Qwen-VL-Plus 2025-05-07 is a named Alibaba visual-language snapshot. Qwen-VL-Plus focuses on detailed visual understanding and reading text within images, turning supplied visual evidence and a written question into a text answer. Its useful starting point is a concrete source: a photograph, a screenshot, an image of a document page or video frames whose content you want explained.
The manufacturer identifies May’s edition separately from Qwen-VL-Plus 2025-07-10. July’s specific surveillance-video enhancement belongs to that later snapshot. For May’s model, organise the task around its documented visual-language purpose: inspect details, read visible text, describe the scene and relate the result to your question. Keep the model name alongside the source material when comparing how the same assignment is answered.
Image understanding can use one picture or several pictures in a shared question. For document work, specify the fields or region you need: “Read the labels in the highlighted area, preserve their spelling and give one line for each label.” For a photographed instruction page, request a transcription followed by a separate explanation. This separation gives you an output that can be checked against the original before it becomes a help article or a user message.
For a screenshot, add the user’s goal and identify any element that should receive special attention. An illustrative brief is: “Explain the visible steps a user would follow to change this setting. Use the button names shown on the screen and list any text that is too small to read.” For comparison, label images A and B and supply a shared criterion, such as changed labels or the position of a visible object. The response is a proposed textual interpretation of the images.
Video understanding examines visual content through frames and returns a textual description or answer. Choose the episode relevant to the question and preserve its order; if speech is necessary to the analysis, add a transcript as text because this visual workflow does not understand the video’s audio track. A useful output can contain the observed sequence, relevant scene details and the questions that the supplied frames leave open.
Prepare document pages as readable images for this visual input workflow; a PDF or Word file is not itself an image input. For the documented Alibaba file workflow, prepare URL images no larger than 10 MB; a video file supplied by public URL must be 2–40 seconds long and no larger than 150 MB, or use an ordered list of 4–80 frames. Combined visual and text tokens must still fit the model input budget. Crop the region of interest when the full page makes labels too small, while retaining context needed to interpret a table or diagram. State whether you want literal transcription, an explanation or a short summary. That choice makes the deliverable easier to reuse without confusing the words visible in the source with the model’s additional interpretation.
", "aboutText": "Qwen-VL-Plus 2025-05-07 is a named Alibaba visual-language snapshot. Qwen-VL-Plus focuses on detailed visual understanding and reading text within images, turning supplied visual evidence and a written question into a text answer. Its useful starting point is a concrete source: a photograph, a screenshot, an image of a document page or video frames whose content you want explained.\n\nThe manufacturer identifies May’s edition separately from Qwen-VL-Plus 2025-07-10. July’s specific surveillance-video enhancement belongs to that later snapshot. For May’s model, organise the task around its documented visual-language purpose: inspect details, read visible text, describe the scene and relate the result to your question. Keep the model name alongside the source material when comparing how the same assignment is answered.\n\nImage understanding can use one picture or several pictures in a shared question. For document work, specify the fields or region you need: “Read the labels in the highlighted area, preserve their spelling and give one line for each label.” For a photographed instruction page, request a transcription followed by a separate explanation. This separation gives you an output that can be checked against the original before it becomes a help article or a user message.\n\nFor a screenshot, add the user’s goal and identify any element that should receive special attention. An illustrative brief is: “Explain the visible steps a user would follow to change this setting. Use the button names shown on the screen and list any text that is too small to read.” For comparison, label images A and B and supply a shared criterion, such as changed labels or the position of a visible object. The response is a proposed textual interpretation of the images.\n\nVideo understanding examines visual content through frames and returns a textual description or answer. Choose the episode relevant to the question and preserve its order; if speech is necessary to the analysis, add a transcript as text because this visual workflow does not understand the video’s audio track. A useful output can contain the observed sequence, relevant scene details and the questions that the supplied frames leave open.\n\nPrepare document pages as readable images for this visual input workflow; a PDF or Word file is not itself an image input. For the documented Alibaba file workflow, prepare URL images no larger than 10 MB; a video file supplied by public URL must be 2–40 seconds long and no larger than 150 MB, or use an ordered list of 4–80 frames. Combined visual and text tokens must still fit the model input budget. Crop the region of interest when the full page makes labels too small, while retaining context needed to interpret a table or diagram. State whether you want literal transcription, an explanation or a short summary. That choice makes the deliverable easier to reuse without confusing the words visible in the source with the model’s additional interpretation.", "editorialOverlay": { "copy_fields": { "practical_html": "Technical example in English
Turn transcribed controls into instructions\n\nUse this written transcription of a settings screen: heading 'Notifications'; checkbox 'Weekly summary' checked; checkbox 'Order alerts' checked; button 'Save changes'. A user wants to stop only weekly summaries. Write three numbered steps using the exact visible control names, and add one sentence stating which alert setting should stay enabled.Three concise steps plus a preservation note for Order alerts, based solely on the transcription.Only Weekly summary is unchecked, Save changes is included, and Order alerts remains enabled.qwen-vl-plus-2025-05-07Qwen-VL-Plus 2025-05-07 combines text with images and video to produce visual explanations. Use this named snapshot for scene descriptions, visible-text extraction and questions about details in documents, screenshots or a sequence of frames.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
Qwen-VL-Plus 2025-05-07 is a named Alibaba visual-language snapshot. Qwen-VL-Plus focuses on detailed visual understanding and reading text within images, turning supplied visual evidence and a written question into a text answer. Its useful starting point is a concrete source: a photograph, a screenshot, an image of a document page or video frames whose content you want explained.
The manufacturer identifies May’s edition separately from Qwen-VL-Plus 2025-07-10. July’s specific surveillance-video enhancement belongs to that later snapshot. For May’s model, organise the task around its documented visual-language purpose: inspect details, read visible text, describe the scene and relate the result to your question. Keep the model name alongside the source material when comparing how the same assignment is answered.
Image understanding can use one picture or several pictures in a shared question. For document work, specify the fields or region you need: “Read the labels in the highlighted area, preserve their spelling and give one line for each label.” For a photographed instruction page, request a transcription followed by a separate explanation. This separation gives you an output that can be checked against the original before it becomes a help article or a user message.
For a screenshot, add the user’s goal and identify any element that should receive special attention. An illustrative brief is: “Explain the visible steps a user would follow to change this setting. Use the button names shown on the screen and list any text that is too small to read.” For comparison, label images A and B and supply a shared criterion, such as changed labels or the position of a visible object. The response is a proposed textual interpretation of the images.
Video understanding examines visual content through frames and returns a textual description or answer. Choose the episode relevant to the question and preserve its order; if speech is necessary to the analysis, add a transcript as text because this visual workflow does not understand the video’s audio track. A useful output can contain the observed sequence, relevant scene details and the questions that the supplied frames leave open.
Prepare document pages as readable images for this visual input workflow; a PDF or Word file is not itself an image input. For the documented Alibaba file workflow, prepare URL images no larger than 10 MB; a video file supplied by public URL must be 2–40 seconds long and no larger than 150 MB, or use an ordered list of 4–80 frames. Combined visual and text tokens must still fit the model input budget. Crop the region of interest when the full page makes labels too small, while retaining context needed to interpret a table or diagram. State whether you want literal transcription, an explanation or a short summary. That choice makes the deliverable easier to reuse without confusing the words visible in the source with the model’s additional interpretation.
POST /v1/chat/completionstexttext| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits qwen-vl-plus-2025-05-07 | 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 $0.00044 / 1K tokens Output · Output tokens $0.0013 / 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-vl-plus-2025-05-07", "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-vl-plus-2025-05-07',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-vl-plus-2025-05-07',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-vl-plus-2025-05-07
Use the method paths shown above.
qwen-vl-plus-2025-05-07Qwen-VL-Plus 2025-05-07 combines text with images and video to produce visual explanations. Use this named snapshot for scene descriptions, visible-text extraction and questions about details in documents, screenshots or a sequence of frames.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
Qwen-VL-Plus 2025-05-07 is a named Alibaba visual-language snapshot. Qwen-VL-Plus focuses on detailed visual understanding and reading text within images, turning supplied visual evidence and a written question into a text answer. Its useful starting point is a concrete source: a photograph, a screenshot, an image of a document page or video frames whose content you want explained.
The manufacturer identifies May’s edition separately from Qwen-VL-Plus 2025-07-10. July’s specific surveillance-video enhancement belongs to that later snapshot. For May’s model, organise the task around its documented visual-language purpose: inspect details, read visible text, describe the scene and relate the result to your question. Keep the model name alongside the source material when comparing how the same assignment is answered.
Image understanding can use one picture or several pictures in a shared question. For document work, specify the fields or region you need: “Read the labels in the highlighted area, preserve their spelling and give one line for each label.” For a photographed instruction page, request a transcription followed by a separate explanation. This separation gives you an output that can be checked against the original before it becomes a help article or a user message.
For a screenshot, add the user’s goal and identify any element that should receive special attention. An illustrative brief is: “Explain the visible steps a user would follow to change this setting. Use the button names shown on the screen and list any text that is too small to read.” For comparison, label images A and B and supply a shared criterion, such as changed labels or the position of a visible object. The response is a proposed textual interpretation of the images.
Video understanding examines visual content through frames and returns a textual description or answer. Choose the episode relevant to the question and preserve its order; if speech is necessary to the analysis, add a transcript as text because this visual workflow does not understand the video’s audio track. A useful output can contain the observed sequence, relevant scene details and the questions that the supplied frames leave open.
Prepare document pages as readable images for this visual input workflow; a PDF or Word file is not itself an image input. For the documented Alibaba file workflow, prepare URL images no larger than 10 MB; a video file supplied by public URL must be 2–40 seconds long and no larger than 150 MB, or use an ordered list of 4–80 frames. Combined visual and text tokens must still fit the model input budget. Crop the region of interest when the full page makes labels too small, while retaining context needed to interpret a table or diagram. State whether you want literal transcription, an explanation or a short summary. That choice makes the deliverable easier to reuse without confusing the words visible in the source with the model’s additional interpretation.
POST /v1/messagestextjson, sse| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits qwen-vl-plus-2025-05-07 | 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 $0.00044 / 1K tokens Output · Output tokens $0.0013 / 1K tokens Input · Cache read $0 / 1K tokens Input · Cache write $0 / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
{\n "model": "qwen-vl-plus-2025-05-07",\n "max_tokens": 128,\n "messages": [\n {\n "role": "user",\n "content": "Summarize the incident report."\n }\n ],\n "stream": false\n}set -eu\n: "${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-vl-plus-2025-05-07', '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-vl-plus-2025-05-07', '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-vl-plus-2025-05-07
Use the method paths shown above.
Technical example in English
Turn transcribed controls into instructions\n\nUse this written transcription of a settings screen: heading 'Notifications'; checkbox 'Weekly summary' checked; checkbox 'Order alerts' checked; button 'Save changes'. A user wants to stop only weekly summaries. Write three numbered steps using the exact visible control names, and add one sentence stating which alert setting should stay enabled.Three concise steps plus a preservation note for Order alerts, based solely on the transcription.Only Weekly summary is unchecked, Save changes is included, and Order alerts remains enabled.