GLM-4V-Plus is Zhipu AI’s multimodal model for interpreting images or video and producing text. The manufacturer’s agent-development guide explicitly distinguishes it from GLM-4V’s image-to-text workflow: the Plus profile accepts image or video material with a written prompt. Use that combination to turn a visual question into a source-linked description, explanation or working note.
Provide the picture or video containing the event, object or relationship to examine, and state the purpose of the task. An image question can focus on a visible arrangement or a detail in a document. A video question can ask for an explanation of the action shown or a summary of the supplied sequence.
Keep the reference identity with the question. For a video review, name the sequence and, when useful, the time interval you want considered. An illustrative instruction is: “Describe the action in the selected interval and explain how the visible sequence supports your description.” A time reference in the brief identifies the requested material; it does not replace the video input.
The maker documents two agent workflows: a conversational agent that associates an uploaded image or video with the prompt, and a text-oriented agent with explicitly named upload inputs. In the latter workflow, several visual inputs can be configured through separate named input fields. Give each reference a clear role, such as an initial state, a later state or a document to inspect.
For an illustrative comparison, label the references before asking what changes. Request a written comparison that names the source for each observation. This makes the result usable as a review note and keeps a follow-up about a particular image or sequence attached to the correct reference.
Define the artifact the next step needs: a caption, an explanation, a sequence summary or a list of observations linked to source references. Ask the model to distinguish what is visible from what requires additional context. For a document question, retain the page identity with the returned interpretation; for an action sequence, keep the selected interval and the original clip beside the summary.
The output of the documented GLM-4V-Plus workflow is text. Preserve that response with the prompt and the input references when preparing a visual knowledge record or handing the review to another person. Inspect names, labels and event descriptions against the source material, then make the next request about the specific observation that needs clarification.
GLM-4V and GLM-4V-Plus are separately named producer profiles. Choose Plus for a task whose supplied evidence includes video, and retain that selection with the completed note. A useful continuation refers to the same material, narrows the requested observation and preserves the earlier answer as part of the working record.
", "aboutText": "GLM-4V-Plus is Zhipu AI’s multimodal model for interpreting images or video and producing text. The manufacturer’s agent-development guide explicitly distinguishes it from GLM-4V’s image-to-text workflow: the Plus profile accepts image or video material with a written prompt. Use that combination to turn a visual question into a source-linked description, explanation or working note.\n\n### Select the visual evidence for the question\n\nProvide the picture or video containing the event, object or relationship to examine, and state the purpose of the task. An image question can focus on a visible arrangement or a detail in a document. A video question can ask for an explanation of the action shown or a summary of the supplied sequence.\n\nKeep the reference identity with the question. For a video review, name the sequence and, when useful, the time interval you want considered. An illustrative instruction is: “Describe the action in the selected interval and explain how the visible sequence supports your description.” A time reference in the brief identifies the requested material; it does not replace the video input.\n\n### Associate each input with its role\n\nThe maker documents two agent workflows: a conversational agent that associates an uploaded image or video with the prompt, and a text-oriented agent with explicitly named upload inputs. In the latter workflow, several visual inputs can be configured through separate named input fields. Give each reference a clear role, such as an initial state, a later state or a document to inspect.\n\nFor an illustrative comparison, label the references before asking what changes. Request a written comparison that names the source for each observation. This makes the result usable as a review note and keeps a follow-up about a particular image or sequence attached to the correct reference.\n\n### Ask for a useful written result\n\nDefine the artifact the next step needs: a caption, an explanation, a sequence summary or a list of observations linked to source references. Ask the model to distinguish what is visible from what requires additional context. For a document question, retain the page identity with the returned interpretation; for an action sequence, keep the selected interval and the original clip beside the summary.\n\nThe output of the documented GLM-4V-Plus workflow is text. Preserve that response with the prompt and the input references when preparing a visual knowledge record or handing the review to another person. Inspect names, labels and event descriptions against the source material, then make the next request about the specific observation that needs clarification.\n\n### Keep the selected visual profile with the result\n\nGLM-4V and GLM-4V-Plus are separately named producer profiles. Choose Plus for a task whose supplied evidence includes video, and retain that selection with the completed note. A useful continuation refers to the same material, narrows the requested observation and preserves the earlier answer as part of the working record.", "editorialOverlay": { "copy_fields": { "practical_html": "Technical example in English
Compare labelled inspection notes\n\nUse only these text inspection notes. Before: crate latch closed, seal present, label K7. After: latch open, seal missing, label K7. Write a comparison for the receiving team. Distinguish documented changes from unknown causes and list the next evidence to request. No image or video is attached.A before/after comparison with source labels, an unknown-cause note and a short evidence request.The latch and seal changes are reported, while damage, theft and the person responsible remain unasserted.glm-4v-plusGLM-4V-Plus is Zhipu AI’s model for interpreting images or video together with a text prompt and returning a written response. Choose the video-capable Plus profile for a sequence summary or a written explanation of the action shown.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
GLM-4V-Plus is Zhipu AI’s multimodal model for interpreting images or video and producing text. The manufacturer’s agent-development guide explicitly distinguishes it from GLM-4V’s image-to-text workflow: the Plus profile accepts image or video material with a written prompt. Use that combination to turn a visual question into a source-linked description, explanation or working note.
Provide the picture or video containing the event, object or relationship to examine, and state the purpose of the task. An image question can focus on a visible arrangement or a detail in a document. A video question can ask for an explanation of the action shown or a summary of the supplied sequence.
Keep the reference identity with the question. For a video review, name the sequence and, when useful, the time interval you want considered. An illustrative instruction is: “Describe the action in the selected interval and explain how the visible sequence supports your description.” A time reference in the brief identifies the requested material; it does not replace the video input.
The maker documents two agent workflows: a conversational agent that associates an uploaded image or video with the prompt, and a text-oriented agent with explicitly named upload inputs. In the latter workflow, several visual inputs can be configured through separate named input fields. Give each reference a clear role, such as an initial state, a later state or a document to inspect.
For an illustrative comparison, label the references before asking what changes. Request a written comparison that names the source for each observation. This makes the result usable as a review note and keeps a follow-up about a particular image or sequence attached to the correct reference.
Define the artifact the next step needs: a caption, an explanation, a sequence summary or a list of observations linked to source references. Ask the model to distinguish what is visible from what requires additional context. For a document question, retain the page identity with the returned interpretation; for an action sequence, keep the selected interval and the original clip beside the summary.
The output of the documented GLM-4V-Plus workflow is text. Preserve that response with the prompt and the input references when preparing a visual knowledge record or handing the review to another person. Inspect names, labels and event descriptions against the source material, then make the next request about the specific observation that needs clarification.
GLM-4V and GLM-4V-Plus are separately named producer profiles. Choose Plus for a task whose supplied evidence includes video, and retain that selection with the completed note. A useful continuation refers to the same material, narrows the requested observation and preserves the earlier answer as part of the working record.
POST /v1/chat/completionstexttext| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits glm-4v-plus | 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 $0.0028 / 1K tokens Output · Output tokens $0.0028 / 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": "glm-4v-plus", "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='glm-4v-plus',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='glm-4v-plus',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 glm-4v-plus
Use the method paths shown above.
glm-4v-plusGLM-4V-Plus is Zhipu AI’s model for interpreting images or video together with a text prompt and returning a written response. Choose the video-capable Plus profile for a sequence summary or a written explanation of the action shown.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
GLM-4V-Plus is Zhipu AI’s multimodal model for interpreting images or video and producing text. The manufacturer’s agent-development guide explicitly distinguishes it from GLM-4V’s image-to-text workflow: the Plus profile accepts image or video material with a written prompt. Use that combination to turn a visual question into a source-linked description, explanation or working note.
Provide the picture or video containing the event, object or relationship to examine, and state the purpose of the task. An image question can focus on a visible arrangement or a detail in a document. A video question can ask for an explanation of the action shown or a summary of the supplied sequence.
Keep the reference identity with the question. For a video review, name the sequence and, when useful, the time interval you want considered. An illustrative instruction is: “Describe the action in the selected interval and explain how the visible sequence supports your description.” A time reference in the brief identifies the requested material; it does not replace the video input.
The maker documents two agent workflows: a conversational agent that associates an uploaded image or video with the prompt, and a text-oriented agent with explicitly named upload inputs. In the latter workflow, several visual inputs can be configured through separate named input fields. Give each reference a clear role, such as an initial state, a later state or a document to inspect.
For an illustrative comparison, label the references before asking what changes. Request a written comparison that names the source for each observation. This makes the result usable as a review note and keeps a follow-up about a particular image or sequence attached to the correct reference.
Define the artifact the next step needs: a caption, an explanation, a sequence summary or a list of observations linked to source references. Ask the model to distinguish what is visible from what requires additional context. For a document question, retain the page identity with the returned interpretation; for an action sequence, keep the selected interval and the original clip beside the summary.
The output of the documented GLM-4V-Plus workflow is text. Preserve that response with the prompt and the input references when preparing a visual knowledge record or handing the review to another person. Inspect names, labels and event descriptions against the source material, then make the next request about the specific observation that needs clarification.
GLM-4V and GLM-4V-Plus are separately named producer profiles. Choose Plus for a task whose supplied evidence includes video, and retain that selection with the completed note. A useful continuation refers to the same material, narrows the requested observation and preserves the earlier answer as part of the working record.
POST /v1/messagestextjson, sse| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits glm-4v-plus | 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 $0.0028 / 1K tokens Output · Output tokens $0.0028 / 1K tokens Input · Cache read $0 / 1K tokens Input · Cache write $0 / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
{\n "model": "glm-4v-plus",\n "max_tokens": 128,\n "messages": [\n {\n "role": "user",\n "content": "Summarize the incident report."\n }\n ],\n "stream": false\n}set -eu\n: "${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': 'glm-4v-plus', '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': 'glm-4v-plus', '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 glm-4v-plus
Use the method paths shown above.
Technical example in English
Compare labelled inspection notes\n\nUse only these text inspection notes. Before: crate latch closed, seal present, label K7. After: latch open, seal missing, label K7. Write a comparison for the receiving team. Distinguish documented changes from unknown causes and list the next evidence to request. No image or video is attached.A before/after comparison with source labels, an unknown-cause note and a short evidence request.The latch and seal changes are reported, while damage, theft and the person responsible remain unasserted.