GLM-4V is Zhipu AI’s vision-language model for combining an image with a written question. The manufacturer’s native multimodal example supplies both parts in one user message and returns a text response. Its agent-development guide likewise identifies GLM-4V as an image-to-text model, making the source picture and the wording of the question the central inputs to the task.
Choose the image that contains the evidence needed for the answer, then say what to examine. A general question can request a description of the scene; a focused question can identify an object, label or relationship to explain. Give enough context to disambiguate the task, such as which item in a product photograph matters or which region of a screenshot to describe.
The producer’s example uses a JPEG image encoded as an image reference alongside the text question. Preserve the original image when preparing the request, particularly if a crop would remove labels or surrounding context. For an illustrative support task, supply the screenshot and ask for a description of the visible state, keeping the exact message on the screen separate from a proposed explanation of what caused it.
Specify whether the result should be a caption, a short explanation, an ordered description or a working note with separate observations. For an inventory photograph, an illustrative brief is: “Describe the visible objects and their arrangement; identify the item indicated in my question.” For a document image, ask about the visible passage and retain the image or page identifier with the answer.
Keep the question narrow enough that the relevant evidence remains easy to inspect. If the next step requires a name, number or quoted label, ask for that item separately and compare the returned wording with the original picture.
The maker’s agent workflow associates the uploaded image with a named input and the accompanying prompt. Retain that association when continuing the discussion. A useful follow-up names the same image and narrows the question to a particular region or relationship, so the next answer refers to the intended material.
Save the completed text with the image identifier, question and any region reference used in the task. This produces a practical handoff for a visual assistance conversation, a catalogue description or a source-linked document note. The model’s role is interpreting the supplied image into language; the original picture remains available for checking specific details and correcting the next question.
", "aboutText": "GLM-4V is Zhipu AI’s vision-language model for combining an image with a written question. The manufacturer’s native multimodal example supplies both parts in one user message and returns a text response. Its agent-development guide likewise identifies GLM-4V as an image-to-text model, making the source picture and the wording of the question the central inputs to the task.\n\n### Put the question beside the image\n\nChoose the image that contains the evidence needed for the answer, then say what to examine. A general question can request a description of the scene; a focused question can identify an object, label or relationship to explain. Give enough context to disambiguate the task, such as which item in a product photograph matters or which region of a screenshot to describe.\n\nThe producer’s example uses a JPEG image encoded as an image reference alongside the text question. Preserve the original image when preparing the request, particularly if a crop would remove labels or surrounding context. For an illustrative support task, supply the screenshot and ask for a description of the visible state, keeping the exact message on the screen separate from a proposed explanation of what caused it.\n\n### Make the expected written artifact explicit\n\nSpecify whether the result should be a caption, a short explanation, an ordered description or a working note with separate observations. For an inventory photograph, an illustrative brief is: “Describe the visible objects and their arrangement; identify the item indicated in my question.” For a document image, ask about the visible passage and retain the image or page identifier with the answer.\n\nKeep the question narrow enough that the relevant evidence remains easy to inspect. If the next step requires a name, number or quoted label, ask for that item separately and compare the returned wording with the original picture.\n\n### Carry the image into a visual conversation\n\nThe maker’s agent workflow associates the uploaded image with a named input and the accompanying prompt. Retain that association when continuing the discussion. A useful follow-up names the same image and narrows the question to a particular region or relationship, so the next answer refers to the intended material.\n\nSave the completed text with the image identifier, question and any region reference used in the task. This produces a practical handoff for a visual assistance conversation, a catalogue description or a source-linked document note. The model’s role is interpreting the supplied image into language; the original picture remains available for checking specific details and correcting the next question.", "editorialOverlay": { "copy_fields": { "practical_html": "Technical example in English
Explain a transcribed screen state\n\nUse this screenshot transcription only: heading 'Export orders'; format 'CSV'; button 'Download' disabled; note 'Select at least one order'; selected count '0'. Write a two-sentence help message explaining the visible state and the next user step. Keep the visible note separate from any cause you cannot verify.Two help sentences grounded in the selected count, disabled button and selection note.The message asks the user to select an order and introduces no unsupported account, network or permission diagnosis.glm-4vGLM-4V is Zhipu AI’s vision-language model for asking questions about an image and receiving a written answer. Supply the image with a focused text prompt to create a description, interpret a visible detail or prepare an evidence-linked visual note.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
GLM-4V is Zhipu AI’s vision-language model for combining an image with a written question. The manufacturer’s native multimodal example supplies both parts in one user message and returns a text response. Its agent-development guide likewise identifies GLM-4V as an image-to-text model, making the source picture and the wording of the question the central inputs to the task.
Choose the image that contains the evidence needed for the answer, then say what to examine. A general question can request a description of the scene; a focused question can identify an object, label or relationship to explain. Give enough context to disambiguate the task, such as which item in a product photograph matters or which region of a screenshot to describe.
The producer’s example uses a JPEG image encoded as an image reference alongside the text question. Preserve the original image when preparing the request, particularly if a crop would remove labels or surrounding context. For an illustrative support task, supply the screenshot and ask for a description of the visible state, keeping the exact message on the screen separate from a proposed explanation of what caused it.
Specify whether the result should be a caption, a short explanation, an ordered description or a working note with separate observations. For an inventory photograph, an illustrative brief is: “Describe the visible objects and their arrangement; identify the item indicated in my question.” For a document image, ask about the visible passage and retain the image or page identifier with the answer.
Keep the question narrow enough that the relevant evidence remains easy to inspect. If the next step requires a name, number or quoted label, ask for that item separately and compare the returned wording with the original picture.
The maker’s agent workflow associates the uploaded image with a named input and the accompanying prompt. Retain that association when continuing the discussion. A useful follow-up names the same image and narrows the question to a particular region or relationship, so the next answer refers to the intended material.
Save the completed text with the image identifier, question and any region reference used in the task. This produces a practical handoff for a visual assistance conversation, a catalogue description or a source-linked document note. The model’s role is interpreting the supplied image into language; the original picture remains available for checking specific details and correcting the next question.
POST /v1/chat/completionstexttext| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits glm-4v | 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 $0.014 / 1K tokens Output · Output tokens $0.014 / 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", "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',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',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
Use the method paths shown above.
glm-4vGLM-4V is Zhipu AI’s vision-language model for asking questions about an image and receiving a written answer. Supply the image with a focused text prompt to create a description, interpret a visible detail or prepare an evidence-linked visual note.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
GLM-4V is Zhipu AI’s vision-language model for combining an image with a written question. The manufacturer’s native multimodal example supplies both parts in one user message and returns a text response. Its agent-development guide likewise identifies GLM-4V as an image-to-text model, making the source picture and the wording of the question the central inputs to the task.
Choose the image that contains the evidence needed for the answer, then say what to examine. A general question can request a description of the scene; a focused question can identify an object, label or relationship to explain. Give enough context to disambiguate the task, such as which item in a product photograph matters or which region of a screenshot to describe.
The producer’s example uses a JPEG image encoded as an image reference alongside the text question. Preserve the original image when preparing the request, particularly if a crop would remove labels or surrounding context. For an illustrative support task, supply the screenshot and ask for a description of the visible state, keeping the exact message on the screen separate from a proposed explanation of what caused it.
Specify whether the result should be a caption, a short explanation, an ordered description or a working note with separate observations. For an inventory photograph, an illustrative brief is: “Describe the visible objects and their arrangement; identify the item indicated in my question.” For a document image, ask about the visible passage and retain the image or page identifier with the answer.
Keep the question narrow enough that the relevant evidence remains easy to inspect. If the next step requires a name, number or quoted label, ask for that item separately and compare the returned wording with the original picture.
The maker’s agent workflow associates the uploaded image with a named input and the accompanying prompt. Retain that association when continuing the discussion. A useful follow-up names the same image and narrows the question to a particular region or relationship, so the next answer refers to the intended material.
Save the completed text with the image identifier, question and any region reference used in the task. This produces a practical handoff for a visual assistance conversation, a catalogue description or a source-linked document note. The model’s role is interpreting the supplied image into language; the original picture remains available for checking specific details and correcting the next question.
POST /v1/messagestextjson, sse| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits glm-4v | 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 $0.014 / 1K tokens Output · Output tokens $0.014 / 1K tokens Input · Cache read $0 / 1K tokens Input · Cache write $0 / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
{\n "model": "glm-4v",\n "max_tokens": 128,\n "messages": [\n {\n "role": "user",\n "content": "Summarize the incident report."\n }\n ],\n "stream": false\n}set -eu\n: "${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', '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', '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
Use the method paths shown above.
Technical example in English
Explain a transcribed screen state\n\nUse this screenshot transcription only: heading 'Export orders'; format 'CSV'; button 'Download' disabled; note 'Select at least one order'; selected count '0'. Write a two-sentence help message explaining the visible state and the next user step. Keep the visible note separate from any cause you cannot verify.Two help sentences grounded in the selected count, disabled button and selection note.The message asks the user to select an order and introduces no unsupported account, network or permission diagnosis.