glm-4.7

This documentation is valid for the following list of our models:

• zhipu/glm-4.7

Try in Playground

Model Overview

The flagship LLM optimized for agentic coding and stable multi-step reasoning, supporting long-context workflows (200K context; up to 128K max output tokens).

How to Make a Call

Step-by-Step Instructions

1️ Setup You Can’t Skip

▪️ Create an Account: Visit the AI/ML API website and create an account (if you don’t have one yet). ▪️ Generate an API Key: After logging in, navigate to your account dashboard and generate your API key. Ensure that key is enabled on UI.

2️ Copy the code example

At the bottom of this page, you'll find a code example that shows how to structure the request. Choose the code snippet in your preferred programming language and copy it into your development environment.

3️ Modify the code example

▪️ Replace <YOUR_AIMLAPI_KEY> with your actual AI/ML API key from your account. ▪️ Insert your question or request into the content field—this is what the model will respond to.

4️ ^_(Optional) Adjust other optional parameters if needed

Only model and messages are required parameters for this model (and we’ve already filled them in for you in the example), but you can include optional parameters if needed to adjust the model’s behavior. Below, you can find the corresponding API schema, which lists all available parameters along with notes on how to use them.

5️ Run your modified code

Run your modified code in your development environment. Response time depends on various factors, but for simple prompts it rarely exceeds a few seconds.

If you need a more detailed walkthrough for setting up your development environment and making a request step by step — feel free to use our Quickstart guide.

API Schema

post

Body

application/json

modelstring · enumRequiredPossible values:

max_completion_tokensinteger · min: 1Optional

An upper bound for the number of tokens that can be generated for a completion, including visible output tokens and reasoning tokens.

max_tokensnumber · min: 1Optional

The maximum number of tokens that can be generated in the chat completion. This value can be used to control costs for text generated via API.

streambooleanOptional

If set to True, the model response data will be streamed to the client as it is generated using server-sent events.

Default: false

tool_choiceany ofOptional

Controls which (if any) tool is called by the model. none means the model will not call any tool and instead generates a message. auto means the model can pick between generating a message or calling one or more tools. required means the model must call one or more tools. Specifying a particular tool via {"type": "function", "function": {"name": "my_function"}} forces the model to call that tool. none is the default when no tools are present. auto is the default if tools are present.

string · enumOptional

none means the model will not call any tool and instead generates a message. auto means the model can pick between generating a message or calling one or more tools. required means the model must call one or more tools.

Possible values:

or

parallel_tool_callsbooleanOptional

Whether to enable parallel function calling during tool use.

ninteger | nullableOptional

How many chat completion choices to generate for each input message. Note that you will be charged based on the number of generated tokens across all of the choices. Keep n as 1 to minimize costs.

top_pnumber · min: 0.01 · max: 1Optional

An alternative to sampling with temperature, called nucleus sampling, where the model considers the results of the tokens with top_p probability mass. So 0.1 means only the tokens comprising the top 10% probability mass are considered. We generally recommend altering this or temperature but not both.

frequency_penaltynumber | nullableOptional

Number between -2.0 and 2.0. Positive values penalize new tokens based on their existing frequency in the text so far, decreasing the model's likelihood to repeat the same line verbatim.

presence_penaltynumber | nullableOptional

Positive values penalize new tokens based on whether they appear in the text so far, increasing the model's likelihood to talk about new topics.

response_formatone ofOptional

An object specifying the format that the model must output.

or

or

Responses

200Success

application/jsontext/event-stream

post

/v1/chat/completions

JavaScriptPythoncURLHTTP

async function main() {
  const response = await fetch('https://api.aimlapi.com/v1/chat/completions', {
    method: 'POST',
    headers: {
      'Authorization': 'Bearer <YOUR_AIMLAPI_KEY>',
      'Content-Type': 'application/json',
    },
    body: JSON.stringify({
      model: 'zhipu/glm-4.7',
      messages:[
          {
              role:'user',
              content: 'Hello'
          }
      ],
    }),
  });

  const data = await response.json();
  console.log(JSON.stringify(data, null, 2));
}

main();

200Success

{
  "id": "chatcmpl-CQ9FPg3osank0dx0k46Z53LTqtXMl",
  "object": "chat.completion",
  "created": 1762343744,
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "Hello! I'm just a program, so I don't have feelings, but I'm here and ready to help you. How can I assist you today?",
        "refusal": null,
        "annotations": null,
        "audio": null,
        "tool_calls": null
      },
      "finish_reason": "stop",
      "logprobs": null
    }
  ],
  "model": "zhipu/glm-4.7",
  "usage": {
    "prompt_tokens": 137,
    "completion_tokens": 914,
    "total_tokens": 1051,
    "completion_tokens_details": null,
    "prompt_tokens_details": null
  }
}

Code Example

Python

import requests
import json  # for getting a structured output with indentation 

response = requests.post(
    "https://api.aimlapi.com/v1/chat/completions",
    headers={
        # Insert your AIML API Key instead of <YOUR_AIMLAPI_KEY>:
        "Authorization":"Bearer <YOUR_AIMLAPI_KEY>",
        "Content-Type":"application/json"
    },
    json={
        "model":"zhipu/glm-4.7",
        "messages":[
            {
                "role":"user",
                "content":"Hello" # insert your prompt here, instead of Hello
            }
        ]
    }
)

data = response.json()
print(json.dumps(data, indent=2, ensure_ascii=False))

JavaScript

Response

{
  "choices": [
    {
      "finish_reason": "stop",
      "index": 0,
      "message": {
        "content": "Hello! I'm GLM, a large language model trained by Z.ai. I'm designed to have helpful, respectful conversations and assist with various tasks.\n\nHow can I help you today? Whether you have questions, need information, or just want to chat, I'm here to assist.",
        "reasoning_content": "Let me consider how to respond to this greeting thoughtfully.\n\nThe user has started with a simple \"Hello\" - this is likely the beginning of a conversation. I should respond in a way that's both welcoming and open-ended.\n\nFirst, I'll acknowledge their greeting warmly. Then I should introduce myself briefly to establish context. Since I'm GLM, an AI assistant, I should make that clear while also expressing my willingness to help.\n\nI should also consider what information might be useful to share at this point. My capabilities include answering questions, providing information, and assisting with various tasks. It would be helpful to mention some examples of what I can do.\n\nThe tone should be friendly and professional, inviting further interaction. I want to make the user feel comfortable asking questions or requesting assistance.\n\nLet me craft a response that's welcoming, informative, and encourages further conversation. I'll keep it concise but include enough detail to be helpful.",
        "role": "assistant"
      }
    }
  ],
  "created": 1766547128,
  "id": "20251224113151a94620120f9e4ebf",
  "model": "glm-4.7",
  "object": "chat.completion",
  "request_id": "20251224113151a94620120f9e4ebf",
  "usage": {
    "completion_tokens": 247,
    "prompt_tokens": 6,
    "prompt_tokens_details": {
      "cached_tokens": 2
    },
    "total_tokens": 253
  },
  "meta": {
    "usage": {
      "credits_used": 1149
    }
  }
}