Spark Max is the iFlytek Spark text-model family identified in the manufacturer’s documentation as generalv3.5. iFlytek highlights structured extraction and logical reasoning in its description of Max. It is useful to consider when a task starts with text and needs a defined result: an explanation of a process, fields extracted from a message, a reply shaped by a conversation, or a draft that follows a particular role and style.
The input is a sequence of messages. The documented roles distinguish the user’s request, the assistant’s previous replies, a system instruction and results returned by tools. A system instruction can define the assistant’s purpose and the rules for its response. For a continuing conversation, include the relevant previous messages in order: the information needed to answer the current request must be present in the submitted context. Put the immediate question beside the source material it concerns, and specify which facts must survive editing.
The manufacturer provides ordinary text output and a JSON-object mode. In JSON mode, the instructions still need to ask for JSON explicitly. This is useful for extraction tasks with an agreed result format. An illustrative request is: “Read this service enquiry. Return a JSON object with the requested action, the information already supplied and the questions that remain unanswered.” List the expected fields and rules for missing values rather than treating the output-format setting as a substitute for a precise task.
Generation controls include sampling settings, repetition penalties, an output-length setting and streaming or complete-response delivery. For the documented Max entry, iFlytek lists an 8K input configuration and an 8K maximum output; the Max max_tokens setting runs from 1 to 8,192, with a documented default of 4,096. The separate Max-32K name identifies another configuration. Keep the conversation, reference text and current question within the applicable input budget, and select the answer length according to the task rather than the volume of supplied material.
The manufacturer’s tool descriptions include web search and built-in utilities for weather, dates, poetry, word information and stock information. Search has normal and deep modes: the normal mode uses retrieved summaries, while the deep mode uses fuller retrieved material. The source-label control can request information about retrieved sources. These are documented features of iFlytek’s tool interface; prepare a question that states what information is missing and what evidence would answer it.
Function calling is described for the manufacturer’s HTTP interface. The application supplies a function name, its purpose and a valid JSON Schema for parameters. Tool-selection options include automatic selection, disabling calls, requiring a call or selecting a specified function. The documentation also distinguishes alternative ways of representing returned calls. The function’s actual result is supplied back with a tool-role message, so the surrounding application remains responsible for carrying out the operation and returning data the model can use.
For a practical assistant, define one useful task and the fields needed to complete it. A workflow example might be to extract a requested product characteristic, obtain an unresolved value through an application tool, then write a reply that separates supplied facts from open questions. For editing, specify the reader, tone and facts to preserve; for a logical question, state the conditions and ask for a conclusion that can be checked against them. The generalv3.5 name belongs to the Max family rather than an independently named deep-thinking model. Keep its configuration distinct from other Spark editions, and establish the limits and tool surface of the selected connection without importing features from a different release.
", "aboutText": "Spark Max is the iFlytek Spark text-model family identified in the manufacturer’s documentation as generalv3.5. iFlytek highlights structured extraction and logical reasoning in its description of Max. It is useful to consider when a task starts with text and needs a defined result: an explanation of a process, fields extracted from a message, a reply shaped by a conversation, or a draft that follows a particular role and style.\n\nThe input is a sequence of messages. The documented roles distinguish the user’s request, the assistant’s previous replies, a system instruction and results returned by tools. A system instruction can define the assistant’s purpose and the rules for its response. For a continuing conversation, include the relevant previous messages in order: the information needed to answer the current request must be present in the submitted context. Put the immediate question beside the source material it concerns, and specify which facts must survive editing.\n\nThe manufacturer provides ordinary text output and a JSON-object mode. In JSON mode, the instructions still need to ask for JSON explicitly. This is useful for extraction tasks with an agreed result format. An illustrative request is: “Read this service enquiry. Return a JSON object with the requested action, the information already supplied and the questions that remain unanswered.” List the expected fields and rules for missing values rather than treating the output-format setting as a substitute for a precise task.\n\nGeneration controls include sampling settings, repetition penalties, an output-length setting and streaming or complete-response delivery. For the documented Max entry, iFlytek lists an 8K input configuration and an 8K maximum output; the Max max_tokens setting runs from 1 to 8,192, with a documented default of 4,096. The separate Max-32K name identifies another configuration. Keep the conversation, reference text and current question within the applicable input budget, and select the answer length according to the task rather than the volume of supplied material.\n\nThe manufacturer’s tool descriptions include web search and built-in utilities for weather, dates, poetry, word information and stock information. Search has normal and deep modes: the normal mode uses retrieved summaries, while the deep mode uses fuller retrieved material. The source-label control can request information about retrieved sources. These are documented features of iFlytek’s tool interface; prepare a question that states what information is missing and what evidence would answer it.\n\nFunction calling is described for the manufacturer’s HTTP interface. The application supplies a function name, its purpose and a valid JSON Schema for parameters. Tool-selection options include automatic selection, disabling calls, requiring a call or selecting a specified function. The documentation also distinguishes alternative ways of representing returned calls. The function’s actual result is supplied back with a tool-role message, so the surrounding application remains responsible for carrying out the operation and returning data the model can use.\n\nFor a practical assistant, define one useful task and the fields needed to complete it. A workflow example might be to extract a requested product characteristic, obtain an unresolved value through an application tool, then write a reply that separates supplied facts from open questions. For editing, specify the reader, tone and facts to preserve; for a logical question, state the conditions and ask for a conclusion that can be checked against them. The generalv3.5 name belongs to the Max family rather than an independently named deep-thinking model. Keep its configuration distinct from other Spark editions, and establish the limits and tool surface of the selected connection without importing features from a different release.", "editorialOverlay": { "copy_fields": { "practical_html": "Technical example in English
Extract a service enquiry\n\nRead this enquiry: 'Please send two replacement filters for purifier P40 to our Leeds office. The old ones arrived crushed. I can confirm the street address tomorrow.' Return a compact record with requested_action, product, quantity, reason and missing_information. Use null for any absent scalar value. Follow with one question that obtains only the missing delivery detail.A requested JSON-shaped record and one concise address question; the structure must be checked before use.The record preserves P40 and quantity 2 without inventing the street address or asserting a replacement was sent.generalv3.5Spark Max is iFlytek’s text-focused model for structured extraction, logical tasks and conversation. Its documented interface combines role-based messages, JSON-object output, web-search controls and application-defined function calls.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
Spark Max is the iFlytek Spark text-model family identified in the manufacturer’s documentation as generalv3.5. iFlytek highlights structured extraction and logical reasoning in its description of Max. It is useful to consider when a task starts with text and needs a defined result: an explanation of a process, fields extracted from a message, a reply shaped by a conversation, or a draft that follows a particular role and style.
The input is a sequence of messages. The documented roles distinguish the user’s request, the assistant’s previous replies, a system instruction and results returned by tools. A system instruction can define the assistant’s purpose and the rules for its response. For a continuing conversation, include the relevant previous messages in order: the information needed to answer the current request must be present in the submitted context. Put the immediate question beside the source material it concerns, and specify which facts must survive editing.
The manufacturer provides ordinary text output and a JSON-object mode. In JSON mode, the instructions still need to ask for JSON explicitly. This is useful for extraction tasks with an agreed result format. An illustrative request is: “Read this service enquiry. Return a JSON object with the requested action, the information already supplied and the questions that remain unanswered.” List the expected fields and rules for missing values rather than treating the output-format setting as a substitute for a precise task.
Generation controls include sampling settings, repetition penalties, an output-length setting and streaming or complete-response delivery. For the documented Max entry, iFlytek lists an 8K input configuration and an 8K maximum output; the Max max_tokens setting runs from 1 to 8,192, with a documented default of 4,096. The separate Max-32K name identifies another configuration. Keep the conversation, reference text and current question within the applicable input budget, and select the answer length according to the task rather than the volume of supplied material.
The manufacturer’s tool descriptions include web search and built-in utilities for weather, dates, poetry, word information and stock information. Search has normal and deep modes: the normal mode uses retrieved summaries, while the deep mode uses fuller retrieved material. The source-label control can request information about retrieved sources. These are documented features of iFlytek’s tool interface; prepare a question that states what information is missing and what evidence would answer it.
Function calling is described for the manufacturer’s HTTP interface. The application supplies a function name, its purpose and a valid JSON Schema for parameters. Tool-selection options include automatic selection, disabling calls, requiring a call or selecting a specified function. The documentation also distinguishes alternative ways of representing returned calls. The function’s actual result is supplied back with a tool-role message, so the surrounding application remains responsible for carrying out the operation and returning data the model can use.
For a practical assistant, define one useful task and the fields needed to complete it. A workflow example might be to extract a requested product characteristic, obtain an unresolved value through an application tool, then write a reply that separates supplied facts from open questions. For editing, specify the reader, tone and facts to preserve; for a logical question, state the conditions and ask for a conclusion that can be checked against them. The generalv3.5 name belongs to the Max family rather than an independently named deep-thinking model. Keep its configuration distinct from other Spark editions, and establish the limits and tool surface of the selected connection without importing features from a different release.
POST /v1/chat/completionstexttext| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits generalv3.5 | 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 $0.00946 / 1K tokens Output · Output tokens $0.00946 / 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": "generalv3.5", "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='generalv3.5',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='generalv3.5',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 generalv3.5
Use the method paths shown above.
generalv3.5Spark Max is iFlytek’s text-focused model for structured extraction, logical tasks and conversation. Its documented interface combines role-based messages, JSON-object output, web-search controls and application-defined function calls.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
Spark Max is the iFlytek Spark text-model family identified in the manufacturer’s documentation as generalv3.5. iFlytek highlights structured extraction and logical reasoning in its description of Max. It is useful to consider when a task starts with text and needs a defined result: an explanation of a process, fields extracted from a message, a reply shaped by a conversation, or a draft that follows a particular role and style.
The input is a sequence of messages. The documented roles distinguish the user’s request, the assistant’s previous replies, a system instruction and results returned by tools. A system instruction can define the assistant’s purpose and the rules for its response. For a continuing conversation, include the relevant previous messages in order: the information needed to answer the current request must be present in the submitted context. Put the immediate question beside the source material it concerns, and specify which facts must survive editing.
The manufacturer provides ordinary text output and a JSON-object mode. In JSON mode, the instructions still need to ask for JSON explicitly. This is useful for extraction tasks with an agreed result format. An illustrative request is: “Read this service enquiry. Return a JSON object with the requested action, the information already supplied and the questions that remain unanswered.” List the expected fields and rules for missing values rather than treating the output-format setting as a substitute for a precise task.
Generation controls include sampling settings, repetition penalties, an output-length setting and streaming or complete-response delivery. For the documented Max entry, iFlytek lists an 8K input configuration and an 8K maximum output; the Max max_tokens setting runs from 1 to 8,192, with a documented default of 4,096. The separate Max-32K name identifies another configuration. Keep the conversation, reference text and current question within the applicable input budget, and select the answer length according to the task rather than the volume of supplied material.
The manufacturer’s tool descriptions include web search and built-in utilities for weather, dates, poetry, word information and stock information. Search has normal and deep modes: the normal mode uses retrieved summaries, while the deep mode uses fuller retrieved material. The source-label control can request information about retrieved sources. These are documented features of iFlytek’s tool interface; prepare a question that states what information is missing and what evidence would answer it.
Function calling is described for the manufacturer’s HTTP interface. The application supplies a function name, its purpose and a valid JSON Schema for parameters. Tool-selection options include automatic selection, disabling calls, requiring a call or selecting a specified function. The documentation also distinguishes alternative ways of representing returned calls. The function’s actual result is supplied back with a tool-role message, so the surrounding application remains responsible for carrying out the operation and returning data the model can use.
For a practical assistant, define one useful task and the fields needed to complete it. A workflow example might be to extract a requested product characteristic, obtain an unresolved value through an application tool, then write a reply that separates supplied facts from open questions. For editing, specify the reader, tone and facts to preserve; for a logical question, state the conditions and ask for a conclusion that can be checked against them. The generalv3.5 name belongs to the Max family rather than an independently named deep-thinking model. Keep its configuration distinct from other Spark editions, and establish the limits and tool surface of the selected connection without importing features from a different release.
POST /v1/messagestextjson, sse| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits generalv3.5 | 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 $0.00946 / 1K tokens Output · Output tokens $0.00946 / 1K tokens Input · Cache read $0 / 1K tokens Input · Cache write $0 / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
{\n "model": "generalv3.5",\n "max_tokens": 128,\n "messages": [\n {\n "role": "user",\n "content": "Summarize the incident report."\n }\n ],\n "stream": false\n}set -eu\n: "${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': 'generalv3.5', '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': 'generalv3.5', '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 generalv3.5
Use the method paths shown above.
Technical example in English
Extract a service enquiry\n\nRead this enquiry: 'Please send two replacement filters for purifier P40 to our Leeds office. The old ones arrived crushed. I can confirm the street address tomorrow.' Return a compact record with requested_action, product, quantity, reason and missing_information. Use null for any absent scalar value. Follow with one question that obtains only the missing delivery detail.A requested JSON-shaped record and one concise address question; the structure must be checked before use.The record preserves P40 and quantity 2 without inventing the street address or asserting a replacement was sent.