Introduction

GLM-4.5 has arrived, promising a one-click package of “multimodal + coding + assistant” functionalities! But does the actual experience live up to the hype? In this hands-on review, we will reveal what works well and what falls short, providing an in-depth analysis of AI capabilities.

Have you ever faced such frustrations? Wanting AI to write a report, but Model A has good logic but poor writing style; needing it to write code, but having to switch to Model B; trying to automate a task, only to find you need to manually connect several tools… It feels like running back and forth in multiple kitchens just to prepare a dish.

As AI capabilities seem to become increasingly specialized, Zhipu AI has introduced its new flagship model, GLM-4.5, claiming to create an all-rounder that can do everything.

What has changed? Is it truly a game-changer or just hype? Today, we will conduct an in-depth test to uncover the truth!

Overview: What’s New in GLM-4.5?

In simple terms, GLM-4.5’s biggest ambition is to natively integrate various previously scattered superpowers into a single model.

1. Core Highlight: Native “Intelligent Agent” Capability

This is no longer just a “chatbot” that answers questions one at a time. GLM-4.5 is designed to understand complex goals, autonomously plan tasks, and call tools to execute multi-step actions, functioning as an “AI employee”. Officially, it claims to be the first SOTA-level native intelligent agent model.

2. “Trinity” of Versatile Capabilities

It integrates complex reasoning (like a strategist), code generation (like a programmer), and intelligent agent interaction (like a project manager) into a cohesive whole. The goal is to say goodbye to the “specialist” and become a “hexagonal warrior” capable of tackling any problem.

3. Completely Open Source and Affordable

Most importantly, both GLM-4.5 and its lightweight version, GLM-4.5-Air, have been fully open-sourced and are available on platforms like Hugging Face. The API call price is as low as 0.8 yuan per million tokens, drastically lowering the barrier for using high-performance large models, which is a huge boon for developers and small businesses.

Official Results & Community Response

Let’s take a look at the official results.

In 12 globally recognized hardcore tests, including graduate-level reasoning and complex software engineering problem-solving, GLM-4.5 scored third globally, ranking first among all domestic and open-source models.

This report card is quite impressive. After its release, community feedback was extremely enthusiastic: within just 10 hours, it surged to second place on the international open-source community Hugging Face hot list, setting a record for growth. Foreign media also focused on its “lower cost and better performance” features, considering it an attractive high-performance foundational model for global enterprises.

It seems that GLM-4.5 is indeed making waves. But how does it perform in reality? Let’s move into our “devil’s test” segment!

Hands-On Testing: Let’s See How It Performs!

Official data looks good, but nothing beats trying it out yourself. I designed several scenarios that best showcase its “versatile” features to give you a real feel.

Scenario 1: Intelligent Agent “One-Stop” Task — Let AI Be Your Secretary

I tasked it with: “Help me create a 15-page PPT report on the ‘2025 World Artificial Intelligence Conference (WAIC)’, requiring rich visuals and text, including highlights of the conference, main exhibitors, and future trend predictions.” My prompt input:

GLM-4.5’s execution results:

It first confirmed some basic information with me.

After planning the task, it asked if I needed to add anything, and I felt it was okay, so I chose none.

It then developed a task plan.

It searched for information online.

Each time it gathered information, it showed some impressive thought processes; let’s take a look at the final product.

(There are a total of 15 slides; I won’t display them all here, but the link will be provided below for you to check out.)

So far, so good. The color scheme and design of the PPT were consistent, which is impressive, but then…

The size of one slide was equivalent to the above two, and the visual experience still needs improvement…

Link: https://chatglm.cn/share/dFSqcxA7

My Review:

This round of testing was quite complex, with both positive and negative aspects.

The pleasant surprise is that it can indeed function like a real assistant, accurately understanding my complex needs, autonomously searching for information, and summarizing key points.

However, the downside is that during the PPT generation process, I found that the formatting size varied from slide to slide, leading to a somewhat uncontrolled final effect. Despite this, its demonstrated “one-stop” service potential remains a significant productivity tool for content creators and professionals, though it still needs further refinement in details.

Scenario 2: Zero-Code “Full-Stack Development” — Turn a Sentence into a Developer

The official demo shows generating websites and games with a single sentence, so let’s replicate this classic task: “Help me develop a playable ‘Flappy Bird’ game using HTML, CSS, and JavaScript.” My prompt input:

GLM-4.5’s execution results:

Here’s a part of the JS code.

Forgive me for not having gaming talent; anyone skilled in gaming can share screenshots in the comments.

Link: https://chatglm.cn/share/hFSPc4S0

My Review: The results exceeded expectations. It generated not just code but a complete game that can be played directly in a web browser! The code structure is clear, with adequate comments, and all core functionalities are implemented. Although the UI is simple, this fully demonstrates GLM-4.5’s incredible potential in code generation and application development, truly turning ideas into reality at the push of a button.

Scenario 3: Extreme Logical Reasoning — Challenging AI’s Brain

Finally, let’s present a tough question to test its logic and understanding of Chinese: “In the ‘Tengwang Ge Xu’, the phrase ’the setting sun and lone wild goose fly together, the autumn waters and the sky share the same color’ depicts dynamic or static? Please analyze from the perspectives of time-space view and aesthetics.” My prompt input:

GLM-4.5’s execution results:

Link: https://chatglm.cn/share/2FSDcHGn

My Review: Its answer was very profound, showcasing strong logical breakdown and multi-dimensional analysis capabilities. It accurately identified this as a “dynamic-static combination” classic phrase and analyzed it step by step from the dynamic-static relationship, time-space view, and aesthetics. The response not only cited the original text to support its viewpoint but also extended to the author’s Wang Bo’s life experiences and creative mindset, indicating that its understanding of the Chinese context, knowledge association, and deep thinking ability has reached a remarkably high level.

Conclusion: Is It Worth Getting?

After an in-depth experience, here are my thoughts on GLM-4.5:

👍 Pros

• Comprehensive capabilities beyond imagination: It truly achieves being an “all-rounder”; whether in office tasks, development, or creation, it can provide high-quality assistance, making it highly practical.
• “Promises kept” intelligent agent: The completion rate for complex, multi-step tasks is very high; it is no longer a “toy” but a “tool” that can be put into production.
• Exceptional cost-performance ratio: Powerful performance combined with open-source and low API prices allows all developers and enterprises to enjoy the benefits of top-tier AI.

🤔 Areas for Improvement

• Stability of generated content needs refinement: In executing multi-step, continuous generation tasks (like making PPTs), there may be issues with details going awry, such as PPT page formatting sizes varying, affecting the direct usability of the final results.
• Feedback for complex tasks could be clearer: When executing complex tasks like development or analysis, providing clearer, real-time progress feedback or displaying the “thought process” would greatly enhance user control and experience.
• UI aesthetics of generated applications could be improved: While the model can quickly generate fully functional applications (like games), the default UI is quite basic and has significant room for aesthetic design optimization.
• Tolerance for ambiguous instructions: When faced with extremely tricky or unclear instructions, the model’s performance can occasionally fluctuate, requiring users to describe their needs more precisely to achieve the best results.

In summary, Zhipu GLM-4.5 is undoubtedly a “heavyweight bomb” in the recent large model market. It not only achieves a “unified” technical approach but also, through open-source and low-cost strategies, sounds the horn for the popularization of AI applications.

For ordinary users and developers, a more powerful, cheaper, and versatile AI era is accelerating towards us.