models

Z.ai: GLM 5.3 FlashX

Markdown · JSON

Z.ai: GLM 5.3 FlashX

GLM-5.3-FlashX is Z.ai’s multimodal model for visual coding, document work and tool-assisted agents. Its producer profile accepts images, video, text and files, returns text and lists a 1M context with up to 128K output tokens.

About the model

This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.

GLM-5.3-FlashX is the FlashX edition in Z.ai’s native multimodal GLM-5.3-Flash family. The maker presents the pair for visual coding and professional work, combining text with images, video and files while returning text. FlashX is separately named for faster inference in the producer’s service. A useful brief can ask for a screenshot-informed implementation, a document-based analysis or a plan that an application carries out with tools. The source lists both model codes; their service identities remain distinct.

The family uses a hybrid sparse-and-linear-attention design and is described with 320 billion total parameters and 18 billion activated. Sparse attention retrieves global information, while linear attention models local dependencies; the producer also describes index pooling for long context. These are family architecture facts rather than the numerical format of every hosted instance. Its shared profile is one million context tokens and up to 128K output tokens. Source material, represented visuals, history, reasoning and the answer all need space in the configured workflow.

Visual coding is central. Z.ai’s examples combine multiple page screenshots or a recording with instructions to reconstruct page relationships, shared components, navigation, states and animation behaviour. Prepare references in order and state which functions must work. Request editable source and a comparison with the design. The application launches or renders the proposal and returns screenshots or interaction results; the model uses that feedback to refine the result. Text and code do not become a finished running application without those operations.

For Office and document work, the producer describes organising material, creating charts and layouts and inspecting the rendered result. A tool-supported workflow can produce PPTX, PDF, DOCX or XLSX. An illustrative report brief can provide sources, the audience and required sections, then request a source-to-conclusion table and an editable report or presentation. Keep supplied values separate from analytical assumptions. Rendering, saving files and checking formula references are application operations. For financial research, the producer describes comparing statements and announcements, separating disclosed values from assumptions, and preparing an editable forecast or valuation model alongside a source-linked report through connected tools.

The maker’s broader examples include video editing, 3D scenes, playable games and CAD reconstruction through tools. A video task can start with a media inventory and timeline; a scene task can define layout, materials and camera views before requesting a modelling script. A CAD brief needs dimensional references and explicit assumptions for measurements not established by the image. The model interprets the material and directs work, while the relevant editing, rendering or modelling tools produce and inspect the assets.

Reasoning cannot be disabled in this profile. The source says text controls are consistent with GLM-5.3 and recommends reasoning_effort=max, temperature 1 and top-p 0.95. It recommends clear_thinking=false for retained reasoning and both text and tool streaming. The associated guide requires preserved reasoning blocks to be returned completely and unmodified. Define the final result before a long analysis, and keep observations attached to the steps that produced them.

The producer lists function calling, context caching, structured outputs and native visual understanding. Give tool descriptions and required output fields explicitly. For an interface task, request the page states covered, discrepancies remaining and what the actual render comparison showed. For analysis, separate source facts, assumptions and conclusions. FlashX supports these multimodal workflows, with execution, rendering and file handling supplied by the connected application.

Practical task

Implement a text-specified form state

Technical example in English

Implement a text-specified form state

Create editable HTML and JavaScript for this form behavior: an email field and a Subscribe button; blank submission shows 'Enter your email' and preserves the current input; nonblank submission shows a local confirmation without sending a request. Include a short state checklist. No screenshot is supplied, and do not claim the form was rendered or deployed.

Expected result

Editable form source and a checklist of empty, corrected and confirmation states.

Check the result

Blank submission must keep the field value and show the requested message; the proposal must not send a network request.
Ailoot Ultra · OpenAI Chat POST /v1/chat/completions z-ai/glm-5.3-flashx

z-ai/glm-5.3-flashx

Z.ai /z-ai/glm-5.3-flashx

Overview

GLM-5.3-FlashX is Z.ai’s multimodal model for visual coding, document work and tool-assisted agents. Its producer profile accepts images, video, text and files, returns text and lists a 1M context with up to 128K output tokens.

Accepted inputs
Text
Results
Text

Available in Ailoot · inputs & methods

Ailoot Ultra · OpenAI Chat POST /v1/chat/completions
Input types
text
Output types
text
Output format
json or SSE
FieldTypeRequirementValues & limitsDefault
FieldmodelTypestringRequirementRequiredValues & limits
z-ai/glm-5.3-flashx
Default—
FieldmessagesTypemessage[]RequirementRequiredValues & limits
Minimum items: 1
Default—
Fieldmessages[].roleTypestringRequirementRequiredValues & 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[].contentTypestring|content_part[]|nullRequirementOptionalValues & limitsDefault—
Fieldmessages[].tool_callsTypetool_call[]RequirementOptionalValues & limitsDefault—
Fieldmessages[].tool_call_idTypestringRequirementOptionalValues & limits
Applies when: role=tool; use actual returned ID
Default—
Fieldmessages[].nameTypestringRequirementOptionalValues & limitsDefault—
Fieldmessages[].reasoning_contentTypestringRequirementOptionalValues & limits
Applies when: Round-trip actual returned reasoning where this model uses it
Default—
Fieldmessages[].reasoning_detailsTypeobject|arrayRequirementOptionalValues & limits
Model-specific
Default—
Fieldmax_tokensTypeintegerRequirementOptionalValues & limits
Minimum: 1
Model-specific
Default—
Fieldmax_completion_tokensTypeintegerRequirementOptionalValues & limits
Minimum: 1
Model-specific
Default—
FieldstreamTypebooleanRequirementOptionalValues & limitsDefaultfalse
Fieldstream_options.include_usageTypebooleanRequirementOptionalValues & limits
Model-specific
Default—
FieldtemperatureTypenumberRequirementOptionalValues & limits
Model-specific
Default—
Fieldtop_pTypenumberRequirementOptionalValues & limits
Model-specific
Default—
FieldstopTypestring|string[]RequirementOptionalValues & limits
Model-specific
Default—
FieldtoolsTypefunction_tool[]RequirementOptionalValues & limits
Model-specific
Default—
Fieldtools[].typeTypestringRequirementOptionalValues & limits
function
Model-specific
Default—
Fieldtools[].function.nameTypestringRequirementRequiredValues & limits
Applies when: function tool supplied
Default—
Fieldtools[].function.descriptionTypestringRequirementOptionalValues & limitsDefault—
Fieldtools[].function.parametersTypeJSON_schemaRequirementOptionalValues & limitsDefault—
Fieldtool_choiceTypestring|objectRequirementOptionalValues & limits
Model-specific
Default—
Fieldparallel_tool_callsTypebooleanRequirementOptionalValues & limits
Model-specific
Default—
Fieldresponse_formatTypeobjectRequirementOptionalValues & limits
Model-specific
Default—
Fieldreasoning_effortTypestringRequirementOptionalValues & limits
Model-specific
Default—
FieldreasoningTypeobjectRequirementOptionalValues & limits
Model-specific
Default—
Fieldservice_tierTypestringRequirementOptionalValues & limits
Model-specific
Default—
FieldinstructionsTypestringRequirementOptionalValues & limits
Compatibility field
Default—
FieldfunctionsTypefunction[]RequirementOptionalValues & limits
Model-specific
Default—
Fieldfunction_callTypestring|objectRequirementOptionalValues & limits
Model-specific
Default—
Fieldprompt_cache_optionsTypeobjectRequirementOptionalValues & limits
Model-specific
Default—
FieldmodalitiesTypestring[]RequirementOptionalValues & limits
text
Default—
FieldproviderTypeobjectRequirementOptionalValues & limits
Conditions: provider.options is rejected on this Ultra text route
Default—
Fieldcache_control.ttlTypestringRequirementOptionalValues & limits
Conditions: 1h is rejected in actual prompt-cache locations
Default—
Input material combinations

Text · UTF8 text

messages[].content

Method parameters and lifecycle
ParameterContract
Parametercontent_typeContractapplication/json
ParameterrequiredContractmodel, messages
Parametermaterial_typesContracttext
ParametermessagesContractmessage array with role/content shape
Parameterfile_materialContractactual UTF-8 document inserted as literal text; no file ID inferred
Parameteroptional_controlsContractstream, temperature, top_p, stop, max_tokens, max_completion_tokens, reasoning, response_format, tools, tool_choice
Parametercontrol_scopeContractmodel-specific; user function tools differ from hosted tools
Parameteroutput_modalitiesContracttext
Parameterhosted_operationsContractfalse
Parametermultimodal_input_bindingContractnull

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.

Pricing

Tariff references do not add request options. Supported parameters are listed for the selected method.

Fast mode
Standard rate When fast mode is selected
Flex mode
Standard rate When flex mode is selected
Maximum reasoning effort
Standard rate When maximum reasoning effort uses the default adjustment
Reasoning effort · high
Standard rate When reasoning effort is high
Reasoning effort · low
Standard rate When reasoning effort is low
Reasoning effort · max
Standard rate When reasoning effort is max

Ailoot rates

Prices by configuration

Match each charge to the conditions shown. Use one rate for each set of alternative configurations.

ConfigurationRate scheduleContext tokens, more thanContext tokens, up to and includingCharge
ConfigurationBase token ratesRate scheduleBase rateContext tokens, more than—Context tokens, up to and includingNo upper boundCharge
Input · Input tokens $0.00074 / 1K tokens
Output · Output tokens $0.0025 / 1K tokens
Input · Cache read $0.00018 / 1K tokens
Input · Cache write — / 1K tokens
Input · Cache write, 1 hour — / 1K tokens
ConfigurationRequest-length tier 1Rate schedule—Context tokens, more than0Context tokens, up to and includingNo upper boundCharge
Input · Input tokens $0.00074 / 1K tokens
Output · Output tokens $0.0025 / 1K tokens
Input · Cache read $0.00018 / 1K tokens
Input · Cache write — / 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 →

Example requests

Ailoot Ultra · OpenAI Chat

curlPython SDKPython stream
set -eu
: "${AILOOT_API_KEY:?Set your Ailoot API key}"
python3 - <<'PY_REQUEST'
import json,os
from pathlib import Path
p=json.loads('{"model": "z-ai/glm-5.3-flashx", "messages": [{"role": "user", "content": "Explain the difference between an observation and a hypothesis in three sentences."}]}')

Path("request.json").write_text(json.dumps(p,ensure_ascii=False))
PY_REQUEST
curl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/chat/completions \
  -H "Authorization: Bearer $AILOOT_API_KEY" -H 'Content-Type: application/json' \
  --data-binary @request.json -D response-headers.txt -o result.json
python3 - <<'PY_RESULT'
import json
from pathlib import Path
r=json.loads(Path("result.json").read_text())
for x in r.get("choices",[]):
    if x.get("message",{}).get("content"): print(x["message"]["content"])
PY_RESULT
from openai import OpenAI
import os

if not os.environ.get("AILOOT_API_KEY", "").strip(): raise SystemExit("Set AILOOT_API_KEY before making a request")
client=OpenAI(base_url="https://api.ailoot.ai/v1",api_key=os.environ["AILOOT_API_KEY"],max_retries=0)
result=client.chat.completions.create(model='z-ai/glm-5.3-flashx',messages=[{"role":"user","content":"Explain observations and hypotheses in three sentences."}])
import base64, json
from pathlib import Path

def save_result(data, stem="result"):
    # Keep every field, including usage, IDs, reasoning and tool requests.
    Path(stem + ".json").write_text(json.dumps(data, ensure_ascii=False, indent=2), encoding="utf-8")
    print(json.dumps(data, ensure_ascii=False, indent=2))
    def save_blob(encoded, suffix, label):
        raw = base64.b64decode(encoded, altchars=b"-_", validate=True)
        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
        file = Path(stem + "-" + label + "." + ext)
        file.write_bytes(raw)
        print("Saved", file)
    for index, block in enumerate(data.get("content") or []):
        print("Content", index, json.dumps(block, ensure_ascii=False))
    for choice in data.get("choices") or []:
        print("Choice", choice.get("index"), json.dumps(choice.get("message", choice.get("delta", {})), ensure_ascii=False))
    for index, item in enumerate(data.get("output") or []):
        print("Output", index, json.dumps(item, ensure_ascii=False))
        if item.get("type") == "image_generation_call" and item.get("result"):
            save_blob(item["result"], item.get("output_format") or "bin", "output-" + str(index))
    for ci, candidate in enumerate(data.get("candidates") or []):
        for pi, part in enumerate((candidate.get("content") or {}).get("parts") or []):
            print("Candidate/part", ci, pi, json.dumps(part, ensure_ascii=False))
            blob = part.get("inlineData") or part.get("inline_data")
            if blob and blob.get("data"):
                mime = blob.get("mimeType") or blob.get("mime_type")
                suffix = {"image/png":"png", "image/jpeg":"jpg", "image/webp":"webp", "audio/wav":"wav", "audio/mpeg":"mp3", "video/mp4":"mp4"}.get(mime, "bin")
                save_blob(blob["data"], suffix, "candidate-" + str(ci) + "-part-" + str(pi))

save_result(result.to_dict(mode="json"))
from openai import OpenAI
import os
from pathlib import Path

if not os.environ.get("AILOOT_API_KEY", "").strip(): raise SystemExit("Set AILOOT_API_KEY before making a request")
client=OpenAI(base_url="https://api.ailoot.ai/v1",api_key=os.environ["AILOOT_API_KEY"],max_retries=0)
import base64, json
from pathlib import Path

def save_result(data, stem="result"):
    # Keep every field, including usage, IDs, reasoning and tool requests.
    Path(stem + ".json").write_text(json.dumps(data, ensure_ascii=False, indent=2), encoding="utf-8")
    print(json.dumps(data, ensure_ascii=False, indent=2))
    def save_blob(encoded, suffix, label):
        raw = base64.b64decode(encoded, altchars=b"-_", validate=True)
        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
        file = Path(stem + "-" + label + "." + ext)
        file.write_bytes(raw)
        print("Saved", file)
    for index, block in enumerate(data.get("content") or []):
        print("Content", index, json.dumps(block, ensure_ascii=False))
    for choice in data.get("choices") or []:
        print("Choice", choice.get("index"), json.dumps(choice.get("message", choice.get("delta", {})), ensure_ascii=False))
    for index, item in enumerate(data.get("output") or []):
        print("Output", index, json.dumps(item, ensure_ascii=False))
        if item.get("type") == "image_generation_call" and item.get("result"):
            save_blob(item["result"], item.get("output_format") or "bin", "output-" + str(index))
    for ci, candidate in enumerate(data.get("candidates") or []):
        for pi, part in enumerate((candidate.get("content") or {}).get("parts") or []):
            print("Candidate/part", ci, pi, json.dumps(part, ensure_ascii=False))
            blob = part.get("inlineData") or part.get("inline_data")
            if blob and blob.get("data"):
                mime = blob.get("mimeType") or blob.get("mime_type")
                suffix = {"image/png":"png", "image/jpeg":"jpg", "image/webp":"webp", "audio/wav":"wav", "audio/mpeg":"mp3", "video/mp4":"mp4"}.get(mime, "bin")
                save_blob(blob["data"], suffix, "candidate-" + str(ci) + "-part-" + str(pi))

with client.chat.completions.stream(model='z-ai/glm-5.3-flashx',messages=[{"role":"user","content":"Explain observations and hypotheses in three sentences."}]) as stream:
    with Path("stream-events.jsonl").open("w",encoding="utf-8") as log:
        for event in stream:
            payload=event.to_dict(mode="json")
            log.write(json.dumps(payload,ensure_ascii=False)+"\n"); log.flush()
            print(json.dumps(payload,ensure_ascii=False))
    final=stream.get_final_completion()
    save_result(final.to_dict(mode="json"),"stream-result")

Receive and save results

Ailoot Ultra · OpenAI Chat

json or SSE

Keep all completed results

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.

If the original response needs checking

Keep request/response IDs and headers. No generic text job retrieval URL is supplied.

Response fields
FieldType
Fieldchoices[].indexTypeinteger
Fieldchoices[].message.contentTypestring|null
Fieldchoices[].message.tool_callsTypetool_call[]
Fieldchoices[].message.reasoning_contentTypestring
Fieldchoices[].message.reasoning_detailsTypeobject|array
Fieldchoices[].finish_reasonTypestring|null
FieldusageTypeobject
Errors and next steps
ResponseConditionNext step
Response400 · invalid_request_errorConditionInvalid required fields, material representation or parameter valueNext stepCorrect the indicated fields before a new creation request.

Aliases

Model aliases

Ailoot Ultra z-ai/glm-5.3-flashx

Method path aliases

Use the method paths shown above.

Related caller IDs

z-ai/glm-5.3-flashx

Ailoot Ultra · Anthropic Messages POST /v1/messages z-ai/glm-5.3-flashx

z-ai/glm-5.3-flashx

Z.ai /z-ai/glm-5.3-flashx

Overview

GLM-5.3-FlashX is Z.ai’s multimodal model for visual coding, document work and tool-assisted agents. Its producer profile accepts images, video, text and files, returns text and lists a 1M context with up to 128K output tokens.

Accepted inputs
Text
Results
json · sse

Available in Ailoot · inputs & methods

Ailoot Ultra · Anthropic Messages POST /v1/messages
Input types
text
Output types
json, sse
Output format
JSON or SSE
FieldTypeRequirementValues & limitsDefault
FieldmodelTypestringRequirementRequiredValues & limits
z-ai/glm-5.3-flashx
Default—
FieldmessagesTypemessage[]RequirementRequiredValues & limitsDefault—
Fieldmessages[].roleTypestringRequirementRequiredValues & limits
user, assistant
Default—
Fieldmessages[].contentTypestringRequirementRequiredValues & limitsDefault—
Fieldmax_tokensTypeintegerRequirementRequiredValues & limitsDefault—
FieldstreamTypebooleanRequirementOptionalValues & limitsDefaultfalse
Input material combinations

Text · UTF-8 text

messages[].content string

Method parameters and lifecycle
ParameterContract
Parametercontent_typeContractapplication/json
ParameterrequiredContractmodel, messages, max_tokens
Parameteroptional_controlsContractstream
Parametermaterial_typesContracttext
Parametertext_file_materialContractRead the actual UTF-8 document and put its text in the indicated input or message content string.

Lifecycle: synchronous_or_streamed

Pricing

Tariff references do not add request options. Supported parameters are listed for the selected method.

Fast mode
Standard rate When fast mode is selected
Flex mode
Standard rate When flex mode is selected
Maximum reasoning effort
Standard rate When maximum reasoning effort uses the default adjustment
Reasoning effort · high
Standard rate When reasoning effort is high
Reasoning effort · low
Standard rate When reasoning effort is low
Reasoning effort · max
Standard rate When reasoning effort is max

Ailoot rates

Prices by configuration

Match each charge to the conditions shown. Use one rate for each set of alternative configurations.

ConfigurationRate scheduleContext tokens, more thanContext tokens, up to and includingCharge
ConfigurationBase token ratesRate scheduleBase rateContext tokens, more than—Context tokens, up to and includingNo upper boundCharge
Input · Input tokens $0.00074 / 1K tokens
Output · Output tokens $0.0025 / 1K tokens
Input · Cache read $0.00018 / 1K tokens
Input · Cache write — / 1K tokens
Input · Cache write, 1 hour — / 1K tokens
ConfigurationRequest-length tier 1Rate schedule—Context tokens, more than0Context tokens, up to and includingNo upper boundCharge
Input · Input tokens $0.00074 / 1K tokens
Output · Output tokens $0.0025 / 1K tokens
Input · Cache read $0.00018 / 1K tokens
Input · Cache write — / 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 →

Example requests

Ailoot Ultra · Anthropic Messages

Complete minimal JSON bodycurl · use your UTF-8 documentPython · save the complete result
{
  "model": "z-ai/glm-5.3-flashx",
  "max_tokens": 128,
  "messages": [
    {
      "role": "user",
      "content": "Summarize the incident report."
    }
  ],
  "stream": false
}
set -eu
: "${AILOOT_API_KEY:?Set your Ailoot API key}"
: "${AILOOT_TEXT_FILE:?Set your actual UTF-8 document}"
export AILOOT_TEXT_FILE
python3 - <<'PY_REQUEST'
import json, os
from pathlib import Path

source_file = Path(os.environ['AILOOT_TEXT_FILE'])
if not source_file.is_file():
    raise SystemExit('Set AILOOT_TEXT_FILE to your actual UTF-8 document')
text = source_file.read_text(encoding='utf-8')
if not text.strip():
    raise SystemExit('Choose a document containing text')
payload = {'model': 'z-ai/glm-5.3-flashx', 'max_tokens': 128, 'messages': [{'role': 'user', 'content': 'Summarize the incident report.'}]}
payload['messages'][0]['content'] = 'Summarize this material and quote supporting source passages.\n\n' + text
Path('request.json').write_text(json.dumps(payload, ensure_ascii=False), encoding='utf-8')
PY_REQUEST
curl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/messages \
  -H "x-api-key: $AILOOT_API_KEY" -H 'anthropic-version: 2023-06-01' -H 'Content-Type: application/json' \
  --data-binary @request.json -D response-headers.txt -o result.json
python3 - <<'PY_RESULT'
import json
from pathlib import Path

result = json.loads(Path('result.json').read_text(encoding='utf-8'))
print(json.dumps(result, ensure_ascii=False, indent=2))
PY_RESULT
import json, os
from pathlib import Path

source_file = Path(os.environ['AILOOT_TEXT_FILE'])
if not source_file.is_file():
    raise SystemExit('Set AILOOT_TEXT_FILE to your actual UTF-8 document')
text = source_file.read_text(encoding='utf-8')
if not text.strip():
    raise SystemExit('Choose a document containing text')
payload = {'model': 'z-ai/glm-5.3-flashx', 'max_tokens': 128, 'messages': [{'role': 'user', 'content': 'Summarize the incident report.'}]}
payload['messages'][0]['content'] = 'Summarize this material and quote supporting source passages.\n\n' + text
from urllib.request import Request, urlopen
from urllib.error import HTTPError

api_key = os.environ['AILOOT_API_KEY']
if not api_key.strip():
    raise SystemExit('Set AILOOT_API_KEY before making a request')
headers = {'x-api-key': 'PLACEHOLDER_KEY', 'anthropic-version': '2023-06-01', 'Content-Type': 'application/json'}
headers['x-api-key'] = api_key
request = Request('https://api.ailoot.ai/v1/messages', data=json.dumps(payload, ensure_ascii=False).encode('utf-8'), headers=headers, method='POST')
try:
    with urlopen(request, timeout=120) as response:
        raw = response.read()
        Path('response-headers.txt').write_text(str(response.headers), encoding='utf-8')
except HTTPError as error:
    Path('result.json').write_bytes(error.read())
    Path('response-headers.txt').write_text(str(error.headers), encoding='utf-8')
    raise
Path('result.json').write_bytes(raw)
import json
from pathlib import Path

result = json.loads(Path('result.json').read_text(encoding='utf-8'))
print(json.dumps(result, ensure_ascii=False, indent=2))

Receive and save results

Ailoot Ultra · Anthropic Messages

JSON or SSE

Keep all completed results

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.

If the original response needs checking

Keep request/response IDs and headers. No generic text job retrieval URL is supplied.

Response fields
FieldType
FieldidTypestring
FieldmodelTypestring
Fieldcontent[]Typetext|thinking|tool_use|other content block
Fieldstop_reasonTypestring|null
FieldusageTypeobject
Errors and next steps
ResponseConditionNext step
Response400 · invalid_request_errorConditionInvalid required fields, material representation or parameter valueNext stepCorrect the indicated fields before a new creation request.

Aliases

Model aliases

Ailoot Ultra z-ai/glm-5.3-flashx

Method path aliases

Use the method paths shown above.

Related caller IDs

z-ai/glm-5.3-flashx

FAQ · Z.ai: GLM 5.3 FlashX
What inputs and results are available?

POST /v1/chat/completions · z-ai/glm-5.3-flashx: text → text

POST /v1/messages · z-ai/glm-5.3-flashx: text → json, sse

Which fields does the request use?

POST /v1/chat/completions · z-ai/glm-5.3-flashx: model, messages, stream, modalities, provider

POST /v1/messages · z-ai/glm-5.3-flashx: model, messages, max_tokens, stream

What limits apply?

POST /v1/chat/completions · z-ai/glm-5.3-flashx: messages.min_items: 1

More details

Connect Ailoot to Claude Code, OpenCode, Hermes or your own app.

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Choose a discounted plan for your regular requests.
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Enter your code at checkout. The price updates to reflect the current promotion.
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The model, tokens and amount are recorded for each request.

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Add text, image, video and audio generation to paid features in your service.
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