configuration used for the conversation.\n\n### Prepare task-specific examples\n\nSenseTime lists model-tuning scenarios as part of Turbo’s positioning. For task-specific adaptation, prepare representative input-and-answer examples that show the behavior wanted: vocabulary, response form, level of detail and handling of missing information. Keep the expected answer consistent with the source facts in each example.\n\nUseful examples might distinguish a factual response from a request for clarification, or a short explanation from a structured field list. Include difficult or ambiguous cases as well as straightforward ones. Check the examples against the intended task before treating their wording as a pattern to repeat. This preparation describes the desired dialogue behavior; the surrounding adaptation workflow determines how the examples are used.\n\n### Completed and streamed responses\n\nThe maker’s conversation interfaces support completed text and streamed text. A completed response gives the answer as one result; a stream supplies successive portions that the application assembles. Preserve the finished answer for review or reuse rather than judging the task only from the first visible words.\n\nFor extraction or classification, check the actual fields or labels against the source. For a rewrite, compare whether the meaning survived the requested tone change. For a question-and-answer exchange, check that the response addresses the current issue and that any follow-up request is relevant.\n\n### Keep the Turbo edition distinct\n\nTurbo and SenseChat-5 are separately named general-dialogue models. SenseChat-5-Cantonese, the character-dialogue models and the vision or fusion-modality models also have their own profiles. Keep the selected edition and the supplied text with the final answer when comparing a task or continuing an application conversation.\n\nChoose Turbo’s bounded-dialogue role when the result should be a concise, usable piece of text. Give the answer an explicit destination—a reply, a revised paragraph, a field list or a clarification question—and inspect that artifact for factual fit, wording and completeness before using it in the next step.", "editorialOverlay": { "copy_fields": { "practical_html": "Technical example in English
Answer a cancellation-window question\n\nAnswer this customer using only the policy. Customer: 'My order is packed but has not left the warehouse. Can I cancel?' Policy: 'Cancellation is available until carrier handover, including packed orders. After handover, use the return procedure.' Give a concise answer and the next information needed if handover status is not confirmed.A short conditional answer and one relevant status-clarification question.The answer recognizes packed orders as eligible before handover and does not assume the handover status was checked.SenseChat-TurboSenseTime’s SenseChat-Turbo is a lightweight text-conversation model positioned for quick questions and task-specific adaptation. Prepare concise requests, keep the relevant dialogue together and review the completed answer against the facts supplied for the task.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
SenseChat-Turbo is the lightweight general-dialogue entry in SenseTime’s SenseNova model lineup. The maker positions it for quick question answering and model-tuning scenarios. Use it for a clearly bounded text task: answering a question from supplied information, rewriting a short message, identifying the point of a passage or continuing a focused conversation.
State what the answer should accomplish and include the information needed to produce it. For a customer-facing explanation, supply the relevant product facts and ask for a concise answer to the actual question. For a rewrite, give the original wording, audience and desired tone. Separate material to analyze from instructions about how to present the result.
A useful brief is: “Use the supplied policy excerpt to explain which step applies to this situation, then list the information still needed.” The resulting text should distinguish a supported answer from a question that the excerpt does not resolve. A short response is easier to use when it answers one clear issue rather than mixing several unrelated objectives.
The producer’s text-conversation workflow uses role-labeled messages and supports a sequence of user requests and assistant answers. Retain the earlier turn when a follow-up refers to it, along with any fact or constraint that still matters. In a workflow without server-held conversation history, the application supplies the relevant history with the next request.
For a continuing support exchange, preserve the question already clarified and the observation the user supplied. Ask the next response to continue from that state. Remove unrelated material that distracts from the present task, and reserve space for the answer within the configuration used for the conversation.
SenseTime lists model-tuning scenarios as part of Turbo’s positioning. For task-specific adaptation, prepare representative input-and-answer examples that show the behavior wanted: vocabulary, response form, level of detail and handling of missing information. Keep the expected answer consistent with the source facts in each example.
Useful examples might distinguish a factual response from a request for clarification, or a short explanation from a structured field list. Include difficult or ambiguous cases as well as straightforward ones. Check the examples against the intended task before treating their wording as a pattern to repeat. This preparation describes the desired dialogue behavior; the surrounding adaptation workflow determines how the examples are used.
The maker’s conversation interfaces support completed text and streamed text. A completed response gives the answer as one result; a stream supplies successive portions that the application assembles. Preserve the finished answer for review or reuse rather than judging the task only from the first visible words.
For extraction or classification, check the actual fields or labels against the source. For a rewrite, compare whether the meaning survived the requested tone change. For a question-and-answer exchange, check that the response addresses the current issue and that any follow-up request is relevant.
Turbo and SenseChat-5 are separately named general-dialogue models. SenseChat-5-Cantonese, the character-dialogue models and the vision or fusion-modality models also have their own profiles. Keep the selected edition and the supplied text with the final answer when comparing a task or continuing an application conversation.
Choose Turbo’s bounded-dialogue role when the result should be a concise, usable piece of text. Give the answer an explicit destination—a reply, a revised paragraph, a field list or a clarification question—and inspect that artifact for factual fit, wording and completeness before using it in the next step.
POST /v1/chat/completionstexttext| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits SenseChat-Turbo | 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 $0.00066 / 1K tokens Output · Output tokens $0.00154 / 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": "SenseChat-Turbo", "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='SenseChat-Turbo',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='SenseChat-Turbo',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 SenseChat-Turbo
Use the method paths shown above.
SenseChat-TurboSenseTime’s SenseChat-Turbo is a lightweight text-conversation model positioned for quick questions and task-specific adaptation. Prepare concise requests, keep the relevant dialogue together and review the completed answer against the facts supplied for the task.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
SenseChat-Turbo is the lightweight general-dialogue entry in SenseTime’s SenseNova model lineup. The maker positions it for quick question answering and model-tuning scenarios. Use it for a clearly bounded text task: answering a question from supplied information, rewriting a short message, identifying the point of a passage or continuing a focused conversation.
State what the answer should accomplish and include the information needed to produce it. For a customer-facing explanation, supply the relevant product facts and ask for a concise answer to the actual question. For a rewrite, give the original wording, audience and desired tone. Separate material to analyze from instructions about how to present the result.
A useful brief is: “Use the supplied policy excerpt to explain which step applies to this situation, then list the information still needed.” The resulting text should distinguish a supported answer from a question that the excerpt does not resolve. A short response is easier to use when it answers one clear issue rather than mixing several unrelated objectives.
The producer’s text-conversation workflow uses role-labeled messages and supports a sequence of user requests and assistant answers. Retain the earlier turn when a follow-up refers to it, along with any fact or constraint that still matters. In a workflow without server-held conversation history, the application supplies the relevant history with the next request.
For a continuing support exchange, preserve the question already clarified and the observation the user supplied. Ask the next response to continue from that state. Remove unrelated material that distracts from the present task, and reserve space for the answer within the configuration used for the conversation.
SenseTime lists model-tuning scenarios as part of Turbo’s positioning. For task-specific adaptation, prepare representative input-and-answer examples that show the behavior wanted: vocabulary, response form, level of detail and handling of missing information. Keep the expected answer consistent with the source facts in each example.
Useful examples might distinguish a factual response from a request for clarification, or a short explanation from a structured field list. Include difficult or ambiguous cases as well as straightforward ones. Check the examples against the intended task before treating their wording as a pattern to repeat. This preparation describes the desired dialogue behavior; the surrounding adaptation workflow determines how the examples are used.
The maker’s conversation interfaces support completed text and streamed text. A completed response gives the answer as one result; a stream supplies successive portions that the application assembles. Preserve the finished answer for review or reuse rather than judging the task only from the first visible words.
For extraction or classification, check the actual fields or labels against the source. For a rewrite, compare whether the meaning survived the requested tone change. For a question-and-answer exchange, check that the response addresses the current issue and that any follow-up request is relevant.
Turbo and SenseChat-5 are separately named general-dialogue models. SenseChat-5-Cantonese, the character-dialogue models and the vision or fusion-modality models also have their own profiles. Keep the selected edition and the supplied text with the final answer when comparing a task or continuing an application conversation.
Choose Turbo’s bounded-dialogue role when the result should be a concise, usable piece of text. Give the answer an explicit destination—a reply, a revised paragraph, a field list or a clarification question—and inspect that artifact for factual fit, wording and completeness before using it in the next step.
POST /v1/messagestextjson, sse| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits SenseChat-Turbo | 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 $0.00066 / 1K tokens Output · Output tokens $0.00154 / 1K tokens Input · Cache read $0 / 1K tokens Input · Cache write $0 / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
{\n "model": "SenseChat-Turbo",\n "max_tokens": 128,\n "messages": [\n {\n "role": "user",\n "content": "Summarize the incident report."\n }\n ],\n "stream": false\n}set -eu\n: "${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': 'SenseChat-Turbo', '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': 'SenseChat-Turbo', '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 SenseChat-Turbo
Use the method paths shown above.
Technical example in English
Answer a cancellation-window question\n\nAnswer this customer using only the policy. Customer: 'My order is packed but has not left the warehouse. Can I cancel?' Policy: 'Cancellation is available until carrier handover, including packed orders. After handover, use the return procedure.' Give a concise answer and the next information needed if handover status is not confirmed.A short conditional answer and one relevant status-clarification question.The answer recognizes packed orders as eligible before handover and does not assume the handover status was checked.