GLM-4.6V is the 106B edition in Z.ai’s GLM-V family, distinct from the lighter 9B GLM-4.6V-Flash. Its training context is extended to 128K tokens, with emphasis on visual understanding and native multimodal function calling. The hosted profile lists images, video, text and files as inputs and text as output. Tasks can request a document explanation, an extraction table, a screenshot-informed implementation or a report grounded in visual evidence.
Native multimodal tool use is a central change. Images, screenshots and document pages can become tool inputs without first being reduced to text descriptions. The model can interpret returned charts, rendered pages or retrieved images and incorporate them into further reasoning. Define the tools and the question, then return real outputs. The application executes operations; a proposed call and its completed result are separate stages.
Document work combines text, structure, charts, tables, formulas and figures. The card describes long or multiple documents within its 128K scope. Supply readable pages and page labels and request named fields or conclusions. An illustrative report can organise claims, the evidence for each and unresolved differences between sources. Visual content uses input space alongside the question and the response.
The maker describes interleaved image-text content assembled from source documents and retrieved visuals. A workflow can find candidate images, assess relevance and compose text around the selected material. This is illustrated-content creation through tools, while the hosted profile still lists text as the native output modality. Specify source references and how each image supports the narrative. It does not establish independent pixel generation or finished-file production.
Front-end replication and visual editing are major uses. The model can interpret layout, components and styles and propose HTML, CSS or JavaScript from a screenshot. It also supports instructions to revise a visible result. Give the target states and requested change, then ask for editable source and a comparison against the actual render. Producing the rendered page and new screenshot requires application operations.
The exact card’s reproducibility settings include temperature 0.8, top-p 0.6, top-k 2 and repetition penalty 1.1, with a 16K generation allowance in that procedure. The quickstart uses a different example allowance. These are published procedures, not a hard output ceiling. Its 106B identity and 128K context remain separate from GLM-4.5V, the 9B Flash model and later visual-coding releases.
The card identifies limitations in pure-text question answering, excessive or repetitive thinking on complex prompts and occasional answer restatement. It also notes perception weaknesses such as counting accuracy. Define the result, preserve small labels and ask for evidence tied to the source. This connects the document and visual-tool capabilities with an artifact that can be checked against the supplied material.
", "aboutText": "GLM-4.6V is the 106B edition in Z.ai’s GLM-V family, distinct from the lighter 9B GLM-4.6V-Flash. Its training context is extended to 128K tokens, with emphasis on visual understanding and native multimodal function calling. The hosted profile lists images, video, text and files as inputs and text as output. Tasks can request a document explanation, an extraction table, a screenshot-informed implementation or a report grounded in visual evidence.\n\nNative multimodal tool use is a central change. Images, screenshots and document pages can become tool inputs without first being reduced to text descriptions. The model can interpret returned charts, rendered pages or retrieved images and incorporate them into further reasoning. Define the tools and the question, then return real outputs. The application executes operations; a proposed call and its completed result are separate stages.\n\nDocument work combines text, structure, charts, tables, formulas and figures. The card describes long or multiple documents within its 128K scope. Supply readable pages and page labels and request named fields or conclusions. An illustrative report can organise claims, the evidence for each and unresolved differences between sources. Visual content uses input space alongside the question and the response.\n\nThe maker describes interleaved image-text content assembled from source documents and retrieved visuals. A workflow can find candidate images, assess relevance and compose text around the selected material. This is illustrated-content creation through tools, while the hosted profile still lists text as the native output modality. Specify source references and how each image supports the narrative. It does not establish independent pixel generation or finished-file production.\n\nFront-end replication and visual editing are major uses. The model can interpret layout, components and styles and propose HTML, CSS or JavaScript from a screenshot. It also supports instructions to revise a visible result. Give the target states and requested change, then ask for editable source and a comparison against the actual render. Producing the rendered page and new screenshot requires application operations.\n\nThe exact card’s reproducibility settings include temperature 0.8, top-p 0.6, top-k 2 and repetition penalty 1.1, with a 16K generation allowance in that procedure. The quickstart uses a different example allowance. These are published procedures, not a hard output ceiling. Its 106B identity and 128K context remain separate from GLM-4.5V, the 9B Flash model and later visual-coding releases.\n\nThe card identifies limitations in pure-text question answering, excessive or repetitive thinking on complex prompts and occasional answer restatement. It also notes perception weaknesses such as counting accuracy. Define the result, preserve small labels and ask for evidence tied to the source. This connects the document and visual-tool capabilities with an artifact that can be checked against the supplied material.", "editorialOverlay": { "copy_fields": { "practical_html": "Technical example in English
Correct a narrow-screen layout\n\nUse this textual layout requirement and CSS. At 320 px viewport width, the panel must fit with 16 px margins and its input must stay inside it. Current CSS: .panel { width: 400px; padding: 24px; } input { width: 100%; padding: 12px; }. Return a focused CSS correction and explain the box-sizing issue. Preserve the class name.An editable CSS patch and checks for panel width, margins and input overflow at a 320 px viewport.Render the proposed styles at 320 px and verify the panel and input bounding boxes remain within the required margins.z-ai/glm-4.6vGLM-4.6V is Z.ai’s 106B vision-language model for richly formatted documents, visual coding and multimodal tool use. Its producer profile accepts text, images, video and files, returns text and has a 128K context.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
GLM-4.6V is the 106B edition in Z.ai’s GLM-V family, distinct from the lighter 9B GLM-4.6V-Flash. Its training context is extended to 128K tokens, with emphasis on visual understanding and native multimodal function calling. The hosted profile lists images, video, text and files as inputs and text as output. Tasks can request a document explanation, an extraction table, a screenshot-informed implementation or a report grounded in visual evidence.
Native multimodal tool use is a central change. Images, screenshots and document pages can become tool inputs without first being reduced to text descriptions. The model can interpret returned charts, rendered pages or retrieved images and incorporate them into further reasoning. Define the tools and the question, then return real outputs. The application executes operations; a proposed call and its completed result are separate stages.
Document work combines text, structure, charts, tables, formulas and figures. The card describes long or multiple documents within its 128K scope. Supply readable pages and page labels and request named fields or conclusions. An illustrative report can organise claims, the evidence for each and unresolved differences between sources. Visual content uses input space alongside the question and the response.
The maker describes interleaved image-text content assembled from source documents and retrieved visuals. A workflow can find candidate images, assess relevance and compose text around the selected material. This is illustrated-content creation through tools, while the hosted profile still lists text as the native output modality. Specify source references and how each image supports the narrative. It does not establish independent pixel generation or finished-file production.
Front-end replication and visual editing are major uses. The model can interpret layout, components and styles and propose HTML, CSS or JavaScript from a screenshot. It also supports instructions to revise a visible result. Give the target states and requested change, then ask for editable source and a comparison against the actual render. Producing the rendered page and new screenshot requires application operations.
The exact card’s reproducibility settings include temperature 0.8, top-p 0.6, top-k 2 and repetition penalty 1.1, with a 16K generation allowance in that procedure. The quickstart uses a different example allowance. These are published procedures, not a hard output ceiling. Its 106B identity and 128K context remain separate from GLM-4.5V, the 9B Flash model and later visual-coding releases.
The card identifies limitations in pure-text question answering, excessive or repetitive thinking on complex prompts and occasional answer restatement. It also notes perception weaknesses such as counting accuracy. Define the result, preserve small labels and ask for evidence tied to the source. This connects the document and visual-tool capabilities with an artifact that can be checked against the supplied material.
POST /v1/chat/completionstexttext| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits z-ai/glm-4.6v | 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.
Match each charge to the conditions shown. Use one rate for each set of alternative configurations.
| Configuration | Rate schedule | Context tokens, more than | Context tokens, up to and including | Charge |
|---|---|---|---|---|
| ConfigurationBase token rates | Rate scheduleBase rate | Context tokens, more than— | Context tokens, up to and includingNo upper bound | Charge Input · Input tokens $0.0006 / 1K tokens Output · Output tokens $0.0018 / 1K tokens Input · Cache read $0.0001 / 1K tokens Input · Cache write $0 / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
| ConfigurationRequest-length tier 1 | Rate schedule— | Context tokens, more than0 | Context tokens, up to and includingNo upper bound | Charge Input · Input tokens $0.0006 / 1K tokens Output · Output tokens $0.0018 / 1K tokens Input · Cache read $0.0001 / 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": "z-ai/glm-4.6v", "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='z-ai/glm-4.6v',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='z-ai/glm-4.6v',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 z-ai/glm-4.6v
Use the method paths shown above.
z-ai/glm-4.6vGLM-4.6V is Z.ai’s 106B vision-language model for richly formatted documents, visual coding and multimodal tool use. Its producer profile accepts text, images, video and files, returns text and has a 128K context.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
GLM-4.6V is the 106B edition in Z.ai’s GLM-V family, distinct from the lighter 9B GLM-4.6V-Flash. Its training context is extended to 128K tokens, with emphasis on visual understanding and native multimodal function calling. The hosted profile lists images, video, text and files as inputs and text as output. Tasks can request a document explanation, an extraction table, a screenshot-informed implementation or a report grounded in visual evidence.
Native multimodal tool use is a central change. Images, screenshots and document pages can become tool inputs without first being reduced to text descriptions. The model can interpret returned charts, rendered pages or retrieved images and incorporate them into further reasoning. Define the tools and the question, then return real outputs. The application executes operations; a proposed call and its completed result are separate stages.
Document work combines text, structure, charts, tables, formulas and figures. The card describes long or multiple documents within its 128K scope. Supply readable pages and page labels and request named fields or conclusions. An illustrative report can organise claims, the evidence for each and unresolved differences between sources. Visual content uses input space alongside the question and the response.
The maker describes interleaved image-text content assembled from source documents and retrieved visuals. A workflow can find candidate images, assess relevance and compose text around the selected material. This is illustrated-content creation through tools, while the hosted profile still lists text as the native output modality. Specify source references and how each image supports the narrative. It does not establish independent pixel generation or finished-file production.
Front-end replication and visual editing are major uses. The model can interpret layout, components and styles and propose HTML, CSS or JavaScript from a screenshot. It also supports instructions to revise a visible result. Give the target states and requested change, then ask for editable source and a comparison against the actual render. Producing the rendered page and new screenshot requires application operations.
The exact card’s reproducibility settings include temperature 0.8, top-p 0.6, top-k 2 and repetition penalty 1.1, with a 16K generation allowance in that procedure. The quickstart uses a different example allowance. These are published procedures, not a hard output ceiling. Its 106B identity and 128K context remain separate from GLM-4.5V, the 9B Flash model and later visual-coding releases.
The card identifies limitations in pure-text question answering, excessive or repetitive thinking on complex prompts and occasional answer restatement. It also notes perception weaknesses such as counting accuracy. Define the result, preserve small labels and ask for evidence tied to the source. This connects the document and visual-tool capabilities with an artifact that can be checked against the supplied material.
POST /v1/messagestextjson, sse| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits z-ai/glm-4.6v | 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.
Match each charge to the conditions shown. Use one rate for each set of alternative configurations.
| Configuration | Rate schedule | Context tokens, more than | Context tokens, up to and including | Charge |
|---|---|---|---|---|
| ConfigurationBase token rates | Rate scheduleBase rate | Context tokens, more than— | Context tokens, up to and includingNo upper bound | Charge Input · Input tokens $0.0006 / 1K tokens Output · Output tokens $0.0018 / 1K tokens Input · Cache read $0.0001 / 1K tokens Input · Cache write $0 / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
| ConfigurationRequest-length tier 1 | Rate schedule— | Context tokens, more than0 | Context tokens, up to and includingNo upper bound | Charge Input · Input tokens $0.0006 / 1K tokens Output · Output tokens $0.0018 / 1K tokens Input · Cache read $0.0001 / 1K tokens Input · Cache write $0 / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
{\n "model": "z-ai/glm-4.6v",\n "max_tokens": 128,\n "messages": [\n {\n "role": "user",\n "content": "Summarize the incident report."\n }\n ],\n "stream": false\n}set -eu\n: "${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': 'z-ai/glm-4.6v', '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': 'z-ai/glm-4.6v', '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 z-ai/glm-4.6v
Use the method paths shown above.
Technical example in English
Correct a narrow-screen layout\n\nUse this textual layout requirement and CSS. At 320 px viewport width, the panel must fit with 16 px margins and its input must stay inside it. Current CSS: .panel { width: 400px; padding: 24px; } input { width: 100%; padding: 12px; }. Return a focused CSS correction and explain the box-sizing issue. Preserve the class name.An editable CSS patch and checks for panel width, margins and input overflow at a 320 px viewport.Render the proposed styles at 320 px and verify the panel and input bounding boxes remain within the required margins.