GLM-5 is an engineering-focused large language model launched by Zhipu AI, with a focus on complex system development, code reasoning, and Agent tasks that require sustained planning. It is suitable not only for generating code snippets, but also for multi-turn collaboration around requirements, implementation, testing, and feedback.
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-5import 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",
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 intended for model selection; this platform's input limits, available parameters, and billing are subject to this model's API and pricing. Use stream for continuous output in Chat Completions, and the client is responsible for saving message history.
Learn what glm-5 can bring to your work.
GLM-5 focuses on complex systems engineering and can organize implementation approaches around frontend, backend, and cross-module requirements. After providing interface constraints, existing code, and acceptance criteria, it is well suited to breaking down changes, explaining dependencies, and supplementing testing suggestions, making the output closer to a reviewable engineering plan rather than isolated code snippets.
For tasks that require multiple steps to complete, GLM-5 emphasizes planning and sustained progress. You can first define objectives, stage deliverables, and stopping conditions, then add test results or tool feedback each turn to let it adjust the next action. Combined with external state records and execution environments, it is suitable for building Agent workflows with checkpoints.
GLM-5's official demonstrations cover requirements documents, reports, and spreadsheet-style deliverables. When used in chat integrations, it can first generate sections, table content, and data-processing code, then hand them off to document tools to create files. It is suitable for organizing scattered materials into editable working drafts while preserving room for human review and formatting adjustments.
Start with specific tasks to identify where the model can be effective.
Provide error logs, relevant functions, API definitions, and reproduction steps, and let GLM-5 analyze the failure path and propose modification locations, candidate patches, and regression tests. Continue sending back results after running tests to ultimately produce a fix description and validation checklist. This is suitable for development tasks that require repeated diagnosis rather than a one-time completion.
Provide product goals, business rules, the existing architecture, and acceptance requirements, and let GLM-5 produce module breakdowns, API drafts, implementation order, and risk dependencies. Code and test plans can then be further refined by module, creating a task list that facilitates development reviews and avoiding a direct jump from vague requirements to large blocks of implementation.
Provide organized business materials, data definitions, and target readers, and let GLM-5 generate report structures, explanatory paragraphs, table fields, and calculation approaches. When Word, PDF, or Excel files are needed, connect the appropriate creation tools; before delivery, verify formulas, units, and key figures to ensure the content is consistent with the materials.
Choose based on task complexity, input materials, and expected results.
If the task involves frontend-backend coordination, complex code changes, or multi-turn tool feedback, consider evaluating GLM-5 first. Official comparisons emphasize improvements in these engineering tasks, but that does not mean it is better suited to every problem. For existing GLM-4.7 applications, use real tickets to compare patch quality, test pass results, and interaction overhead before deciding whether to migrate.
glm-5, glm-5-turbo, and glm-5.1, glm-5.2, glm-5.3 are different model variants. Choose GLM-5 primarily for its positioning in complex engineering and long-running tasks; if requirements depend on specific capacity or control features of other versions, choose the corresponding variant. When switching models, prompts, tool interactions, and output performance should also be revalidated.
Arrange the inputs first, then connect them to the appropriate application workflow.
Prepare requirements, module structure, data models, API boundaries, and performance goals, and specify staged delivery conditions.
Explicitly select glm-5 in the Chat Completions request, organizing the context, materials, and output requirements for this request into messages. First use a clearly scoped task to check the response, then include actual review or test feedback in the next round of messages.
Design tasks directly from the following inputs and acceptance priorities.
Please break the requirements into verifiable engineering stages, listing module changes, data flows, testing, and rollback points; distinguish implementation results from items to be verified at each stage.
Check whether the plan covers exception paths and data consistency, and conduct acceptance by stage after implementation; code generation, tool execution, and deployment each have independent responsibilities.
Before formal use, understand the output quality and capability scope.
Answers to common questions about using glm-5.
No. GLM-5 is an independent native model, with the invocation ID glm-5; names such as glm-5.3 and glm-5-turbo correspond to different models. Do not directly apply the context, output limits, or reasoning controls of other versions to GLM-5. Revalidate task performance when switching.
Submit model=glm-5 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 set the complete base URL according to the SDK you use.
It can analyze code, plan changes, and generate tool calls, but actually reading and writing the project and running tests requires an execution environment. It is recommended to restrict accessible directories, executable commands, and acceptance criteria, then send back the execution results so the model can continue refining based on real feedback rather than judging success from explanations alone.
The official materials demonstrate a workflow for creating office files with Agent skills. Standard chat integrations are better suited for first obtaining document content, table structures, or generation scripts, then using file creation tools to complete the export. Distinguish between textual content in a response and file artifacts that have already been generated and can be downloaded.
First provide requirement boundaries, relevant code, error information, and clear acceptance criteria, then ask it to propose a phased plan. Send back test results and new constraints at each stage, and retain key decisions and unresolved issues. Compared with simply saying “help me fix it,” this feedback-driven collaboration is better aligned with its engineering task focus.
Choose a development approach or application suited to this model and follow its setup guide.
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