GLM-4.7 is a text large language model launched by Zhipu AI, focused on code development, multi-step reasoning, and tool collaboration. It is suitable not only for generating functions, but also for breaking requirements down into implementation plans, coordinating frontend and backend structures, and improving interface layouts and component styles. For tasks that require ongoing discussion of constraints, plan revisions, and delivery progress, it offers more value than one-off Q&A.
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-4.7import 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-4.7",
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 features are 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 from Chat Completions; the client is responsible for preserving message history.
Learn what glm-4.7 can bring to your work.
GLM-4.7 focuses on completing tasks in programming, rather than merely filling in local code. After providing goals, the technology stack, and interface constraints, it can break down modules, coordinate frontend and backend implementation, and explain key dependencies and execution steps. It is suitable for organizing scattered requirements into a verifiable engineering framework, then making incremental changes based on test feedback.
When generating web pages, it can handle layout structure, color relationships, and component styles at the same time, without leaving all visual requirements for later adjustments. By writing page hierarchy, interaction states, and brand style into text requirements, you can obtain more consistent interface code. It is also suitable for creating demo pages, presentation prototypes, and layout plans for office content.
When facing problems that require multiple steps, GLM-4.7 can break down actions around the goal and work with external tools through function calls. During multi-turn discussions, you can continuously add constraints, compare options, and revise conclusions. Its native turn-by-turn reasoning design also gives simple Q&A and complex tasks different ways of thinking, making it suitable for ongoing development collaboration.
Start with specific tasks to find where the model can make a difference.
Enter product requirements, existing code snippets, interface definitions, and error logs, allowing the model to first identify dependencies and the scope of the issue, then provide modification plans, code, and verification steps. For prototypes involving frontend-backend interaction, you can request separate delivery of the module structure and startup instructions, making it easier for developers to check actual runtime results in their own environments.
Define names and parameters for business query functions, and submit user questions as text messages. The model can return function call information; after the application executes the query and fills in the results, it can generate an explanation or recommendations for the next step. This is suitable for workflows such as order inquiries and internal data Q&A, keeping natural-language understanding separate from real business data processing.
Provide the world setting, character profiles, chapter goals, and existing text together, and let the model continue the plot, adjust narrative pacing, or unify character expression. Its strengths in writing include not only expansion, but also atmosphere description and character consistency. During ongoing creation, you can retain key settings and revision records to obtain more coherent chapter drafts and editorial suggestions.
Choose based on task complexity, input materials, and expected results.
If your work mainly involves general Q&A and routine content drafting, GLM-4.6 can still be considered; when tasks involve cross-module programming, multi-step tool calls, or repeated constraint revisions, GLM-4.7 is more worthwhile. Compared with GLM-4.6, its key improvements are programming stability and delivery completeness; use your own coding tasks and acceptance criteria for evaluation.
GLM-4.7, GLM-4.7-Flash, and GLM-4.7-FlashX are different models. For complete programming tasks, frontend generation, and complex collaboration, evaluate GLM-4.7 first; if considering lightweight variants, compare task performance and usage conditions separately. Belonging to the same series does not mean response performance, resource requirements, or invocation configuration are exactly the same.
Arrange the inputs first, then connect them to the appropriate application workflow.
Prepare a feature list, framework, design tokens, and available components, and specify that adding dependencies is prohibited.
Explicitly select glm-4.7 in the Chat Completions request, and organize the background, materials, and requirements for this output into messages. First use a clearly scoped task to check the response, then include actual review or testing feedback in the next message.
Design tasks directly from the following inputs and acceptance priorities.
Please implement a management page with filtering, details, and editing; standardize spacing and component styles, and explain how each state is handled and verified.
Run the page and check primary interactions, keyboard focus, and mobile layout; the model generates an implementation candidate, while complete delivery depends on real testing.
Before formal use, understand the output quality and scope of capabilities.
Answers to common questions about using glm-4.7.
Submit model: glm-4.7 and messages to /v1/chat/completions, using Bearer Token authentication. Regular text responses are located in message.content within choices; also read finish_reason and usage to determine whether the response has ended and this request's token usage.
Submit model=glm-4.7 and messages to /v1/chat/completions. Read regular results from choices[].message.content; use stream for streaming calls to receive incremental results. Use this platform's API Key, and set the complete base URL according to the SDK you use.
Yes. After setting stream: true for the chat completions endpoint, receive text in incremental chunks and progressively concatenate it, which is suitable for long code and solution output. The frontend should parse streaming events rather than treating each network data chunk as a complete response; save the final code only after completion to avoid using content that has not yet been fully generated.
GLM-4.7 should be used as a text model, without treating image understanding or speech generation as native capabilities. Requirements in images can first be converted to text, and audio can first be transcribed before being given to it for analysis. If an application requires direct image recognition or voice interaction, choose models and audio components for the corresponding modalities.
GLM-4.7's native design supports enabling or disabling reasoning on a per-turn basis, which can be used to balance simple answers and complex problem solving. This does not mean this platform provides the same switch: do not directly apply thinking.type from official examples to /v1/chat/completions, and do not treat reasoning_effort as equivalent to enabling or disabling reasoning. When using this platform, start with the default calling method, and organize complex problem solving through clear task requirements, response budgets, and step-by-step verification.
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
$0.35per 1M tokens
Official $0.35
Output
$0.53per 1M tokens
Official $0.53
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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