GLM-Z1-32B-0414 is the reasoning-focused member of Z.ai’s GLM-4-0414 series. It is developed from GLM-4-32B-0414 through cold-start preparation and extended reinforcement learning, with further work on mathematics, code and logic. Pairwise-ranking feedback is also used to strengthen general capabilities. The input is text, and the visible result is a text solution, explanation or program fragment.
Its purpose is a task that needs connected intermediate conclusions rather than an immediate sentence. Supply all assumptions and define the required endpoint: derive a formula, explain an algorithm, justify a bound or prepare a code change. The maker’s mathematical example concerns positive variables linked by an equation and the possible range of their sum. A useful response should identify the conditions, show the relevant transitions and clearly separate the final result.
GLM-Z1-Rumination-32B-0414 is a separate edition for more open-ended research with search tools; GLM-Z1-9B-0414 is another size. This entry preserves the named Z1 32B release instead of importing those editions’ extra workflow or size characteristics.
The manufacturer recommends temperature 0.6, top_p 0.95 and top_k 40. It also recommends a max_new_tokens budget of 30,000 to leave room for thinking. These are recommended generation settings, not a guarantee that every answer needs that length or a documented universal hard output ceiling. The card’s separate inference example uses a smaller request-specific generation budget.
For a reasoning task, allow enough room for the analysis and the final answer together, and define the final answer’s form in the prompt. A mathematical question can request the range or value separately from the explanation; a coding question can request one implementation followed by the assumptions that matter to it. Keep the requested result specific enough to check against the supplied conditions.
The maker’s template starts the generation with the thinking marker <think>. Its published chat template injects this marker, so an implementation should use the release’s actual template rather than assuming an unrelated dialogue format.
For subsequent turns, the usage guide retains the final user-visible reply and removes the internal thinking material from saved dialogue history. This is a preparation rule for the model’s own conversation template. A follow-up should contain the current question, necessary source material and relevant previous answer, not an unfiltered accumulation of all earlier internal analysis.
The published configuration sets max_position_embeddings to 32,768 and does not include a rope_scaling entry. The usage guide recommends considering YaRN when input exceeds 8,192 tokens. Its example scaling configuration uses factor 4.0 with original_max_position_embeddings set to 32,768. The recommendation threshold, the base configuration and the optional extension are different facts; they are not interchangeable response limits.
Static YaRN affects all text and may slightly reduce performance on short inputs, so the manufacturer recommends enabling it when needed. For long material, provide the relevant definitions and passages in a clearly separated input and leave a suitable generation budget in the active context. The configuration should follow the selected deployment’s support for the maker’s scaling procedure rather than treating an arithmetic multiple as an automatically available context window.
The model’s inference example uses a user question and the release-specific chat template. For mathematics, preserve variable definitions and state the proposition to establish. For code, provide the relevant fragment, its required behavior and the case under discussion. Ask for a distinct final artifact—proof, formula, implementation or explanation—so the reasoning can be evaluated through the task’s result. The dedicated Z1 guide specifies text reasoning and its preparation; it does not establish an additional native tool contract or image input for this edition.
", "aboutText": "### Introduction\n\nGLM-Z1-32B-0414 is the reasoning-focused member of Z.ai’s GLM-4-0414 series. It is developed from GLM-4-32B-0414 through cold-start preparation and extended reinforcement learning, with further work on mathematics, code and logic. Pairwise-ranking feedback is also used to strengthen general capabilities. The input is text, and the visible result is a text solution, explanation or program fragment.\n\nIts purpose is a task that needs connected intermediate conclusions rather than an immediate sentence. Supply all assumptions and define the required endpoint: derive a formula, explain an algorithm, justify a bound or prepare a code change. The maker’s mathematical example concerns positive variables linked by an equation and the possible range of their sum. A useful response should identify the conditions, show the relevant transitions and clearly separate the final result.\n\nGLM-Z1-Rumination-32B-0414 is a separate edition for more open-ended research with search tools; GLM-Z1-9B-0414 is another size. This entry preserves the named Z1 32B release instead of importing those editions’ extra workflow or size characteristics.\n\n### Usage guidelines: sampling\n\nThe manufacturer recommends temperature 0.6, top_p 0.95 and top_k 40. It also recommends a max_new_tokens budget of 30,000 to leave room for thinking. These are recommended generation settings, not a guarantee that every answer needs that length or a documented universal hard output ceiling. The card’s separate inference example uses a smaller request-specific generation budget.\n\nFor a reasoning task, allow enough room for the analysis and the final answer together, and define the final answer’s form in the prompt. A mathematical question can request the range or value separately from the explanation; a coding question can request one implementation followed by the assumptions that matter to it. Keep the requested result specific enough to check against the supplied conditions.\n\n### Usage guidelines: thinking and dialogue history\n\nThe maker’s template starts the generation with the thinking marker `Technical example in English
Derive a safe batch bound\n\nA buffer holds at most 96 records. Every batch adds b records, where b is a positive integer, and 12 records must remain free. Derive the largest number of complete batches that fit as a formula in b. Explain the rounding and calculate the result for b = 10, keeping the reserved space separate from usable capacity.A formula using a floor, an explanation of usable capacity and a checked numeric example.Verify the formula reserves 12 records and a further full batch would exceed usable capacity in the example.THUDM/GLM-Z1-32B-0414GLM-Z1-32B-0414 is Z.ai’s fixed text-reasoning edition for mathematics, programming and logic. Its maker supplies explicit guidance for thinking, dialogue history, sampling and optional long-context scaling, so complex tasks can be prepared for this particular release.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
GLM-Z1-32B-0414 is the reasoning-focused member of Z.ai’s GLM-4-0414 series. It is developed from GLM-4-32B-0414 through cold-start preparation and extended reinforcement learning, with further work on mathematics, code and logic. Pairwise-ranking feedback is also used to strengthen general capabilities. The input is text, and the visible result is a text solution, explanation or program fragment.
Its purpose is a task that needs connected intermediate conclusions rather than an immediate sentence. Supply all assumptions and define the required endpoint: derive a formula, explain an algorithm, justify a bound or prepare a code change. The maker’s mathematical example concerns positive variables linked by an equation and the possible range of their sum. A useful response should identify the conditions, show the relevant transitions and clearly separate the final result.
GLM-Z1-Rumination-32B-0414 is a separate edition for more open-ended research with search tools; GLM-Z1-9B-0414 is another size. This entry preserves the named Z1 32B release instead of importing those editions’ extra workflow or size characteristics.
The manufacturer recommends temperature 0.6, top_p 0.95 and top_k 40. It also recommends a max_new_tokens budget of 30,000 to leave room for thinking. These are recommended generation settings, not a guarantee that every answer needs that length or a documented universal hard output ceiling. The card’s separate inference example uses a smaller request-specific generation budget.
For a reasoning task, allow enough room for the analysis and the final answer together, and define the final answer’s form in the prompt. A mathematical question can request the range or value separately from the explanation; a coding question can request one implementation followed by the assumptions that matter to it. Keep the requested result specific enough to check against the supplied conditions.
The maker’s template starts the generation with the thinking marker <think>. Its published chat template injects this marker, so an implementation should use the release’s actual template rather than assuming an unrelated dialogue format.
For subsequent turns, the usage guide retains the final user-visible reply and removes the internal thinking material from saved dialogue history. This is a preparation rule for the model’s own conversation template. A follow-up should contain the current question, necessary source material and relevant previous answer, not an unfiltered accumulation of all earlier internal analysis.
The published configuration sets max_position_embeddings to 32,768 and does not include a rope_scaling entry. The usage guide recommends considering YaRN when input exceeds 8,192 tokens. Its example scaling configuration uses factor 4.0 with original_max_position_embeddings set to 32,768. The recommendation threshold, the base configuration and the optional extension are different facts; they are not interchangeable response limits.
Static YaRN affects all text and may slightly reduce performance on short inputs, so the manufacturer recommends enabling it when needed. For long material, provide the relevant definitions and passages in a clearly separated input and leave a suitable generation budget in the active context. The configuration should follow the selected deployment’s support for the maker’s scaling procedure rather than treating an arithmetic multiple as an automatically available context window.
The model’s inference example uses a user question and the release-specific chat template. For mathematics, preserve variable definitions and state the proposition to establish. For code, provide the relevant fragment, its required behavior and the case under discussion. Ask for a distinct final artifact—proof, formula, implementation or explanation—so the reasoning can be evaluated through the task’s result. The dedicated Z1 guide specifies text reasoning and its preparation; it does not establish an additional native tool contract or image input for this edition.
POST /v1/chat/completionstexttext| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits THUDM/GLM-Z1-32B-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-Z1-32B-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-Z1-32B-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-Z1-32B-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-Z1-32B-0414
Use the method paths shown above.
THUDM/GLM-Z1-32B-0414GLM-Z1-32B-0414 is Z.ai’s fixed text-reasoning edition for mathematics, programming and logic. Its maker supplies explicit guidance for thinking, dialogue history, sampling and optional long-context scaling, so complex tasks can be prepared for this particular release.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
GLM-Z1-32B-0414 is the reasoning-focused member of Z.ai’s GLM-4-0414 series. It is developed from GLM-4-32B-0414 through cold-start preparation and extended reinforcement learning, with further work on mathematics, code and logic. Pairwise-ranking feedback is also used to strengthen general capabilities. The input is text, and the visible result is a text solution, explanation or program fragment.
Its purpose is a task that needs connected intermediate conclusions rather than an immediate sentence. Supply all assumptions and define the required endpoint: derive a formula, explain an algorithm, justify a bound or prepare a code change. The maker’s mathematical example concerns positive variables linked by an equation and the possible range of their sum. A useful response should identify the conditions, show the relevant transitions and clearly separate the final result.
GLM-Z1-Rumination-32B-0414 is a separate edition for more open-ended research with search tools; GLM-Z1-9B-0414 is another size. This entry preserves the named Z1 32B release instead of importing those editions’ extra workflow or size characteristics.
The manufacturer recommends temperature 0.6, top_p 0.95 and top_k 40. It also recommends a max_new_tokens budget of 30,000 to leave room for thinking. These are recommended generation settings, not a guarantee that every answer needs that length or a documented universal hard output ceiling. The card’s separate inference example uses a smaller request-specific generation budget.
For a reasoning task, allow enough room for the analysis and the final answer together, and define the final answer’s form in the prompt. A mathematical question can request the range or value separately from the explanation; a coding question can request one implementation followed by the assumptions that matter to it. Keep the requested result specific enough to check against the supplied conditions.
The maker’s template starts the generation with the thinking marker <think>. Its published chat template injects this marker, so an implementation should use the release’s actual template rather than assuming an unrelated dialogue format.
For subsequent turns, the usage guide retains the final user-visible reply and removes the internal thinking material from saved dialogue history. This is a preparation rule for the model’s own conversation template. A follow-up should contain the current question, necessary source material and relevant previous answer, not an unfiltered accumulation of all earlier internal analysis.
The published configuration sets max_position_embeddings to 32,768 and does not include a rope_scaling entry. The usage guide recommends considering YaRN when input exceeds 8,192 tokens. Its example scaling configuration uses factor 4.0 with original_max_position_embeddings set to 32,768. The recommendation threshold, the base configuration and the optional extension are different facts; they are not interchangeable response limits.
Static YaRN affects all text and may slightly reduce performance on short inputs, so the manufacturer recommends enabling it when needed. For long material, provide the relevant definitions and passages in a clearly separated input and leave a suitable generation budget in the active context. The configuration should follow the selected deployment’s support for the maker’s scaling procedure rather than treating an arithmetic multiple as an automatically available context window.
The model’s inference example uses a user question and the release-specific chat template. For mathematics, preserve variable definitions and state the proposition to establish. For code, provide the relevant fragment, its required behavior and the case under discussion. Ask for a distinct final artifact—proof, formula, implementation or explanation—so the reasoning can be evaluated through the task’s result. The dedicated Z1 guide specifies text reasoning and its preparation; it does not establish an additional native tool contract or image input for this edition.
POST /v1/messagestextjson, sse| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits THUDM/GLM-Z1-32B-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 $0 / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
{\n "model": "THUDM/GLM-Z1-32B-0414",\n "max_tokens": 128,\n "messages": [\n {\n "role": "user",\n "content": "Summarize the incident report."\n }\n ],\n "stream": false\n}set -eu\n: "${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-Z1-32B-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-Z1-32B-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-Z1-32B-0414
Use the method paths shown above.
Technical example in English
Derive a safe batch bound\n\nA buffer holds at most 96 records. Every batch adds b records, where b is a positive integer, and 12 records must remain free. Derive the largest number of complete batches that fit as a formula in b. Explain the rounding and calculate the result for b = 10, keeping the reserved space separate from usable capacity.A formula using a floor, an explanation of usable capacity and a checked numeric example.Verify the formula reserves 12 records and a further full batch would exceed usable capacity in the example.