GLM-4-Flash is one of Zhipu AI’s separately named GLM-4 language-model profiles. The maker’s original GLM-4 API introduction lists Flash alongside GLM-4-0520, Air and AirX. Its current GLM-4 documentation also identifies Flash as the earlier language-model line from which separately named enhanced and dated Flash profiles developed.
Supply the question and the text needed to answer it. A compact request can ask for a short explanation, a summary of a message or a rewrite for a specified audience. State the information the answer should retain and the length or structure that makes it useful in the conversation.
For an illustrative support response, provide the user’s message and the approved facts for the reply. Ask for a concise answer that addresses the stated question, then keep the original message beside the draft. This keeps a later correction focused on a particular fact or phrase.
The producer’s wider GLM-4 guidance recommends semantic classification. For a small classification task, give the category definitions and a clear rule for an ambiguous message. Request the selected category with a brief explanation based on the supplied text.
An illustrative inbox task can distinguish a question, a requested change and an acknowledgement. Preserve the original message identifier with the returned decision. When a message contains more than one purpose, state how it should be represented in the requested result rather than relying on an unstated category rule.
The manufacturer’s GLM-4 conversation examples return written content from supplied messages. Choose a deliverable suited to a focused interaction: a one-paragraph answer, a short list of key points or a revised sentence that retains the approved wording of names and figures.
For summarising a passage, identify the facts that should survive the shorter version. For a rewrite, specify which details are fixed and which aspects of tone or structure may change. These preparation choices give the completed text a concrete purpose and leave the underlying material available for checking.
GLM-4-Flash, FlashX and Flash-250414 are separately named producer profiles. Retain the selected Flash name with the prompt and output when continuing the conversation or reviewing a set of responses. The next instruction can refer to the accepted draft and target a specific change, such as replacing an unclear phrase or restoring a missing qualifier.
Save the concise answer or classification result with its source identifier. Compare extracted names, values and category explanations with the input, then carry the corrected artifact into the next conversation or document step.
", "aboutText": "GLM-4-Flash is one of Zhipu AI’s separately named GLM-4 language-model profiles. The maker’s original GLM-4 API introduction lists Flash alongside GLM-4-0520, Air and AirX. Its current GLM-4 documentation also identifies Flash as the earlier language-model line from which separately named enhanced and dated Flash profiles developed.\n\n### Give a focused task a clear boundary\n\nSupply the question and the text needed to answer it. A compact request can ask for a short explanation, a summary of a message or a rewrite for a specified audience. State the information the answer should retain and the length or structure that makes it useful in the conversation.\n\nFor an illustrative support response, provide the user’s message and the approved facts for the reply. Ask for a concise answer that addresses the stated question, then keep the original message beside the draft. This keeps a later correction focused on a particular fact or phrase.\n\n### Define categories before asking for a decision\n\nThe producer’s wider GLM-4 guidance recommends semantic classification. For a small classification task, give the category definitions and a clear rule for an ambiguous message. Request the selected category with a brief explanation based on the supplied text.\n\nAn illustrative inbox task can distinguish a question, a requested change and an acknowledgement. Preserve the original message identifier with the returned decision. When a message contains more than one purpose, state how it should be represented in the requested result rather than relying on an unstated category rule.\n\n### Produce a concise text artifact\n\nThe manufacturer’s GLM-4 conversation examples return written content from supplied messages. Choose a deliverable suited to a focused interaction: a one-paragraph answer, a short list of key points or a revised sentence that retains the approved wording of names and figures.\n\nFor summarising a passage, identify the facts that should survive the shorter version. For a rewrite, specify which details are fixed and which aspects of tone or structure may change. These preparation choices give the completed text a concrete purpose and leave the underlying material available for checking.\n\n### Keep the Flash profile and the task together\n\nGLM-4-Flash, FlashX and Flash-250414 are separately named producer profiles. Retain the selected Flash name with the prompt and output when continuing the conversation or reviewing a set of responses. The next instruction can refer to the accepted draft and target a specific change, such as replacing an unclear phrase or restoring a missing qualifier.\n\nSave the concise answer or classification result with its source identifier. Compare extracted names, values and category explanations with the input, then carry the corrected artifact into the next conversation or document step. ", "editorialOverlay": { "copy_fields": { "practical_html": "Technical example in English
Draft a concise booking reply\n\nWrite a concise reply to this customer: "Can our group arrive at 09:00?" Approved facts: reception opens at 09:30; luggage may be left from 09:30; room access starts at 15:00. Keep the reply to two sentences. Answer the early-arrival question clearly without promising an earlier room or unattended luggage storage.A two-sentence customer reply using only the approved reception, luggage and room-access times.Check that 09:00 is not accepted and room access remains 15:00.glm-4-flashGLM-4-Flash is a GLM-4 language-model profile from Zhipu AI for written conversation and focused text tasks. Give it a clear question or compact source passage to prepare a short explanation, a classification decision or a concise rewritten response.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
GLM-4-Flash is one of Zhipu AI’s separately named GLM-4 language-model profiles. The maker’s original GLM-4 API introduction lists Flash alongside GLM-4-0520, Air and AirX. Its current GLM-4 documentation also identifies Flash as the earlier language-model line from which separately named enhanced and dated Flash profiles developed.
Supply the question and the text needed to answer it. A compact request can ask for a short explanation, a summary of a message or a rewrite for a specified audience. State the information the answer should retain and the length or structure that makes it useful in the conversation.
For an illustrative support response, provide the user’s message and the approved facts for the reply. Ask for a concise answer that addresses the stated question, then keep the original message beside the draft. This keeps a later correction focused on a particular fact or phrase.
The producer’s wider GLM-4 guidance recommends semantic classification. For a small classification task, give the category definitions and a clear rule for an ambiguous message. Request the selected category with a brief explanation based on the supplied text.
An illustrative inbox task can distinguish a question, a requested change and an acknowledgement. Preserve the original message identifier with the returned decision. When a message contains more than one purpose, state how it should be represented in the requested result rather than relying on an unstated category rule.
The manufacturer’s GLM-4 conversation examples return written content from supplied messages. Choose a deliverable suited to a focused interaction: a one-paragraph answer, a short list of key points or a revised sentence that retains the approved wording of names and figures.
For summarising a passage, identify the facts that should survive the shorter version. For a rewrite, specify which details are fixed and which aspects of tone or structure may change. These preparation choices give the completed text a concrete purpose and leave the underlying material available for checking.
GLM-4-Flash, FlashX and Flash-250414 are separately named producer profiles. Retain the selected Flash name with the prompt and output when continuing the conversation or reviewing a set of responses. The next instruction can refer to the accepted draft and target a specific change, such as replacing an unclear phrase or restoring a missing qualifier.
Save the concise answer or classification result with its source identifier. Compare extracted names, values and category explanations with the input, then carry the corrected artifact into the next conversation or document step.
POST /v1/chat/completionstexttext| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits glm-4-flash | 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 $0.000028 / 1K tokens Output · Output tokens $0.000028 / 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-4-flash", "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-4-flash',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-4-flash',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-4-flash
Use the method paths shown above.
glm-4-flashGLM-4-Flash is a GLM-4 language-model profile from Zhipu AI for written conversation and focused text tasks. Give it a clear question or compact source passage to prepare a short explanation, a classification decision or a concise rewritten response.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
GLM-4-Flash is one of Zhipu AI’s separately named GLM-4 language-model profiles. The maker’s original GLM-4 API introduction lists Flash alongside GLM-4-0520, Air and AirX. Its current GLM-4 documentation also identifies Flash as the earlier language-model line from which separately named enhanced and dated Flash profiles developed.
Supply the question and the text needed to answer it. A compact request can ask for a short explanation, a summary of a message or a rewrite for a specified audience. State the information the answer should retain and the length or structure that makes it useful in the conversation.
For an illustrative support response, provide the user’s message and the approved facts for the reply. Ask for a concise answer that addresses the stated question, then keep the original message beside the draft. This keeps a later correction focused on a particular fact or phrase.
The producer’s wider GLM-4 guidance recommends semantic classification. For a small classification task, give the category definitions and a clear rule for an ambiguous message. Request the selected category with a brief explanation based on the supplied text.
An illustrative inbox task can distinguish a question, a requested change and an acknowledgement. Preserve the original message identifier with the returned decision. When a message contains more than one purpose, state how it should be represented in the requested result rather than relying on an unstated category rule.
The manufacturer’s GLM-4 conversation examples return written content from supplied messages. Choose a deliverable suited to a focused interaction: a one-paragraph answer, a short list of key points or a revised sentence that retains the approved wording of names and figures.
For summarising a passage, identify the facts that should survive the shorter version. For a rewrite, specify which details are fixed and which aspects of tone or structure may change. These preparation choices give the completed text a concrete purpose and leave the underlying material available for checking.
GLM-4-Flash, FlashX and Flash-250414 are separately named producer profiles. Retain the selected Flash name with the prompt and output when continuing the conversation or reviewing a set of responses. The next instruction can refer to the accepted draft and target a specific change, such as replacing an unclear phrase or restoring a missing qualifier.
Save the concise answer or classification result with its source identifier. Compare extracted names, values and category explanations with the input, then carry the corrected artifact into the next conversation or document step.
POST /v1/messagestextjson, sse| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits glm-4-flash | 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 $0.000028 / 1K tokens Output · Output tokens $0.000028 / 1K tokens Input · Cache read $0 / 1K tokens Input · Cache write $0 / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
{\n "model": "glm-4-flash",\n "max_tokens": 128,\n "messages": [\n {\n "role": "user",\n "content": "Summarize the incident report."\n }\n ],\n "stream": false\n}set -eu\n: "${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-4-flash', '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-4-flash', '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-4-flash
Use the method paths shown above.
Technical example in English
Draft a concise booking reply\n\nWrite a concise reply to this customer: "Can our group arrive at 09:00?" Approved facts: reception opens at 09:30; luggage may be left from 09:30; room access starts at 15:00. Keep the reply to two sentences. Answer the early-arrival question clearly without promising an earlier room or unattended luggage storage.A two-sentence customer reply using only the approved reception, luggage and room-access times.Check that 09:00 is not accepted and room access remains 15:00.