Qwen-VL-Plus 2025-07-10 is Alibaba’s dated visual-language model for understanding images and video. Its defining update is stronger understanding of surveillance-video content. The model accepts visual material together with text instructions and returns text: an event description, an answer about the scene or a report organised around the details you ask it to inspect. Alibaba also names this snapshot qwen-vl-plus-0710.
The manufacturer distinguishes this edition from Qwen-VL-Plus 2025-05-07 and the Qwen-VL-Plus latest alias described on the same page. That distinction matters when your task specifically concerns camera footage: the surveillance-focused update belongs to July’s named snapshot. Keep the exact edition in the task record when comparing prompts or reviewing the same footage again.
Visual questions can concern what appears in a frame, where objects are positioned and how visible actions develop across a sequence. A single image can support a scene description; several labelled images can supply different views or successive moments. Video understanding uses sampled frames. The resulting explanation concerns the visible evidence; the model does not generate a replacement video, and this visual workflow does not analyse the video’s audio track.
For an event report, prepare a relevant clip or an ordered frame sequence and identify the zone or activity that matters. An illustrative brief is: “Describe the person’s visible actions near the entrance, in order. For each observation, give the object involved, its position and the change you can see. Put uncertain details in a separate final sentence.” You can request a short narrative, a list of observations or named report sections. These are instructions for the text answer, rather than a separate structured-output mode.
This snapshot’s published context window is 32,768 tokens, with a maximum input of 30,720 tokens and maximum output of 8,096 tokens. The dedicated model table also gives a maximum of 16,384 tokens for one image. Text instructions, conversation context and visual content must fit the input budget together; the output ceiling is a separate bound, not extra space for additional source material. When reviewing a long recording, isolate the episode and retain enough surrounding frames to make its sequence understandable.
Choose source frames in which the relevant objects are distinguishable, label different cameras or views, and make the requested observation precise. 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. If the task depends on a spoken explanation, supply its transcript separately as text. Ask the report to separate a visible movement from an inferred purpose: “walked toward the door” is a different statement from an intention to leave. The useful deliverable is a reviewable text record tied to the footage and the stated observation criteria.
", "aboutText": "Qwen-VL-Plus 2025-07-10 is Alibaba’s dated visual-language model for understanding images and video. Its defining update is stronger understanding of surveillance-video content. The model accepts visual material together with text instructions and returns text: an event description, an answer about the scene or a report organised around the details you ask it to inspect. Alibaba also names this snapshot qwen-vl-plus-0710.\n\nThe manufacturer distinguishes this edition from Qwen-VL-Plus 2025-05-07 and the Qwen-VL-Plus latest alias described on the same page. That distinction matters when your task specifically concerns camera footage: the surveillance-focused update belongs to July’s named snapshot. Keep the exact edition in the task record when comparing prompts or reviewing the same footage again.\n\nVisual questions can concern what appears in a frame, where objects are positioned and how visible actions develop across a sequence. A single image can support a scene description; several labelled images can supply different views or successive moments. Video understanding uses sampled frames. The resulting explanation concerns the visible evidence; the model does not generate a replacement video, and this visual workflow does not analyse the video’s audio track.\n\nFor an event report, prepare a relevant clip or an ordered frame sequence and identify the zone or activity that matters. An illustrative brief is: “Describe the person’s visible actions near the entrance, in order. For each observation, give the object involved, its position and the change you can see. Put uncertain details in a separate final sentence.” You can request a short narrative, a list of observations or named report sections. These are instructions for the text answer, rather than a separate structured-output mode.\n\nThis snapshot’s published context window is 32,768 tokens, with a maximum input of 30,720 tokens and maximum output of 8,096 tokens. The dedicated model table also gives a maximum of 16,384 tokens for one image. Text instructions, conversation context and visual content must fit the input budget together; the output ceiling is a separate bound, not extra space for additional source material. When reviewing a long recording, isolate the episode and retain enough surrounding frames to make its sequence understandable.\n\nChoose source frames in which the relevant objects are distinguishable, label different cameras or views, and make the requested observation precise. 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. If the task depends on a spoken explanation, supply its transcript separately as text. Ask the report to separate a visible movement from an inferred purpose: “walked toward the door” is a different statement from an intention to leave. The useful deliverable is a reviewable text record tied to the footage and the stated observation criteria.", "editorialOverlay": { "copy_fields": { "practical_html": "Technical example in English
A surveillance-event report from frame notes\n\nUse these text frame notes only: 10:01 person stands beside entrance holding a box; 10:02 person places box beside entrance; 10:03 person walks out of frame without the box. Write an ordered event report with timestamps. Separate visible actions reported in the notes from unknown identity, intent and later events. No footage or audio is attached.A timestamped sequence of observations and a separate sentence identifying unsupported identity, intent and later outcome.The report does not label the act theft, infer spoken words or claim the person left the building.qwen-vl-plus-2025-07-10Qwen-VL-Plus 2025-07-10 is a visual-language snapshot focused on understanding surveillance video. It combines images or video frames with a written question to describe visible events, trace actions and produce a text report.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
Qwen-VL-Plus 2025-07-10 is Alibaba’s dated visual-language model for understanding images and video. Its defining update is stronger understanding of surveillance-video content. The model accepts visual material together with text instructions and returns text: an event description, an answer about the scene or a report organised around the details you ask it to inspect. Alibaba also names this snapshot qwen-vl-plus-0710.
The manufacturer distinguishes this edition from Qwen-VL-Plus 2025-05-07 and the Qwen-VL-Plus latest alias described on the same page. That distinction matters when your task specifically concerns camera footage: the surveillance-focused update belongs to July’s named snapshot. Keep the exact edition in the task record when comparing prompts or reviewing the same footage again.
Visual questions can concern what appears in a frame, where objects are positioned and how visible actions develop across a sequence. A single image can support a scene description; several labelled images can supply different views or successive moments. Video understanding uses sampled frames. The resulting explanation concerns the visible evidence; the model does not generate a replacement video, and this visual workflow does not analyse the video’s audio track.
For an event report, prepare a relevant clip or an ordered frame sequence and identify the zone or activity that matters. An illustrative brief is: “Describe the person’s visible actions near the entrance, in order. For each observation, give the object involved, its position and the change you can see. Put uncertain details in a separate final sentence.” You can request a short narrative, a list of observations or named report sections. These are instructions for the text answer, rather than a separate structured-output mode.
This snapshot’s published context window is 32,768 tokens, with a maximum input of 30,720 tokens and maximum output of 8,096 tokens. The dedicated model table also gives a maximum of 16,384 tokens for one image. Text instructions, conversation context and visual content must fit the input budget together; the output ceiling is a separate bound, not extra space for additional source material. When reviewing a long recording, isolate the episode and retain enough surrounding frames to make its sequence understandable.
Choose source frames in which the relevant objects are distinguishable, label different cameras or views, and make the requested observation precise. 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. If the task depends on a spoken explanation, supply its transcript separately as text. Ask the report to separate a visible movement from an inferred purpose: “walked toward the door” is a different statement from an intention to leave. The useful deliverable is a reviewable text record tied to the footage and the stated observation criteria.
POST /v1/chat/completionstexttext| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits qwen-vl-plus-2025-07-10 | 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 $0.000044 / 1K tokens Output · Output tokens $0.00044 / 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-2025-07-10", "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-2025-07-10',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-2025-07-10',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-2025-07-10
Use the method paths shown above.
qwen-vl-plus-2025-07-10Qwen-VL-Plus 2025-07-10 is a visual-language snapshot focused on understanding surveillance video. It combines images or video frames with a written question to describe visible events, trace actions and produce a text report.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
Qwen-VL-Plus 2025-07-10 is Alibaba’s dated visual-language model for understanding images and video. Its defining update is stronger understanding of surveillance-video content. The model accepts visual material together with text instructions and returns text: an event description, an answer about the scene or a report organised around the details you ask it to inspect. Alibaba also names this snapshot qwen-vl-plus-0710.
The manufacturer distinguishes this edition from Qwen-VL-Plus 2025-05-07 and the Qwen-VL-Plus latest alias described on the same page. That distinction matters when your task specifically concerns camera footage: the surveillance-focused update belongs to July’s named snapshot. Keep the exact edition in the task record when comparing prompts or reviewing the same footage again.
Visual questions can concern what appears in a frame, where objects are positioned and how visible actions develop across a sequence. A single image can support a scene description; several labelled images can supply different views or successive moments. Video understanding uses sampled frames. The resulting explanation concerns the visible evidence; the model does not generate a replacement video, and this visual workflow does not analyse the video’s audio track.
For an event report, prepare a relevant clip or an ordered frame sequence and identify the zone or activity that matters. An illustrative brief is: “Describe the person’s visible actions near the entrance, in order. For each observation, give the object involved, its position and the change you can see. Put uncertain details in a separate final sentence.” You can request a short narrative, a list of observations or named report sections. These are instructions for the text answer, rather than a separate structured-output mode.
This snapshot’s published context window is 32,768 tokens, with a maximum input of 30,720 tokens and maximum output of 8,096 tokens. The dedicated model table also gives a maximum of 16,384 tokens for one image. Text instructions, conversation context and visual content must fit the input budget together; the output ceiling is a separate bound, not extra space for additional source material. When reviewing a long recording, isolate the episode and retain enough surrounding frames to make its sequence understandable.
Choose source frames in which the relevant objects are distinguishable, label different cameras or views, and make the requested observation precise. 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. If the task depends on a spoken explanation, supply its transcript separately as text. Ask the report to separate a visible movement from an inferred purpose: “walked toward the door” is a different statement from an intention to leave. The useful deliverable is a reviewable text record tied to the footage and the stated observation criteria.
POST /v1/messagestextjson, sse| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits qwen-vl-plus-2025-07-10 | 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 $0.000044 / 1K tokens Output · Output tokens $0.00044 / 1K tokens Input · Cache read $0 / 1K tokens Input · Cache write $0 / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
{\n "model": "qwen-vl-plus-2025-07-10",\n "max_tokens": 128,\n "messages": [\n {\n "role": "user",\n "content": "Summarize the incident report."\n }\n ],\n "stream": false\n}set -eu\n: "${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-2025-07-10', '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-2025-07-10', '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-2025-07-10
Use the method paths shown above.
Technical example in English
A surveillance-event report from frame notes\n\nUse these text frame notes only: 10:01 person stands beside entrance holding a box; 10:02 person places box beside entrance; 10:03 person walks out of frame without the box. Write an ordered event report with timestamps. Separate visible actions reported in the notes from unknown identity, intent and later events. No footage or audio is attached.A timestamped sequence of observations and a separate sentence identifying unsupported identity, intent and later outcome.The report does not label the act theft, infer spoken words or claim the person left the building.