GLM-4.5 Teardown: Is This the GPT-4 & Claude Killer We’ve Been Waiting For? 🚀

Alright fam, drop what you're doing and listen up. The AI world just got a major shake-up. While everyone was busy watching the usual suspects OpenAI, Anthropic, Google a dark horse just stormed the track. The mad lads at Z.ai just dropped a new model series, GLM-4.5 , and it's coming for the CROWN. 👑

They didn't just release one model; they dropped two: the heavyweight champ GLM-4.5 and its nimble, faster sibling GLM-4.5-Air. And the best part? They’ve open-weighted them, meaning they are available RIGHT NOW on HuggingFace for the community to use and build on. THIS IS HUGE.

So, is this just another model release with fancy marketing, or is it the real deal? We're going to do a full, no-BS teardown. We'll dive deep into the benchmarks, check out its coding skills, see if its 'agentic brain' is all that, and tell you exactly how to get your hands on it. No corporate fluff, just pure data and hype. Let's get into it! 🔥

The 30,000-Foot View: How Does GLM-4.5 Stack Up? (The TL;DR)

Let's not waste any time. The first question on everyone's mind is: how good is it, really? How does it compare to the titans like GPT-4, Claude 4 Opus, and Grok?

Here's the overall performance chart across 12 different benchmarks covering agentic tasks, reasoning, and coding. This is the big picture, the main event.

INSERT Image 1 HERE

Look at that. GLM-4.5 lands at a VERY respectable 3rd place overall. It's not just in the top tier; it's breathing down the necks of Grok 4 and Claude 4 Opus. And check out GLM-4.5-Air, the smaller, more efficient model it's sitting comfortably at #6, beating out giants like Gemini 2.5 Pro and even the new GPT-4.1. That's a massive statement about performance and efficiency.

But here's where it gets really interesting. If you look closer at the sub-categories in the chart, GLM-4.5 isn't the absolute #1 in any single area. So how did it get to #3 overall? Simple. It has no major weaknesses. While some models are coding specialists and others are reasoning whizzes, GLM-4.5 is a decathlete. It's consistently near the top in every single category. This isn't an accident; it's a deliberate design philosophy. The goal was to "unify all the different capabilities" into one model, and the data shows they've created a balanced powerhouse. Its "floor" is incredibly high across the board, making it ridiculously versatile for the complex, multi-domain agentic tasks that are the future of AI.

The Agentic Brain: Can It Actually Get Things Done?

Okay, high-level benchmarks are cool, but can the model do stuff in the real world? Can it use tools, browse the web, and act like a proper AI agent? This is where rubber meets the road.

On standard agentic benchmarks that test for tool use and multi-turn conversations, like TAU-Bench and the Berkeley Function Calling Leaderboard (BFCL-v3), GLM-4.5 is right up there with the best, matching the performance of Claude 4 Sonnet. This proves it can handle complex workflows like a pro.

For those who want the raw numbers (and you know you do), here's the full breakdown against the competition.

Benchmark

|

GLM-4.5

|

GLM-4.5-Air

|

Claude 4 Opus

|

o4-mini-high

|

Grok 4

|

Kimi K2

|
|

TAU-bench

|

70.1

|

69.4

|

70.5

|

57.4

|

67.5

|

62.6

|
|

BFCL v3 (Full)

|

77.8

|

76.4

|

61.8

|

67.2

|

66.2

|

71.1

|
|

BrowseComp

|

26.4

|

21.3

|

18.8

|

28.3

|

32.6

|

7.9

|

But the REAL test isn't a clean benchmark; it's the wild west of the internet. We're talking about BrowseComp , a super-tough web browsing benchmark from OpenAI that requires the model to answer complex questions by navigating websites. Here, GLM-4.5 scores an impressive 26.4% , clearly beating the much-hyped Claude 4 Opus (18.8%). That's a direct win against a top competitor in a messy, practical task.

And check this out the model seems to have a robust internal reasoning process. The more compute you give it at test time, the smarter it gets. This scaling graph shows its accuracy on BrowseComp steadily increasing with more "thinking time."

Now for one of the most IMPORTANT charts for any developer building agents. On the left, you see GLM-4.5 has the HIGHEST average tool-calling success rate at a whopping 90.6%. It's the most reliable model of the bunch. It just... works. But on the right... you see the catch.

This brings us to a crucial point that many might miss. GLM-4.5's best-in-class reliability is directly correlated with its higher token usage. It's the second-most "expensive" model per interaction. But this isn't a flaw; it's a fundamental design trade-off. The model is likely using its special "thinking mode" to generate more elaborate internal reasoning and plans before making a tool call. It's "spending" tokens to "buy" reliability. For a simple, low-stakes task, a cheaper model like Claude 4 Sonnet might be your pick. But for a mission-critical agentic workflow where a single failure can derail the entire process, paying the token "tax" for a >90% success rate isn't just a good deal it's a feature for pro-users.

Reasoning & Logic: More Than Just a Fancy Autocomplete

A big vocabulary is useless without a brain. So, can GLM-4.5 actually think? The model has a special "thinking mode" designed for complex problems, so we put it to the test on some of the toughest reasoning benchmarks known to man.

The numbers don't lie. Here's the showdown on everything from grad-level exams to math competitions.

Benchmark

|

GLM-4.5

|

GLM-4.5-Air

|

Claude 4 Opus

|

Grok 4

|

Gemini 2.5 Pro

|

o3

|
|

MMLU Pro

|

84.6

|

81.4

|

87.3

|

86.6

|

86.2

|

85.3

|
|

AIME24

|

91.0

|

89.4

|

75.7

|

94.3

|

88.7

|

90.3

|
|

MATH 500

|

98.2

|

98.1

|

98.2

|

99.0

|

96.7

|

99.2

|
|

GPQA

|

79.1

|

75.0

|

79.6

|

87.7

|

84.4

|

82.7

|
|

SciCode

|

41.7

|

37.3

|

39.8

|

45.7

|

42.8

|

41.0

|

Let's just take a moment to appreciate some of these scores:

• MATH 500: A staggering 98.2%. It's on par with Claude Opus and just shy of Grok 4. It's basically a math genius.
• AIME24: A very strong 91.0% , proving it can handle brutally difficult competition-level math problems.
• MMLU Pro: At 84.6%, it's firmly in the top league, demonstrating broad and deep general knowledge.

So, what's the secret sauce? How did it get so good at reasoning? It comes down to a specific architectural bet the Z.ai team made. In their research, they state they chose to make the model "deeper" (more layers) rather than "wider" (more experts or a larger hidden dimension). They hypothesized that "deeper models exhibit better reasoning capacity." The stellar performance you see in the table above, especially on multi-step logic-heavy tasks like MATH and AIME, is the validation of that bet. It's not just good at reasoning by chance; it was engineered to be good at reasoning because of this "deep > wide" philosophy.

CODE BEAST MODE: Can It Build Your Next App? 👨‍💻

Alright, my developer fam, this is the section for you. Can GLM-4.5 actually code? Can it fix bugs in a real repo? Can it build a full-stack app from scratch?

Short answer: OH YES.

Let's start with the benchmarks. We're looking at SWE-bench Verified (which involves fixing real-world GitHub issues) and Terminal-Bench (which tests its ability to use a command-line interface).

Benchmark

|

GLM-4.5

|

GLM-4.5-Air

|

Claude 4 Sonnet

|

Kimi K2

|

o3

|
|

SWE-bench Verified

|

64.2

|

57.6

|

70.4

|

65.4

|

69.1

|
|

Terminal-Bench

|

37.5

|

30.0

|

35.5

|

25.0

|

30.2

|

On SWE-bench, it scores a very solid 64.2%, putting it in the same league as the best. On Terminal-Bench, its 37.5% score is impressive, beating many specialized models.

But raw scores are only half the story. Efficiency matters. Look at this Pareto Frontier chart. It plots coding performance against model size.

You see that? GLM-4.5 and GLM-4.5-Air are sitting right on the "efficient frontier." This means for their size, they are delivering absolutely top-tier performance. You're getting MAXIMUM coding bang-for-your-buck.

Now for my favorite chart. Forget abstract benchmarks. This is a direct, head-to-head fight. Z.ai used the Claude Code framework to pit GLM-4.5 against other popular coding models on 52 real-world development tasks. The results are BRUTAL.

Let's break down this beatdown:

• It absolutely CRUSHES Qwen3-Coder with an 80.8% win rate. It's not even a competition.
• It soundly beats Kimi K2 with a 53.9% win rate. A clear victory.
• It's highly competitive with Claude 4 Sonnet. While Sonnet edges it out slightly, GLM-4.5 is holding its own against one of the best coding assistants on the market. This is a phenomenal result for a general-purpose model.

Get Your Hands Dirty: How to Use GLM-4.5 RIGHT NOW

Enough talk. Let's get building. Here’s your quick-start guide to using this beast. No excuses!

Option 1: The Easy Way (Z.ai Chat)

This is the fastest way to start.

1. Go to chat.z.ai.
2. Select GLM-4.5 or GLM-4.5-Air from the model dropdown.
3. Start prompting! You can try the full-stack dev agent, the slide creator, or just have a chat. It's all there.

Option 2: The Pro Way (API Access)

For the developers who want to build applications on top of this, the API is fully OpenAI-compatible. That means it's SUPER easy to integrate into your existing projects.

Here's a copy-paste-ready Python script to get you started:

# Make sure you have the openai package installed
# pip install openai

import os
from openai import OpenAI

# Get your API key from Z.ai after signing up
client = OpenAI(
    api_key="YOUR_Z_AI_API_KEY",
    base_url="https://api.z.ai/v1",
)

print("--- Sending a request to GLM-4.5 ---")
chat_completion = client.chat.completions.create(
    model="glm-4.5", # or "glm-4.5-air" for the faster model
    messages=,
    stream=True
)

# Let's see the magic happen!
print("\nResponse from GLM-4.5:\n")
for chunk in chat_completion:
    print(chunk.choices.delta.content or "", end="")

print("\n")

Option 3: The Chad Way (Local Deployment)

Want to run it on your own hardware? Want full control? The weights are OPEN! This is the way. 🚀

1. Head over to their HuggingFace page: huggingface.co/collections/zai-org/glm-45
2. Download the model weights for the variant you want (e.g., GLM-4.5-Chat).
3. Serve it locally using a framework like vLLM.

Here's a sample command to get a local API server running:

# Make sure you have vLLM installed (pip install vllm)
# Download the model from HuggingFace first!

# This command starts an OpenAI-compatible API server on your machine
python -m vllm.entrypoints.openai.api_server \
    --model zai-org/GLM-4.5-Chat \
    --tensor-parallel-size 4 # Adjust based on your GPU setup (e.g., 1 for a single GPU)

Under the Hood: The Secret Sauce (A Quick Geek-Out)

Ever wonder what makes these things tick? Here's a quick, non-boring look at the tech behind GLM-4.5.

• MoE Architecture: Instead of one single, giant brain, the model uses a Mixture-of-Experts (MoE) architecture. Think of it as a team of 'specialist' brains (the experts) and a smart 'router' that picks the right specialists for the job at hand. This is a key reason why it's so efficient for its power.
• The slime Framework: Training these massive models is HARD, especially for complex agentic tasks where the model has to interact with tools. Z.ai built a custom Reinforcement Learning (RL) framework called slime to do it faster and more efficiently. Its key innovation is decoupling data generation from the actual training. This means the expensive training GPUs are always firing at 100% utilization, never waiting for the model to finish a slow task. 100% utilization, baby! 🚀
• Post-Training Magic: It's not just about throwing a ton of data at the model once. They use a sophisticated multi-stage process. First, it's pre-trained on a massive 15T token general corpus. Then, it's fine-tuned on another 7T tokens of high-quality code and reasoning data. Finally, they use the slime framework for Reinforcement Learning to sharpen its agentic and reasoning skills to a razor's edge. It's a whole curriculum for an AI.

Still Curious? The Ultimate FAQ on GLM-4.5

1. Who actually built GLM-4.5, and when did it drop?

GLM-4.5 is the latest open-weight LLM from the Beijing-based AI powerhouse Zhipu AI (now globally branded as Z.ai ). It was unveiled to the world on 28 July 2025 , immediately making an impact on the AI leaderboards.

2. Is it really “open-source”?

Yes, completely. The full weights, configuration, and code are available on Hugging Face under a permissive MIT license. This means you are free to use, modify, and even ship it in a commercial product without the usual legal complexities.

3. GLM-4.5 vs. GLM-4.5-Air what’s the difference in plain English?

Think of it as same brains, different brawn. The flagship GLM-4.5 is the powerhouse with 32 billion active parameters for maximum performance. The more streamlined GLM-4.5-Air trims down to 12 billion active parameters , making it faster and more efficient, perfect for running on smaller rigs or for applications where response speed is critical.

4. How does it stack up against GPT-4 and Claude Opus on benchmarks?

It's a top contender. Across a dozen community and industry-standard benchmarks, GLM-4.5 consistently ranks in the top 3 overall. It punches right alongside models like Grok 4 and Claude Opus without any glaring weaknesses, performing like an "AI decathlete."

5. Can I run it locally, and what hardware do I need?

Yes, you can run both versions locally. For the full-fat GLM-4.5 model, you'll want some serious hardware, aiming for ≥80 GB of VRAM (like dual A100s). However, the hobbyist community isn't left out; tinkerers on Reddit have successfully run a 4-bit quantized version of GLM-4.5-Air on a single consumer-grade RTX 4090 with 24 GB of VRAM.

6. Does it really nail tool-calling for agents?

Absolutely. Z.ai reports an impressive 90.6% success rate for tool-calling out-of-the-box, the highest published figure to date. Its special "thinking mode" adds a few tokens to the cost but significantly boosts reliability, turning flaky agentic workflows into rock-solid, fire-and-forget pipelines.

7. How smart is it at pure reasoning and exams?

Its deep-stack architecture delivers on the "reasoning-first" promise. It achieves a remarkable 98% on the MATH 500 benchmark and breaks 84% on MMLU Pro , proving it's more than just a text generator it's a study buddy that can actually ace the exam.

8. Is coding its superpower or just a side-gig?

Coding is a headline feature, not an afterthought. It scores an impressive ~64% on SWE-bench Verified and boasts a Pareto-efficient balance of size and performance. In a head-to-head matchup against Qwen3-Coder, it wins over 80% of the time, making it a powerful and efficient coding assistant.

9. How do I hit the API?

It's incredibly straightforward. Just grab an API key from api.z.ai (or access it via OpenRouter). It uses an OpenAI-compatible endpoint, so you can point any existing SDK at https://api.z.ai/v1 and use the model glm-4.5 with zero code rewrites.

10. Is there a free tier, or is it pay-to-play?

You can get started for free. The web chat playground is generous enough for tinkering and exploration. For developers, Z.ai offers a free tier with a number of tokens upon registration. While heavy API use is metered, its pricing undercuts competitors like DeepSeek and GPT-4, making serious workloads significantly more affordable.

The Final Verdict: So, What's the Scene?

Let's wrap this up. After digging through the data, the benchmarks, and the tech, here's the final verdict on GLM-4.5.

• It's a top-tier, balanced powerhouse. It's not a niche specialist; it's a true generalist that excels across reasoning, coding, and agentic tasks.
• Its agentic abilities are S-tier , boasting the most reliable tool-calling in the game. This comes at a higher token cost, but for serious applications, that reliability is priceless.
• Its coding skills are formidable and incredibly efficient. The Pareto chart doesn't lie; it delivers maximum performance for its size, making it a smart choice for developers.
• Its reasoning is elite , a direct result of its "deep" architectural design.

So, back to the big question: is it a GPT-4 or Claude killer?

I'd say it's a legitimate contender for the throne. It might not win every single round in a 12-round boxing match, but it's in the ring, trading heavy blows with the champions on every single front. The fact that it's open-weight is a massive game-changer for the entire community, democratizing access to a truly state-of-the-art model.

The AI race isn't a two-horse race anymore. Z.ai has officially entered the chat, and they came to play. Your move, everyone else. The competition just got a whole lot spicier. 🔥