Qwen Turbo 2024-11-01 is Alibaba’s fixed Qwen-Turbo snapshot from the Qwen2.5 generation. It takes written instructions and text context and produces a text answer. Useful tasks include questions about supplied documents, summaries that connect several sources, explanations of a codebase and extraction into JSON. Keep the dated model name with the prompt when comparing results between editions.
Qwen’s Qwen2.5-Turbo introduction demonstrates reading long narratives and several related papers. The useful output depends on the question: an overview can explain the relationship between works, while a focused comparison can separate their approaches and supporting evidence. Supply clearly labeled text and ask for the distinctions that matter to the reader. An original brief might request each document’s main proposal, the passage supporting it and any disagreements between the documents. Ask for the answer in the language the audience needs, and keep quotations connected to their source.
The maker also demonstrates reading a repository assembled into a text file, first explaining its purpose and then locating classes and their file paths. Prepare code with file boundaries, names and relevant surrounding definitions intact. Ask for a map of a component before requesting a narrower explanation of how its parts relate. The resulting explanation or list of locations can guide inspection of the original files; review those locations against the supplied code.
In the manufacturer’s documented text workflow, a system message establishes the role and task rules, a user message supplies the question and material, and the model replies as the assistant. Keep the intended artifact explicit: a summary, a comparison table, a concise explanation or extracted fields. For a document task, distinguish the text to be read from the instruction describing what to do with it. Include terminology, output language and any rule for handling information that the source does not provide.
The maker’s Chat Completions workflow is stateless: continued discussion uses conversation history supplied with the next question. Append the preceding user and assistant messages when the follow-up depends on them. A first turn can establish a document’s argument; a second can ask which passage supports one conclusion. Keep the necessary source context available rather than assuming that a previous answer gives the model permanent memory. As the discussion grows, retain recent turns and the important source facts, or use a reviewed summary of earlier material. Removing history or compressing it can remove details needed for the next answer.
Alibaba explicitly includes qwen-turbo-2024-11-01 in its JSON Object support. In the maker’s workflow, the response format is set to json_object and a system or user instruction must contain the word JSON. Specify field names and the treatment of missing information. For example, an extraction brief can request a document title, its named author and a supporting passage, with a stated convention for an absent author.
JSON Object mode targets valid JSON syntax; it does not enforce a particular schema, stable field names or field types. Inspect the returned object for the fields and values the task requires before using it downstream. A result cut off before completion may also be invalid JSON. Retain the complete object and its source text so that a formatting problem can be distinguished from an incorrect extraction.
Qwen’s introduction identifies instability in demanding long-sequence tasks. A large amount of supplied text does not guarantee that every detail is retrieved or every relationship is interpreted correctly. Check important statements, references and code locations against the original material, especially when the conclusion combines distant passages. Preserve the source, exact prompt and selected response as the working artifact. Alibaba’s version mapping distinguishes this snapshot from Qwen-Turbo-2025-04-28, so a comparison of editions should retain both model names and the same task.
", "aboutText": "Qwen Turbo 2024-11-01 is Alibaba’s fixed Qwen-Turbo snapshot from the Qwen2.5 generation. It takes written instructions and text context and produces a text answer. Useful tasks include questions about supplied documents, summaries that connect several sources, explanations of a codebase and extraction into JSON. Keep the dated model name with the prompt when comparing results between editions.\n\n### Read connected material with a specific question\n\nQwen’s Qwen2.5-Turbo introduction demonstrates reading long narratives and several related papers. The useful output depends on the question: an overview can explain the relationship between works, while a focused comparison can separate their approaches and supporting evidence. Supply clearly labeled text and ask for the distinctions that matter to the reader. An original brief might request each document’s main proposal, the passage supporting it and any disagreements between the documents. Ask for the answer in the language the audience needs, and keep quotations connected to their source.\n\n### Understand a repository through its text\n\nThe maker also demonstrates reading a repository assembled into a text file, first explaining its purpose and then locating classes and their file paths. Prepare code with file boundaries, names and relevant surrounding definitions intact. Ask for a map of a component before requesting a narrower explanation of how its parts relate. The resulting explanation or list of locations can guide inspection of the original files; review those locations against the supplied code.\n\n### Supply instructions and material as messages\n\nIn the manufacturer’s documented text workflow, a system message establishes the role and task rules, a user message supplies the question and material, and the model replies as the assistant. Keep the intended artifact explicit: a summary, a comparison table, a concise explanation or extracted fields. For a document task, distinguish the text to be read from the instruction describing what to do with it. Include terminology, output language and any rule for handling information that the source does not provide.\n\n### Continue a conversation with the relevant history\n\nThe maker’s Chat Completions workflow is stateless: continued discussion uses conversation history supplied with the next question. Append the preceding user and assistant messages when the follow-up depends on them. A first turn can establish a document’s argument; a second can ask which passage supports one conclusion. Keep the necessary source context available rather than assuming that a previous answer gives the model permanent memory. As the discussion grows, retain recent turns and the important source facts, or use a reviewed summary of earlier material. Removing history or compressing it can remove details needed for the next answer.\n\n### Extract information with JSON Object mode\n\nAlibaba explicitly includes qwen-turbo-2024-11-01 in its JSON Object support. In the maker’s workflow, the response format is set to json_object and a system or user instruction must contain the word JSON. Specify field names and the treatment of missing information. For example, an extraction brief can request a document title, its named author and a supporting passage, with a stated convention for an absent author.\n\nJSON Object mode targets valid JSON syntax; it does not enforce a particular schema, stable field names or field types. Inspect the returned object for the fields and values the task requires before using it downstream. A result cut off before completion may also be invalid JSON. Retain the complete object and its source text so that a formatting problem can be distinguished from an incorrect extraction.\n\n### Review the answer against the original material\n\nQwen’s introduction identifies instability in demanding long-sequence tasks. A large amount of supplied text does not guarantee that every detail is retrieved or every relationship is interpreted correctly. Check important statements, references and code locations against the original material, especially when the conclusion combines distant passages. Preserve the source, exact prompt and selected response as the working artifact. Alibaba’s version mapping distinguishes this snapshot from Qwen-Turbo-2025-04-28, so a comparison of editions should retain both model names and the same task.", "editorialOverlay": { "copy_fields": { "practical_html": "Technical example in English
Map a component from supplied code text\n\nMap this miniature repository. File reader.py: class CsvReader exposes read(path). File service.py: class ImportService receives a reader and calls reader.read(path) inside import_file(path). Explain each class’s role, identify where file loading occurs and list the supplied paths. Do not invent validation, storage or error handling absent from the excerpts.A two-component map, the read call location and an explicit list of unspecified behavior.Check that file loading is attributed to CsvReader.read and orchestration to ImportService.import_file, using the supplied paths only.qwen-turbo-2024-11-01Alibaba’s Qwen Turbo 2024-11-01 is a fixed text-model snapshot for answering questions about supplied material, summarizing related documents and reading source code. It also supports the maker’s JSON Object mode for extracting information into a parseable result.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
Qwen Turbo 2024-11-01 is Alibaba’s fixed Qwen-Turbo snapshot from the Qwen2.5 generation. It takes written instructions and text context and produces a text answer. Useful tasks include questions about supplied documents, summaries that connect several sources, explanations of a codebase and extraction into JSON. Keep the dated model name with the prompt when comparing results between editions.
Qwen’s Qwen2.5-Turbo introduction demonstrates reading long narratives and several related papers. The useful output depends on the question: an overview can explain the relationship between works, while a focused comparison can separate their approaches and supporting evidence. Supply clearly labeled text and ask for the distinctions that matter to the reader. An original brief might request each document’s main proposal, the passage supporting it and any disagreements between the documents. Ask for the answer in the language the audience needs, and keep quotations connected to their source.
The maker also demonstrates reading a repository assembled into a text file, first explaining its purpose and then locating classes and their file paths. Prepare code with file boundaries, names and relevant surrounding definitions intact. Ask for a map of a component before requesting a narrower explanation of how its parts relate. The resulting explanation or list of locations can guide inspection of the original files; review those locations against the supplied code.
In the manufacturer’s documented text workflow, a system message establishes the role and task rules, a user message supplies the question and material, and the model replies as the assistant. Keep the intended artifact explicit: a summary, a comparison table, a concise explanation or extracted fields. For a document task, distinguish the text to be read from the instruction describing what to do with it. Include terminology, output language and any rule for handling information that the source does not provide.
The maker’s Chat Completions workflow is stateless: continued discussion uses conversation history supplied with the next question. Append the preceding user and assistant messages when the follow-up depends on them. A first turn can establish a document’s argument; a second can ask which passage supports one conclusion. Keep the necessary source context available rather than assuming that a previous answer gives the model permanent memory. As the discussion grows, retain recent turns and the important source facts, or use a reviewed summary of earlier material. Removing history or compressing it can remove details needed for the next answer.
Alibaba explicitly includes qwen-turbo-2024-11-01 in its JSON Object support. In the maker’s workflow, the response format is set to json_object and a system or user instruction must contain the word JSON. Specify field names and the treatment of missing information. For example, an extraction brief can request a document title, its named author and a supporting passage, with a stated convention for an absent author.
JSON Object mode targets valid JSON syntax; it does not enforce a particular schema, stable field names or field types. Inspect the returned object for the fields and values the task requires before using it downstream. A result cut off before completion may also be invalid JSON. Retain the complete object and its source text so that a formatting problem can be distinguished from an incorrect extraction.
Qwen’s introduction identifies instability in demanding long-sequence tasks. A large amount of supplied text does not guarantee that every detail is retrieved or every relationship is interpreted correctly. Check important statements, references and code locations against the original material, especially when the conclusion combines distant passages. Preserve the source, exact prompt and selected response as the working artifact. Alibaba’s version mapping distinguishes this snapshot from Qwen-Turbo-2025-04-28, so a comparison of editions should retain both model names and the same task.
POST /v1/chat/completionstexttext| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits qwen-turbo-2024-11-01 | 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 $0.0001 / 1K tokens Output · Output tokens $0.00018 / 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": "qwen-turbo-2024-11-01", "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='qwen-turbo-2024-11-01',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='qwen-turbo-2024-11-01',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 qwen-turbo-2024-11-01
Use the method paths shown above.
qwen-turbo-2024-11-01Alibaba’s Qwen Turbo 2024-11-01 is a fixed text-model snapshot for answering questions about supplied material, summarizing related documents and reading source code. It also supports the maker’s JSON Object mode for extracting information into a parseable result.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
Qwen Turbo 2024-11-01 is Alibaba’s fixed Qwen-Turbo snapshot from the Qwen2.5 generation. It takes written instructions and text context and produces a text answer. Useful tasks include questions about supplied documents, summaries that connect several sources, explanations of a codebase and extraction into JSON. Keep the dated model name with the prompt when comparing results between editions.
Qwen’s Qwen2.5-Turbo introduction demonstrates reading long narratives and several related papers. The useful output depends on the question: an overview can explain the relationship between works, while a focused comparison can separate their approaches and supporting evidence. Supply clearly labeled text and ask for the distinctions that matter to the reader. An original brief might request each document’s main proposal, the passage supporting it and any disagreements between the documents. Ask for the answer in the language the audience needs, and keep quotations connected to their source.
The maker also demonstrates reading a repository assembled into a text file, first explaining its purpose and then locating classes and their file paths. Prepare code with file boundaries, names and relevant surrounding definitions intact. Ask for a map of a component before requesting a narrower explanation of how its parts relate. The resulting explanation or list of locations can guide inspection of the original files; review those locations against the supplied code.
In the manufacturer’s documented text workflow, a system message establishes the role and task rules, a user message supplies the question and material, and the model replies as the assistant. Keep the intended artifact explicit: a summary, a comparison table, a concise explanation or extracted fields. For a document task, distinguish the text to be read from the instruction describing what to do with it. Include terminology, output language and any rule for handling information that the source does not provide.
The maker’s Chat Completions workflow is stateless: continued discussion uses conversation history supplied with the next question. Append the preceding user and assistant messages when the follow-up depends on them. A first turn can establish a document’s argument; a second can ask which passage supports one conclusion. Keep the necessary source context available rather than assuming that a previous answer gives the model permanent memory. As the discussion grows, retain recent turns and the important source facts, or use a reviewed summary of earlier material. Removing history or compressing it can remove details needed for the next answer.
Alibaba explicitly includes qwen-turbo-2024-11-01 in its JSON Object support. In the maker’s workflow, the response format is set to json_object and a system or user instruction must contain the word JSON. Specify field names and the treatment of missing information. For example, an extraction brief can request a document title, its named author and a supporting passage, with a stated convention for an absent author.
JSON Object mode targets valid JSON syntax; it does not enforce a particular schema, stable field names or field types. Inspect the returned object for the fields and values the task requires before using it downstream. A result cut off before completion may also be invalid JSON. Retain the complete object and its source text so that a formatting problem can be distinguished from an incorrect extraction.
Qwen’s introduction identifies instability in demanding long-sequence tasks. A large amount of supplied text does not guarantee that every detail is retrieved or every relationship is interpreted correctly. Check important statements, references and code locations against the original material, especially when the conclusion combines distant passages. Preserve the source, exact prompt and selected response as the working artifact. Alibaba’s version mapping distinguishes this snapshot from Qwen-Turbo-2025-04-28, so a comparison of editions should retain both model names and the same task.
POST /v1/messagestextjson, sse| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits qwen-turbo-2024-11-01 | 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 $0.0001 / 1K tokens Output · Output tokens $0.00018 / 1K tokens Input · Cache read $0 / 1K tokens Input · Cache write $0 / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
{\n "model": "qwen-turbo-2024-11-01",\n "max_tokens": 128,\n "messages": [\n {\n "role": "user",\n "content": "Summarize the incident report."\n }\n ],\n "stream": false\n}set -eu\n: "${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': 'qwen-turbo-2024-11-01', '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': 'qwen-turbo-2024-11-01', '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 qwen-turbo-2024-11-01
Use the method paths shown above.
Technical example in English
Map a component from supplied code text\n\nMap this miniature repository. File reader.py: class CsvReader exposes read(path). File service.py: class ImportService receives a reader and calls reader.read(path) inside import_file(path). Explain each class’s role, identify where file loading occurs and list the supplied paths. Do not invent validation, storage or error handling absent from the excerpts.A two-component map, the read call location and an explicit list of unspecified behavior.Check that file loading is attributed to CsvReader.read and orchestration to ImportService.import_file, using the supplied paths only.