GLM-5.1 is Zhipu's text model for long-horizon software engineering and continuous optimization, emphasizing progress from requirements analysis through implementation, testing, and feedback-driven revision. It is suitable for code refactoring, performance analysis, and multi-turn work that needs to preserve engineering constraints. Standard Chat Completions delivers model results, while source code reading, execution, and validation are handled by the integrated application environment.
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-5.1import 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-5.1",
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 inputs and standard calling method.
Native model characteristics are for model selection; this platform's input limits, available parameters, and billing are subject to the API and pricing for this model. Use stream for continuous output from Chat Completions; the client is responsible for saving message history.
Learn what glm-5.1 can bring to your work.
GLM-5.1 focuses not only on generating a piece of code, but also on planning and iteration in complex engineering tasks. After providing requirements, relevant implementations, and acceptance criteria, you can have it first break down the scope of changes and then propose an implementation plan; continue supplying test failure information to drive revisions instead of treating the initial response as the final deliverable.
For tasks that require repeated analysis, execution, and adjustment, GLM-5.1 is suitable as the text-based decision-making core of an Agent. Organize objectives, current state, and tool results into continuous context so it can determine the next action. Task progress should still be recorded by the application so you can check whether the plan has deviated from the original constraints.
GLM-5.1's official introduction emphasizes continuous engineering iteration. You can retain analysis, modification, and measurement records around an implementation, requiring each round to explain whether the hypothesis is supported; performance and correctness gains must be proven by actual results in the same environment.
Start with specific tasks to find where the model can be effective.
Provide the relevant source code, error logs, dependency notes, and expected behavior, and ask GLM-5.1 to output issue diagnosis, modification suggestions, and a test checklist. Bring actual test results back in the next round to continue refining the solution. Suitable for defect handling and code review that require understanding constraints; delivered code still needs to be verified in a real environment.
Organize requirements documents, interface specifications, or design records into text, and ask the model to extract constraints, identify contradictions, and produce a decision summary. Analyze section by section, then summarize items requiring confirmation and implementation steps; retain section numbers so the team can trace the specific basis, rather than receiving only a general overview.
Keep the versions of materials submitted to glm-5.1 and the actual responses, distinguishing original facts, model suggestions, and operations 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.
Choose based on task complexity, input materials, and expected results.
If the goal is complex reasoning, engineering code, or long-document analysis, GLM-5.1 is a mature flagship choice in the GLM series. GLM-5.3 is positioned for newer complex software engineering and Agent tasks, and its capacity and reasoning settings cannot be applied to GLM-5.1. Existing applications can compare results using the same requirements, logs, and acceptance criteria before deciding whether to switch.
Use /v1/chat/completions and explicitly set model=glm-5.1. Existing OpenAI-compatible applications can retain their message and result handling; configure the platform address, API Key, and exact model name during integration.
Arrange the inputs first, then connect them to the corresponding application workflow.
Prepare the existing implementation, performance measurements, reproduction steps, and external behavior that must not be changed.
Explicitly select glm-5.1 in the Chat Completions request, and organize the background, materials, and output requirements for this run into messages. First use a task with a clearly defined scope 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 propose optimization hypotheses, prioritizing minimal changes that can be validated with tests; after receiving measurement results, explain the benefits and remaining bottlenecks.
Use the same data to measure runtime and result consistency, check whether correctness has been changed for performance; retain records of each round of changes and measurements.
Before formal use, understand the output quality and capability scope.
Answers to common questions about using glm-5.1.
It can be used for text-based Q&A, but its more valuable use cases are complex programming, reasoning, and long-document analysis. Simple Q&A does not necessarily require a flagship model; when tasks involve engineering constraints, error diagnosis, or multiple rounds of revision, GLM-5.1 is better suited to participate in analysis and execution.
Provide the relevant code, runtime environment, complete error messages, and acceptance criteria at the same time. First have it explain the issue and scope of changes, then generate the implementation. Continue revising after feeding back test results; do not provide only “fix this error,” and do not omit dependencies and configuration that affect behavior.
Submit model=glm-5.1 and messages to /v1/chat/completions. Read standard results from choices[].message.content; use stream for incremental results in streaming calls. Use this platform's API Key, and set the complete base URL according to the SDK you use.
Have the application save the message history, and include user and assistant messages relevant to the current issue in messages. Prepare the existing implementation, performance measurements, reproduction steps, and external behavior that must not change. When materials or constraints change, update them in the next request.
Use a fixed set of real inputs and acceptance requirements, and record answer omissions, citation accuracy, and the amount of manual editing. Applications that integrate tools should also check parameters, permissions, and result write-back; choose the model based on delivery performance for the complete task, not just the length of a single response.
Choose a development approach or application suited to this model and follow its setup guide.
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