GLM‑4.7 is a brand‑new open‑source language model that has just hit the scene. It is the first model from Z.ai to score an impressive 87.4 on the τ²‑Bench, a test that looks at how well a model can use tools and follow instructions. In this article we will break down what makes GLM‑4.7 special, how it compares to other big models, and why it matters for developers and businesses that want to build smart AI tools.

What Is GLM‑4.7?

GLM‑4.7 is a large language model that can understand and generate text, code, and even help run other software tools. It is built on a new architecture that lets it keep track of long conversations and remember what it has done before. The model is open‑source, which means anyone can download it, run it on their own hardware, or tweak it for their own projects.

Key Features

  • Long‑Running Task Support – GLM‑4.7 can keep working on a task for a long time without losing context. This is useful for projects that need to run for hours or days, like data pipelines or automated research.
  • Stable Tool Calling – The model can call external tools (APIs, databases, code runners) reliably. It knows when a tool has finished and can use the result in the next step.
  • High Accuracy on Reasoning Tasks – On the τ²‑Bench, GLM‑4.7 scored 87.4, the highest score for any open‑source model that can use tools. This shows it can think through problems step by step.
  • Open‑Source Licensing – Anyone can use, modify, or redistribute the model. This encourages community contributions and faster innovation.

How Does GLM‑4.7 Compare to Other Models?

GPT‑4 and Claude

Large commercial models like GPT‑4 and Claude are known for their strong language skills. However, they are not open‑source and can be expensive to use. GLM‑4.7 offers a comparable level of reasoning and tool use, but it is free to download and run. For teams that need to keep costs low or want full control over the model, GLM‑4.7 is a good choice.

DeepSeek V3.2

DeepSeek V3.2 is another open‑source model that focuses on reasoning. While DeepSeek is strong on pure text generation, GLM‑4.7 shines when the task requires calling external tools. If your project needs to fetch data from a database or run code, GLM‑4.7 is more reliable.

IQuest 40B

IQuest 40B is a new open‑source coding model that scored 81.4% on SWE‑Bench Verified. It is great for writing code, but it does not have the same level of tool‑calling stability as GLM‑4.7. If you need a model that can both write code and manage external tools, GLM‑4.7 is a better fit.

Why GLM‑4.7 Is Important for Developers

  1. Cost Savings – Because it is open‑source, you can run GLM‑4.7 on your own servers or in the cloud without paying per‑token fees. This can save thousands of dollars for large projects.
  2. Customizability – You can fine‑tune GLM‑4.7 on your own data. If you have a niche domain, you can make the model speak your language.
  3. Tool Integration – The model’s stable tool‑calling makes it easy to build applications that need to interact with APIs, databases, or other software. For example, you can build a chatbot that pulls real‑time weather data or a data‑analysis assistant that runs SQL queries.
  4. Community Support – Open‑source projects often have active communities. You can get help on forums, GitHub, or Discord channels.

Building an AI Assistant with GLM‑4.7

Let’s walk through a simple example of how you can use GLM‑4.7 to build a small AI assistant that can answer questions and fetch data from an API.

Step 1: Set Up the Environment

# Install the required libraries
pip install torch transformers

Step 2: Load the Model

from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("zai/glm-4-7")
model = AutoModelForCausalLM.from_pretrained("zai/glm-4-7")

Step 3: Define a Tool

Suppose you want the assistant to fetch the current temperature from a weather API. You can define a simple function:

import requests

def get_temperature(city):
    url = f"https://api.weather.com/v3/wx/conditions/current?city={city}&apiKey=YOUR_KEY"
    response = requests.get(url)
    data = response.json()
    return data["temperature"]

Step 4: Create a Prompt

The prompt tells the model what to do and how to use the tool:

You are a helpful assistant. When asked for the temperature, call the get_temperature function with the city name. Return the result in a friendly sentence.

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Step 5: Run the Model

prompt = "What is the temperature in Paris?"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=50)
answer = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(answer)

The model will output something like:

The current temperature in Paris is 18°C. Let me know if you need anything else!

This simple example shows how GLM‑4.7 can be used to build a tool‑aware assistant. You can expand this by adding more tools, such as database queries or code execution.

Integrating GLM‑4.7 with Neura AI

Neura AI offers a platform that can help you deploy GLM‑4.7 quickly. Here’s how:

  • Neura Router – Connects to GLM‑4.7 and other models with a single API endpoint. You can switch models without changing your code.
  • Neura ACE – Automates content creation and can use GLM‑4.7 for research and drafting. It can pull in data from external sources and generate articles.
  • Neura Artifacto – A chat interface that lets you interact with GLM‑4.7 in real time. You can test prompts and see how the model behaves.

If you want to try Neura’s tools, visit the main site at https://meetneura.ai or explore the product overview at https://meetneura.ai/products.

Real‑World Use Cases

1. Customer Support Bots

A company can use GLM‑4.7 to build a chatbot that answers FAQs and pulls data from a CRM. Because the model can call tools reliably, it can fetch a customer’s order status or update a ticket in real time.

2. Data Analysis Pipelines

Data scientists can build pipelines that ask GLM‑4.7 to run SQL queries, clean data, and generate visualizations. The model’s long‑running task support means it can handle large datasets without losing context.

3. Educational Tutors

An online learning platform can use GLM‑4.7 to create interactive tutors that explain concepts, solve problems, and provide real‑time feedback. The model can call a math solver or a code runner to show step‑by‑step solutions.

4. Content Generation

Marketers can use GLM‑4.7 to draft blog posts, social media captions, or product descriptions. By integrating with Neura ACE, the process can be automated and optimized for SEO.

Performance Benchmarks

Model τ²‑Bench Score Open‑Source?
GLM‑4.7 87.4 Yes
DeepSeek V3.2 80.2 Yes
GPT‑4 92.1 No
Claude 90.5 No

GLM‑4.7 leads the open‑source field in tool‑use tasks. While it is slightly behind the commercial giants, its open‑source nature and stable tool calling make it a strong contender for many projects.

How to Get Started

  1. Download the Model – Visit the Z.ai GitHub page or the model hub to download GLM‑4.7.
  2. Set Up Your Environment – Install PyTorch, Transformers, and any other dependencies.
  3. Fine‑Tune (Optional) – If you have domain data, fine‑tune the model to improve performance on your specific tasks.
  4. Build Your Application – Use the example code above or integrate with Neura’s platform for a smoother experience.
  5. Deploy – Run the model on your own servers, a cloud instance, or a container.

Future Directions

Z.ai is already working on the next version, GLM‑5.0, which will bring even larger context windows and faster inference. The community is also contributing new tool‑calling plugins, making it easier to connect the model to more services.

Bottom Line

GLM‑4.7 is a powerful open‑source model that sets a new standard for tool‑aware reasoning. It offers a cost‑effective, customizable, and reliable option for developers who need a model that can keep track of long conversations and call external tools. Whether you’re building a chatbot, a data pipeline, or a content generator, GLM‑4.7 gives you the flexibility to create intelligent applications without the high price tag of commercial models.

If you want to explore more about how GLM‑4.7 can fit into your projects, check out the Neura AI platform at https://meetneura.ai or read our case studies at https://blog.meetneura.ai/#case-studies.