GLM-5-Turbo is a text model optimized by Zhipu for OpenClaw workflows, focusing not only on answering questions, but also on understanding complex goals, breaking down steps, and connecting tool results. It is suitable for agent collaboration in software development, information organization, and ongoing tasks, providing long context, structured output, and streaming response capabilities to help applications organize multi-turn discussions into trackable task workflows.
Point the Base URL to api.acedata.cloud, configure your platform API key and the model ID below, and use your compatible SDK or client.
api.acedata.cloudglm-5-turboimport os
from openai import OpenAI
client = OpenAI(
api_key=os.environ["ACEDATACLOUD_API_KEY"],
base_url="https://api.acedata.cloud/v1",
)
response = client.chat.completions.create(
model="glm-5-turbo",
messages=[{"role": "user", "content": "Hello!"}],
)
print(response.choices[0].message.content)Choose an available protocol for this model. OpenAI SDK uses a Base URL ending in /v1; Anthropic SDK uses the root URL. See each guide for protocol-specific parameters, tools and response formats.
First, clarify this model's input and standard invocation method.
Native model features are for model selection; this platform's input limits, available parameters, and billing are subject to this model's API and pricing. Chat Completions uses stream for continuous output, and the client is responsible for saving message history.
Learn what glm-5-turbo can bring to your work.
GLM-5-Turbo is optimized for multi-layer constraints and long-chain instructions, and can be used to identify goals, arrange steps, and organize agent responsibilities. For tasks that simultaneously include delivery formats, dependency conditions, and completion criteria, it is more suitable to have it form a plan first, then continue based on the results returned from each step.
Tool calling is a key optimization focus of this model. Applications can declare functions and parameter structures, allowing the model to choose how to call them and then return execution results to the model. It is suited to workflows that require alternating querying, analysis, and organization, and JSON output also makes it easier to hand task conclusions to business systems for processing.
The model has enhanced its understanding of timing requirements and execution continuity for scheduled triggers, continuous execution, and tasks with long logical chains. You can specify deadline conditions, stage statuses, and follow-up actions in tasks, allowing the model to continue work based on this information; scheduling, state preservation, and tool execution are still handled by the application or session service.
Start with specific tasks to find where the model can be effective.
Provide requirement descriptions, code text, error logs, and acceptance criteria, and let the model break down troubleshooting steps and propose modification plans based on the inspection results returned by tools. Deliverables can include patch suggestions, test checklists, and change descriptions; actual compilation, testing, and code writing must be completed by configured tools.
Convert materials into text, attach analysis criteria, field requirements, and report structure, and let the model summarize information by topic and generate summaries or JSON records. Long context makes it convenient to include more background materials, while multi-turn interaction is suitable for supplementing data batch by batch and revising conclusions, ultimately producing reports and items pending confirmation.
OpenClaw is officially optimized for instruction following, tool calling, and long-running task understanding. After integration, the runtime environment manages scheduling, progress, and tool permissions, while the model arranges subsequent steps based on actual results; understanding timing requirements should not be treated as independently running scheduled tasks.
Choose based on task complexity, input materials, and expected results.
GLM-5-Turbo has a clear focus on OpenClaw scenarios, including tool calling, complex instructions, and sustained long-chain execution. The official report shows scenario improvements over GLM-5 in ZClawBench, but this does not mean it is better for all question-answering tasks. If the application relies on multi-step tool collaboration, prioritize comparing completion quality and interruption recovery performance using real tasks.
Retain the version of materials submitted to glm-5-turbo and the actual responses, distinguishing original facts, model suggestions, and actions already completed by the application. Before structured results enter the system, check required fields, value types, and business rules to avoid directly turning missing information into definitive records.
Arrange the inputs first, then connect them to the corresponding application workflow.
Provide the objective, cycle conditions, available tools, permission scope, and actions requiring human confirmation, and set task completion criteria.
Explicitly select glm-5-turbo in the Chat Completions request, and organize the context, materials, and output requirements for this run into messages. First use a clearly scoped task to check the response, then include real review or test feedback in the next round of messages.
Design tasks directly from the following inputs and acceptance priorities.
Please break this material-organizing task into steps for reading, categorization, deduplication, and reporting; when a tool fails, save progress and explain the next step, and do not expand the scope of operations on your own.
In OpenClaw, verify tool calls and state recovery, and check whether long-running tasks are executed repeatedly; scheduling is provided by the runtime environment, while the model is responsible for understanding and planning.
Before formal use, understand the output quality and capability scope.
Answers to common questions about using glm-5-turbo.
No. It is a model specifically optimized for OpenClaw workflows, with a focus on tool calling, instruction decomposition, time requirements, and long-chain execution. Choose it based on whether the task requires these capabilities, rather than inferring fixed latency or general performance improvements solely from the Turbo name.
When using /v1/chat/completions, specify model as glm-5-turbo and submit messages containing roles and text. For regular responses, read the assistant content from choices; if tool_calls is returned, enter the tool execution and result return flow rather than directly treating the function parameters as the final answer.
The model supports generating tool-calling decisions, but the execution method depends on the entry point. Chat Completions requires the application to execute functions and return results; v2 sessions can advance tasks using configured tools and authorized connections. Having tool capabilities does not mean that any external service is already connected or authorized for writing.
Submit model=glm-5-turbo and messages to /v1/chat/completions. Read regular results from choices[].message.content; for streaming calls, obtain incremental results through stream. Use this platform's API Key, and configure the full base URL according to the SDK in use.
Native examples use thinking.type to select enabled or disabled, with thinking enabled by default. This platform's Chat Completions request structure provides reasoning_effort, but it is not a control with the same name as the native switch and should not be understood interchangeably; task prompts should still clearly specify objectives, constraints, and acceptance criteria.
Choose a development approach or application suited to this model and follow its setup guide.
↗OpenAI Chat Completions API
SDK installation, authentication and examples
↗Using Ace Data Cloud with Cherry Studio
Chat client setup · Base URL, API key and model
↗Using Ace Data Cloud with ChatBox
Chat client setup · Base URL, API key and model
↗Input
$0.72per 1M tokens
Official $1.20
Output
$2.40per 1M tokens
Official $4.00
You pay$13,584for 142,800 credits · $0.0951 / credits46% OFF
You're billed in credits. Pick a top-up tier to see its per-credit rate — the larger the top-up, the cheaper each credit.
This is only a basic call example. See the full docs for more parameters and advanced usage.
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