GLM 5.2 vs Claude: benchmarks, pricing and who wins

A couple weeks ago, the US government forced Enthropic to shut down public access to their frontier AI model, Fable 5. But the very next day, on the other side of the planet, a Chinese lab did the exact opposite. It put its best model out for free. You can download it for free or run it on your own hardware.

And once you have it, no one can take it away from you. That model is called GLM 5.2 by ZAI, and it might just be the most slept on AI release of the year so far. So, in this video, we'll cover how powerful GLM 5.2 2 is how it compares to other frontier models like GPT and Claude and what it means for the future of AI. So, let's dive right in. So, GLM 5.2 comes from a company called Z.AI. If you've never heard of them, they're one of the bigger Chinese labs at the moment alongside names like Deepseek and Alibaba's Quinn. On June 13 of 2026, they dropped GLM 5.2. So, what makes it so special? First, let's talk about size. GLM 5.2 has around 750 billion parameters. Parameters are the internal settings a model tunes as it trains. The more it has, the more it can pick up.

So, it's a rough proxy for raw capability. If you want to go deeper on parameters and other stuff like this, I have a video on the 30 most important AI concepts you need to know. The link to that video is somewhere here on screen.

You can also find it in the description. Now, to put 750 billion in perspective, GPT3, the model the original ChatGPT was built on, ran on about 175 billion parameters. So, GLM is more than four times the size of the model that started all of this essentially. But raw size comes with a problem because bigger malls are obviously slower and a lot more expensive to run. GLM gets around that with a design called mixture of experts. To make it simple, instead of being a giant brain, it's split down into a bunch of smaller experts. For any given question, it only switches on the few that are actually relevant. And that typically takes on around 40 billion out of the 750 billion parameters that it has. Now, GLM didn't invent this. It's actually how a lot of the top models are built today. It's become the standard trick for getting frontier level intelligence without running frontier level costs. The second thing we should talk about is the memory. GLM 5.2 can hold about a million tokens at a time in its context window. This is on par with cloud opus 4.8 and GBT 5.5 which both sit around a million tokens, too. If you're new to AI, a context window is an LLMs working memory. Essentially everything it can keep in front of it in one single chat. Zai's previous models held around 200,000 tokens in their context windows. This is about five times more essentially. And the third thing is that it's open weights or open source in AI terms. Most US models are closed. So you never actually get the model itself. You run access through their servers and they keep the keys.

ZAI did the exact opposite. They released the actual model itself and it's free to download under a license that lets you run it, change it, and build a business on top of it. And as mentioned previously with open source models, once you have it, no one can pull it back anymore. So that's the general overview on GLM 5.2. Now we can take a closer look at its capabilities and feeds. A benchmark is just a standardized exam for AI. Every model essentially goes to the same tests, gets a score, and now you can actually compare two models that were built by different teams. And just like school, you have different exams for different skills. So for AI, that's primarily coding, math, reasoning, how well it follows, instructions, plus some other things. Obviously, the one thing to keep in mind is that a benchmark is like a proxy. It's like judging a chef by a single cooking test. It's a start, but it won't tell you the full story on how good of a cook they actually are based off one dish. So, the smart way to read benchmarks is to look at all the results holistically and watch for the patterns.

The pattern with GLM 5.2 is that it's a coding monster. The main tester here is called SWE Bench Pro. We've covered it in the past. On that test, GLM 5.2 scored 62.1. And for context, OpenAI's GPT 5.5 scored 58.6 on the same test by the lab's own reported numbers. So, a free model you can download off the internet beat one of OpenAI's flagship models at fixing actual production code.

There's another test called Terminal Bench, which checks whether a model can work like an actual engineer. So, not just write a snippet, but run commands, hit errors, fix them, and keep going essentially until the job is done. GLM 5.2 scored 81 on that one. While Clock Opus 4.8, the model most developers consider the best in the world at this, scored 85. So GLM didn't win that one, but it got within four points of the leader as a free model. And then there's stuff outside of pure coding. There's a leaderboard called design arena where real people are shown what different AIs build side by side and they vote on which one actually looks better. When it came to web design, GLM 5.2 took first place ahead of everyone, including Anthropics Fable 5. Now, one more scoreboard you should know about as well is the artificial analysis intelligence index. It takes a whole stack of these exams for reasoning, science, general knowledge, math, and blends them into one number for overall intelligence.

Now, on that scoreboard, GLM 5.2 scores around 51, and that makes it the single best open model in the world. Now, as you can see on the chart here, Claude Fable 5, Claude Opus, and GBD 5.5 are still ahead in this index, but GLM 5.2 takes the edge on all of these models you can see here, which just goes to show how impressive it actually is. As a quick disclaimer, you should know that a chunk of these numbers do come from ZAI itself or from early outside tests that are still being doublech checked. A few people have pointed out that independent results look slightly weaker than what's actually being reported. But the honest read is that GLM 5.2 is still the best open- source AI model out in the market right now. So that's a summary of the model and how good it is. But what does it mean if you actually want to use it?

Running GLM 5.2 2 costs roughly 16th of what GPT 5.5 or claude opus cost for the same work. The big US models charge around $5 per million input tokens and $25 to $30 on output tokens. GLM does the same job for about a dollar in and $3 or $4 out. Now, let's talk about actually getting your hands on it. GLM 5.2 is a 1.5 TB file. That's much bigger than the storage on most laptops, so the model wouldn't even fit. And to run it, the whole thing has to run on fast memory at once. And that means a server with eight top- end data center GPUs.

Now, this setup costs more than a car. So, for 99% of people, you can't actually run this thing yourself. Just like other AI models, there's two other ways you can use GLM 5.2. None of which requires any crazy hardware. The fastest one is to go to ZAI's website and just use it off the browser. The second way is through the API. And again, that's roughly a dollar per million tokens in and $4 per a million tokens out. Now, one thing to note is that Z.AI I servers run in China, so your data goes there.

If that's a problem, which for a lot of businesses it might be, there's a third choice. Because the model is open, a lot of Western companies host GLM 5.2 on their servers and usually ends up being very, very cheap. The only reason you'd actually host GLM 5.2 yourself is when your data legally can't leave your building or you're running such insane volume that actually owning the hardware beats renting it. So, that's basically everything you need to know about GLM 5.2 and I think honestly it's the start of something very, very impressive. If you rewind just 18 months ago, Deep Seek came out of nowhere and built a top tier model for a reported $6 million, which made Wall Street panic. So, Nvidia, the company that makes the chips all of this runs on, lost hundreds of billions in value in a single day. Back then, the question was, can China even compete with the US on AI? Now, the question is just about how far behind are they? And the honest answer from the people who track this closely is about 7 months.

And here's the crazy part in all this. China's essentially stopped trying to win America's game and they've started playing a different one when it comes to AI. The American strategy is to have their models be closed and premium. You build the smartest models and you charge top dollar for them. It's pretty much a page out of the Apple playbook. China has gone the complete opposite way.

Their top models are open and cheap and their strategy is to win on volume and price. By the middle of this year, most of the AI being used on the biggest neutral marketplace for these models was Chinese and open. The US still builds the smartest model, but China now sets the price of AI for everyone else. The US versus China AI race is a whole other video I'll make sometime in the future.

But if you had to take one thing away from this video, it's this. GLM 5.2 is not the smartest LLM on the planet. It probably won't even be the most impressive release of 2026. But in the grand scheme of things, it might actually be the most significant. Not just for what it can do, but for what it means for the future landscape of AI and technology as a whole. If you enjoyed this video, hit that like button and subscribe. If you want to get in touch with me directly, you can connect with me on LinkedIn. Links down in the description below. And if you're currently running a business and you're looking to integrate AI agents into your workflow, you can book an AI opportunity audit with myself and my team. That's the first link down in the description below. I'll see you in the next video.