Using GLM-4-Plus Large Model API: Features, Code Samples, and Practical Application Scenarios

Hello, I'm Fei! In the past two years, large‑model technology has become the fastest‑growing field. With open APIs from platforms like BigModel, anyone can boost productivity or even build applications without deep AI expertise.

The newly released Plus series models from Zhipu AI approach international performance levels and are now the preferred choice for many domestic large‑model users.

Advantages of Large Models

Historically, machine‑learning research required strong mathematical foundations and familiarity with algorithms such as logistic regression, SVM, CNN, LSTM, and various frameworks. Large models like GPT, GLM, and Llama have transformed this landscape by offering powerful, general‑purpose capabilities through simple API calls, dramatically lowering the entry barrier.

GLM‑4‑Plus excels in three key areas:

Language Understanding: Trained on massive corpora, it matches GPT‑4o and Llama 3.1 in benchmark scores, making it suitable for intelligent客服 and office automation.

Long‑Text Processing: Innovative memory and segment‑processing techniques enable efficient handling of extensive documents, reaching international standards for legal analysis, academic research, and complex content creation.

Reasoning Ability: Incorporates Proximal Policy Optimization (PPO) to maintain stability and efficiency in complex mathematical and programming tasks.

Given these strengths, developers can now directly use GLM‑4‑Plus instead of wrestling with higher‑threshold models like GPT.

How to Use the GLM‑4‑Plus API

1. Register on the Zhipu platform and obtain an API key (https://bigmodel.cn/). 2. Review the official documentation for GLM‑4 series models (https://bigmodel.cn/dev/api/normal-model/glm-4). 3. Choose a specific model variant (e.g., GLM‑4‑Plus, glm‑4‑0520, glm‑4‑air, etc.) based on capability and pricing.

Key request parameters:

model : the model name, e.g., "GLM-4-Plus".

messages : an array of system, user, and assistant messages.

temperature : controls randomness; lower values yield more deterministic outputs.

max_tokens : maximum number of tokens in the response (default 1024, max 4095).

Example of a simple curl request:

# curl --location 'https://open.bigmodel.cn/api/paas/v4/chat/completions' \
--header 'Authorization: xxxxxx' \
--header 'Content-Type: application/json' \
--data '{
    "model": "GLM-4-Plus",
    "messages": [{"role": "user", "content": "你好"}],
    "temperature": 1.0,
    "max_tokens": 2048
}'

The response returns a JSON object containing the model's reply, token usage, and other metadata.

Practical Use Cases

Code Generation : By sending a system prompt that defines the assistant as a shell‑programming expert and a user request for CPU‑usage statistics, GLM‑4‑Plus returns ready‑to‑run shell scripts with detailed comments.

# curl --location 'https://open.bigmodel.cn/api/paas/v4/chat/completions' \
--header 'Authorization: xxxxxx' \
--header 'Content-Type: application/json' \
--data '{
    "model": "GLM-4-Plus",
    "messages": [
        {"role": "system", "content": "You are a professional shell programming expert."},
        {"role": "user", "content": "Write a shell script to calculate average CPU utilization over 3 seconds, separating user, kernel, and soft‑interrupt time."}
    ]
}'

The model provides a complete script, usage explanation, and suggestions for saving the file.

Social‑Media Copy : Prompting the model as a “WeChat Moments copywriter” yields engaging, emoji‑rich posts for events such as a badminton competition.

curl --location 'https://open.bigmodel.cn/api/paas/v4/chat/completions' \
--header 'Authorization: xxxxxx' \
--header 'Content-Type: application/json' \
--data '{
    "model": "GLM-4-Plus",
    "messages": [
        {"role": "system", "content": "You are a professional Moments copywriter."},
        {"role": "user", "content": "I placed third in a badminton tournament, write a fun and positive post."}
    ]
}'

Travel Planning : Supplying a detailed system prompt describing the desired output format allows the model to generate a comprehensive 7‑day itinerary for Xinjiang, including destinations, daily activities, accommodation, transport, budget, and packing list.

Interview Question Generation : For a Golang backend engineer role, a structured prompt produces a categorized list of ten interview questions covering fundamentals, concurrency, RESTful design, scenario‑based problems, and teamwork.

Beyond these examples, GLM‑4‑Plus can handle translation, summarization, chatbot interactions, code explanation, content moderation, legal document drafting, product recommendation, and more.

The article concludes by mentioning additional Zhipu models such as CogView for image generation and GLM‑4V for multimodal video analysis, encouraging readers to explore further documentation at https://bigmodel.cn/dev/howuse/introduction.