GLM-Z1-Rumination-32B-0414 is the research-oriented reasoning edition in Z.ai’s GLM-4-0414 series. It is designed to spend longer on open-ended questions and use retrieved information during the reasoning process. The maker describes comparative research writing and complex retrieval as characteristic tasks. The final artifact is text, such as an analytical report that connects a question with the evidence gathered for it.
Give the topic, scope and comparison criteria before collecting material. For a report comparing development plans in two cities, specify the cities, the period and the aspects to compare. Ask for the relevant evidence, the points of comparison and a distinct conclusion. An open-ended task becomes easier to review when the expected sections and source requirements are explicit.
Provide existing documents or excerpts with clear titles and identifiers. Separate source material from instructions, and state how the answer should handle incomplete or conflicting evidence. A report can distinguish a documented fact from an interpretation and explain which source supports each material point.
The maker’s worked tool loop includes searching by keyword, opening a search result, opening a specified URL and finishing information gathering before writing. The model selects an action and arguments; the surrounding application executes the retrieval and supplies the observation. This is a research workflow built around the model, rather than an assumption that a text response has already browsed the web.
For an application using that pattern, return the retrieved page’s relevant content with its title and URL. Keep the source identifiers consistent across turns, so a later comparison refers to the same documents. Ask the model to refine a search when the observation does not answer the research question, and to produce the final report once the requested evidence has been collected.
The published example uses mock retrieval functions to illustrate the control flow. A working application supplies its own retrieval implementations and decides when the loop has gathered enough information. The example’s turn bound and generation budget describe that example; they are not universal limits of this edition.
Request an answer organized around the actual research problem: findings, comparison, uncertainty and conclusion. For a document comparison, ask the model to identify which conditions differ before proposing an interpretation. For a research brief, define the audience and the questions it must answer so the returned text stays tied to the task.
Review the report alongside its source observations. Check quotations, dates and causal claims against the material supplied to the loop. Retain the question, retrieved passages and final result together when a report will be revised later. This preserves the evidence needed to understand how the conclusion was reached.
This entry retains the Rumination 32B 0414 identity. The ordinary GLM-Z1-32B-0414 reasoning edition and the smaller Z1 edition are separately named releases. The maker’s Rumination card establishes the research-oriented tool pattern and inference example; its sparse limit information should be supplemented by the actual interface contract when preparing a long request.
", "aboutText": "GLM-Z1-Rumination-32B-0414 is the research-oriented reasoning edition in Z.ai’s GLM-4-0414 series. It is designed to spend longer on open-ended questions and use retrieved information during the reasoning process. The maker describes comparative research writing and complex retrieval as characteristic tasks. The final artifact is text, such as an analytical report that connects a question with the evidence gathered for it.\n\n### Define the research question\n\nGive the topic, scope and comparison criteria before collecting material. For a report comparing development plans in two cities, specify the cities, the period and the aspects to compare. Ask for the relevant evidence, the points of comparison and a distinct conclusion. An open-ended task becomes easier to review when the expected sections and source requirements are explicit.\n\nProvide existing documents or excerpts with clear titles and identifiers. Separate source material from instructions, and state how the answer should handle incomplete or conflicting evidence. A report can distinguish a documented fact from an interpretation and explain which source supports each material point.\n\n### Search, read and return observations\n\nThe maker’s worked tool loop includes searching by keyword, opening a search result, opening a specified URL and finishing information gathering before writing. The model selects an action and arguments; the surrounding application executes the retrieval and supplies the observation. This is a research workflow built around the model, rather than an assumption that a text response has already browsed the web.\n\nFor an application using that pattern, return the retrieved page’s relevant content with its title and URL. Keep the source identifiers consistent across turns, so a later comparison refers to the same documents. Ask the model to refine a search when the observation does not answer the research question, and to produce the final report once the requested evidence has been collected.\n\nThe published example uses mock retrieval functions to illustrate the control flow. A working application supplies its own retrieval implementations and decides when the loop has gathered enough information. The example’s turn bound and generation budget describe that example; they are not universal limits of this edition.\n\n### Prepare and review the report\n\nRequest an answer organized around the actual research problem: findings, comparison, uncertainty and conclusion. For a document comparison, ask the model to identify which conditions differ before proposing an interpretation. For a research brief, define the audience and the questions it must answer so the returned text stays tied to the task.\n\nReview the report alongside its source observations. Check quotations, dates and causal claims against the material supplied to the loop. Retain the question, retrieved passages and final result together when a report will be revised later. This preserves the evidence needed to understand how the conclusion was reached.\n\n### Preserve the fixed edition\n\nThis entry retains the Rumination 32B 0414 identity. The ordinary GLM-Z1-32B-0414 reasoning edition and the smaller Z1 edition are separately named releases. The maker’s Rumination card establishes the research-oriented tool pattern and inference example; its sparse limit information should be supplemented by the actual interface contract when preparing a long request.", "editorialOverlay": { "copy_fields": { "practical_html": "Technical example in English
Synthesize a source-bounded pilot comparison\n\nWrite a short research comparison from these supplied excerpts. Pilot A: staff reported faster intake, but no timing measurement was collected. Pilot B: median intake fell from 12 to 9 minutes; error counts were not recorded. Compare evidence for speed and quality, cite A or B, and state what follow-up measurement is needed before a broad conclusion.A concise comparison with findings, evidence gaps and a limited conclusion grounded in the excerpts.The report distinguishes reported impressions from measured timing and does not claim improved quality without error data.THUDM/GLM-Z1-Rumination-32B-0414GLM-Z1-Rumination-32B-0414 is a fixed text-reasoning edition for open-ended research and comparative reports. Its maker documents a search-and-reading loop in which the application retrieves sources and returns observations before the model writes its result.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
GLM-Z1-Rumination-32B-0414 is the research-oriented reasoning edition in Z.ai’s GLM-4-0414 series. It is designed to spend longer on open-ended questions and use retrieved information during the reasoning process. The maker describes comparative research writing and complex retrieval as characteristic tasks. The final artifact is text, such as an analytical report that connects a question with the evidence gathered for it.
Give the topic, scope and comparison criteria before collecting material. For a report comparing development plans in two cities, specify the cities, the period and the aspects to compare. Ask for the relevant evidence, the points of comparison and a distinct conclusion. An open-ended task becomes easier to review when the expected sections and source requirements are explicit.
Provide existing documents or excerpts with clear titles and identifiers. Separate source material from instructions, and state how the answer should handle incomplete or conflicting evidence. A report can distinguish a documented fact from an interpretation and explain which source supports each material point.
The maker’s worked tool loop includes searching by keyword, opening a search result, opening a specified URL and finishing information gathering before writing. The model selects an action and arguments; the surrounding application executes the retrieval and supplies the observation. This is a research workflow built around the model, rather than an assumption that a text response has already browsed the web.
For an application using that pattern, return the retrieved page’s relevant content with its title and URL. Keep the source identifiers consistent across turns, so a later comparison refers to the same documents. Ask the model to refine a search when the observation does not answer the research question, and to produce the final report once the requested evidence has been collected.
The published example uses mock retrieval functions to illustrate the control flow. A working application supplies its own retrieval implementations and decides when the loop has gathered enough information. The example’s turn bound and generation budget describe that example; they are not universal limits of this edition.
Request an answer organized around the actual research problem: findings, comparison, uncertainty and conclusion. For a document comparison, ask the model to identify which conditions differ before proposing an interpretation. For a research brief, define the audience and the questions it must answer so the returned text stays tied to the task.
Review the report alongside its source observations. Check quotations, dates and causal claims against the material supplied to the loop. Retain the question, retrieved passages and final result together when a report will be revised later. This preserves the evidence needed to understand how the conclusion was reached.
This entry retains the Rumination 32B 0414 identity. The ordinary GLM-Z1-32B-0414 reasoning edition and the smaller Z1 edition are separately named releases. The maker’s Rumination card establishes the research-oriented tool pattern and inference example; its sparse limit information should be supplemented by the actual interface contract when preparing a long request.
POST /v1/chat/completionstexttext| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits THUDM/GLM-Z1-Rumination-32B-0414 | 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 $0.00014 / 1K tokens Output · Output tokens $0.00014 / 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": "THUDM/GLM-Z1-Rumination-32B-0414", "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='THUDM/GLM-Z1-Rumination-32B-0414',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='THUDM/GLM-Z1-Rumination-32B-0414',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 THUDM/GLM-Z1-Rumination-32B-0414
Use the method paths shown above.
THUDM/GLM-Z1-Rumination-32B-0414GLM-Z1-Rumination-32B-0414 is a fixed text-reasoning edition for open-ended research and comparative reports. Its maker documents a search-and-reading loop in which the application retrieves sources and returns observations before the model writes its result.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
GLM-Z1-Rumination-32B-0414 is the research-oriented reasoning edition in Z.ai’s GLM-4-0414 series. It is designed to spend longer on open-ended questions and use retrieved information during the reasoning process. The maker describes comparative research writing and complex retrieval as characteristic tasks. The final artifact is text, such as an analytical report that connects a question with the evidence gathered for it.
Give the topic, scope and comparison criteria before collecting material. For a report comparing development plans in two cities, specify the cities, the period and the aspects to compare. Ask for the relevant evidence, the points of comparison and a distinct conclusion. An open-ended task becomes easier to review when the expected sections and source requirements are explicit.
Provide existing documents or excerpts with clear titles and identifiers. Separate source material from instructions, and state how the answer should handle incomplete or conflicting evidence. A report can distinguish a documented fact from an interpretation and explain which source supports each material point.
The maker’s worked tool loop includes searching by keyword, opening a search result, opening a specified URL and finishing information gathering before writing. The model selects an action and arguments; the surrounding application executes the retrieval and supplies the observation. This is a research workflow built around the model, rather than an assumption that a text response has already browsed the web.
For an application using that pattern, return the retrieved page’s relevant content with its title and URL. Keep the source identifiers consistent across turns, so a later comparison refers to the same documents. Ask the model to refine a search when the observation does not answer the research question, and to produce the final report once the requested evidence has been collected.
The published example uses mock retrieval functions to illustrate the control flow. A working application supplies its own retrieval implementations and decides when the loop has gathered enough information. The example’s turn bound and generation budget describe that example; they are not universal limits of this edition.
Request an answer organized around the actual research problem: findings, comparison, uncertainty and conclusion. For a document comparison, ask the model to identify which conditions differ before proposing an interpretation. For a research brief, define the audience and the questions it must answer so the returned text stays tied to the task.
Review the report alongside its source observations. Check quotations, dates and causal claims against the material supplied to the loop. Retain the question, retrieved passages and final result together when a report will be revised later. This preserves the evidence needed to understand how the conclusion was reached.
This entry retains the Rumination 32B 0414 identity. The ordinary GLM-Z1-32B-0414 reasoning edition and the smaller Z1 edition are separately named releases. The maker’s Rumination card establishes the research-oriented tool pattern and inference example; its sparse limit information should be supplemented by the actual interface contract when preparing a long request.
POST /v1/messagestextjson, sse| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits THUDM/GLM-Z1-Rumination-32B-0414 | 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 $0.00014 / 1K tokens Output · Output tokens $0.00014 / 1K tokens Input · Cache read $0 / 1K tokens Input · Cache write $0 / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
{\n "model": "THUDM/GLM-Z1-Rumination-32B-0414",\n "max_tokens": 128,\n "messages": [\n {\n "role": "user",\n "content": "Summarize the incident report."\n }\n ],\n "stream": false\n}set -eu\n: "${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': 'THUDM/GLM-Z1-Rumination-32B-0414', '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': 'THUDM/GLM-Z1-Rumination-32B-0414', '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 THUDM/GLM-Z1-Rumination-32B-0414
Use the method paths shown above.
Technical example in English
Synthesize a source-bounded pilot comparison\n\nWrite a short research comparison from these supplied excerpts. Pilot A: staff reported faster intake, but no timing measurement was collected. Pilot B: median intake fell from 12 to 9 minutes; error counts were not recorded. Compare evidence for speed and quality, cite A or B, and state what follow-up measurement is needed before a broad conclusion.A concise comparison with findings, evidence gaps and a limited conclusion grounded in the excerpts.The report distinguishes reported impressions from measured timing and does not claim improved quality without error data.