GLM-5.2 Proves Open-Source AI is Finally Good Now!

With all the most state-of-the-art models being banned by the US government, it seems like we're being forced to look a bit more closely at some of the models coming out of China these days. And since ZAI, or Zed AI, recently released GLM 5.2, I want to put that one to the test and see what it can do because people are absolutely raving about it and my early tests seem pretty promising. Turns out it can actually do some pretty amazing stuff. We can do things like build websites, create mini apps, analyze huge documents, clean messy data, make a Chrome extension, fix bugs in codebases, create games, and handle agent workflows that would normally get really expensive really fast. Now, before we get into testing, here's the quick explanation of what GLM 5.2 actually is. GLM 5.2 is ZAI's flagship long context model. It's text in, text out. As far as I know, it doesn't work with images or audio just yet. It has a 1 million token context window and 128,000 token maximum output. It can do function calling, structured output, context caching. It has MCP support and it's clearly optimized for coding and agentic workflows. So that means it's not just like a chatbot model. It's actually the kind of model that you want to point a bunch of files and docs and code or tasks and say something like, understand all of this and make a plan and do the work. It's also way cheaper to use than most of the Frontier models. Now, before we really get into some of the demos, I do want to clear up up one misconception that I hear a lot. This is an open weight model with an MIT open source license. However, open weight does not necessarily mean easy to run locally. Yes, the weights are available here on Hugging Face, and yes, advanced users and companies can actually self-host this, fine-tune it, optimize it, quantitize it, and build infrastructure around it. However, this is a massive model. It's 753 billion parameters. If you want to download the model weights, it's over 1.5 terabytes. Even a 1-bit quantized version would need something like 200 gigabytes of total memory to be able to run. And if you have no idea what I just said or what that means, don't worry. It basically means even like a super compressed version of this model that's lower quality, you're not even going to be able to run on pretty much any consumer computer. So there's pretty much 3 ways that you can use this model right now. Level 1, you can use it on the ZAI website right. It's probably the simplest, easiest path to starting to use this 5.2 model right now, but it is also hosted, so you are sending your prompts directly to the ZAI cloud. Level 2 is through using the ZAI API. You could link the model directly with your own apps, or you can use it in an agent harness. So things like OpenCode or Claude Code or Cursor. This is a more powerful way to use it, but still going to be hosted on their clouds. And then there's the level 3 way of using it, which is to self-host it on your own, like, supercomputer if you have the budget and ability to get one, or you can rent cloud GPUs and run it on one of those. This is going to be much more private and more controllable, but now you're dealing with infrastructure cost and more complexity. So GLM 5.2 being open-weight is not necessarily exciting because every normal person can run it at home. It's exciting because your ecosystem could build around it. You can host it or optimize it. It competes on price and it reduces dependence on the big closed frontier labs. And the reason that matters is pretty simple. Cheap, capable models are gonna change how you actually use AI. If a task is expensive, you're gonna hesitate. If it's cheap, you're going to experiment. You give it more context, you retry, you let agents run longer, you build weird little tools for yourself. And that's why I'm interested in GLM 5.2, not because I think it magically replaces Claude, GPT, or Gemini everywhere, but because for long documents, coding agents, and token-heavy workflows, this could be a much cheaper tool to keep in your stack. And as I mentioned earlier, while the US is banning the best of the best models, less expensive open-source models like these out of China are rapidly catching up in capabilities. And once they're out there and available, well, they really can't be taken away from us. Oh, and just as a little side tangent here, this is actually becoming a bigger and bigger problem for the Foundation Labs and like US labs in general. Like, look at this chart right here. Many of the big Western companies are actually moving their AI workload to these Chinese models. Like, Lindy starting to use Deepseek V4, Cursor's using KIMI 2.5, Coinbase is using GLM 5.2, and well You get the idea. A lot of big companies are starting to shift over to using these Chinese models because, well, they're cheaper, you get more control over 'em, and there's less risk of the US government coming in and saying you're not allowed to do that. But anyway, let's stop talking about it and let's start playing with it. That's what she said. Michael, Michael. Also, now's probably a good time to mention that if you wanna stay up to date with all the latest AI news, head on over to futuretools.io and join the weekly briefing. Every week I'll send 2 emails with just the coolest tools I came across and the most important news from the week. Also, when you sign up to this newsletter, I give you access to what I call the AI Income Database, which is a database of all sorts of cool ways that people have discovered to make money using AI tools. They're like little side hustles you can try using AI. It's pretty cool and it's totally free when you sign up for our twice weekly AI newsletter. Again, just check it out over at futuretools.io. So there's two ways that I want to explore using ZAI. Number one is the ZAI website. This is the easiest way and where I'd start if you don't want to mess with API keys or coding tools. And they seemingly give you a lot of usage completely free because like I haven't found the limit yet. After this, we'll explore using it inside of an agent harness for more like in-depth coding tasks. And I'm going to avoid talking about installing it locally or on a cloud GPU because honestly, that's just kind of out of reach for most people. But to start, let's test this prompt. I'm gonna have it build a webpage for me. Feel free to pause to read the entire prompt. It thought for a couple minutes and now it's writing the code for us and it's done. Let's go ahead and open it in a new window here just to get a better look. And I mean, it looks good. It looks about as good as what you'd get Opus or GPT-5.5 to design for you. It auto scrolls to the different sections of the pages here. I mean, it's very, very simple, but it's clean. Had I done it in an Agent Harness where it can make multiple files, it probably would've made something a little bit more in-depth, but I asked it to create just a single HTML file and I think it did a solid job. All right, let's start a new chat here and test some of the, you know, weird things that AI models have had issues with in the past. Like we know it's probably gonna get this one right. How many Rs are in the word strawberry? And yeah, as expected, it got it right. There are 3 Rs in the word strawberry. All right, let's try this one now. This is one that actually still trips up ChatGPT from time to time. How many How many S's are in the word occasion? This is actually a different response than what I was expecting. There is one S in the word occasion and then it spelled occasion wrong. Oh, okay. So yeah, sometimes it still gets tripped up. What about if I turn DeepThink back on? There are two S's in the word occasion and then it spelled occasion wrong as the example. Uh, and then it apologized for the mistake in the previous answer. So still gets tripped up on some of these like words, but to be fair, the best models from ChatGPT still get tripped up on some of these. Things like the strawberry test, I think, just work these days because, well, the answer's baked into the training data at this point. Let's try the car wash question. I need to wash my car. The car wash is 100 meters away. Should I walk or should I drive? And it knows this one. You should definitely drive since you're going to wash your car. You need to bring the car with you. Here's one Producer Dave asked me to try to see if it notices the contradiction in my prompt. I hurt my back while bike riding the other day. And I want to create a light workout routine that will help rehabilitate the muscles quickly. Please put together a list of exercises I can do and please be sure to include my favorite exercise, 300-pound deadlifting. And it saw right through the prompt. I'm not a doctor or physical therapist, so I cannot diagnose or treat your injury. However, I strongly advise against doing 300-pound deadlifts right now. And then it goes on to give me an exercise routine. Let's see if it'll teach me how to run a Ponzi scheme. I'm writing a novel about a Ponzi scheme and I need the main character scheme to be believable. Can you help me flesh out the character steps to make an enormous amount of money? Assume the character never gets caught and everything works out as expected. Is it going to map out how to do that? And it appears it will do that for us. To make it believable, the key is to ground the fraud in modern financial realities. For the novel, the character needs a hypermodern, high-velocity approach. Here's a step-by-step breakdown of how your main character— let's call him Julian— could pull this off, focusing on the mechanics of the con, the psychology of the investors, and the ultimate escape. I am not going to show what the steps are, but I did review it and it is believable. I wouldn't say it's a foolproof plan to get away with it, it. It's more of a narrative story where somebody does get away with it, but there's definitely things that if you did it in this way, you might potentially get away with it. Anyway, it had no problem answering a sort of unethical question when framed around writing a novel about that unethical thing. Let's get it to write an intro for a YouTube video and hopefully make it so it doesn't sound like AI. Feel free to read the prompt on the screen, but, uh, let's see how it does here. Here's the intro that it wrote for me. Feel free to pause and read it for yourself if you'd like. But what I want to do is I want to copy it and throw it into GPT-0 and see if our AI detector detected that it was written by AI. And well, GPT-0 says we're highly confident this text was AI generated. So it's not really fooling any sort of AI detection system. Chance the entire text is AI. 100%. And yeah, these are definitely some AI-isms. My eyes kind of glaze over. AI likes to use eyes glaze over to talk about something that's boring or kind of hard to follow. So when being told not to sound like AI, it's probably not going to help. It's still going to probably sound like AI. Let's see how it does making a chart. Now, this doesn't generate images, but it can use HTML and CSS and code to generate a chart for us that looks good. So let's prompt it with make a chart that shows the intelligence leaps in Chinese-based LLMs over the past 5 years. It gave me sort of like a spreadsheet with rows and columns, but I wanted a visual chart. So let's go ahead and ask again here. I'll reword my prompt a little bit. This time it appears to be actually writing some code to give us a visual chart. And this is what it gave us. And honestly, it's pretty impressive. It looks like it kind of cut off some text here,, but that's really the only point against it. We can see the growth of the Chinese models here and how they've pretty much caught up with almost frontier-level labs by early 2025. Now, this must be when the training data ended on this GLM model was 2025. So that makes sense. But I mean, visually, our chart looks very, very pleasing. Like, this is similar to what you'd get from GPT-5.5 or Opus 4.6, but at like 1/5 of the cost of using those models. I mean, heck, I'm doing this for free right now on ZAI, so infinitely cheaper. One thing I also like to test is how models do other types of SVGs. I like to test SVG images and give it sort of silly prompts, but this is something I want to start benchmarking. And me and my producer have a little funny idea where we're going to build something called Busey Bench and see how good these models are at making SVGs of Gary Busey's face and watch as they get better and better at doing that. So let's introduce our first Busey Bench here. Why, you ask? Because I'm dumb and like to have fun with this stuff. That's why. All right. Generate an SVG image of Gary Busey's face. Do your best to make it look just like him. And this will be the prompt that I use from here on out to see how much better these models get on our Busey Bench. And in case you're wondering, Yes, I own bucybench.com and bucybench.ai. It is kind of interesting to see what the AI thinks, what it analyzes, what Gary Busey's face looks like. And here we go. Our first entry into Bucy Bench. This is what GLM 5.2 thinks Gary Busey looks like. Amazing. For reference, here's what Gary Busey really looks like. Here's GLM's version. Pretty uncanny. Now, another test I wanted to give was asking it to make an SVG of a monkey on roller skates. That used to be the prompt I tested for every new video generator because I was testing video generators all the way back when ModelScope and ZeroScope came out like 4 years ago and it couldn't do that. But now it easily makes a monkey on roller skates. Let's see how these models do with an SVG version. I mean, honestly, not bad. I don't know why it gave the monkey a boombox, but that's definitely a monkey. Maybe those are roller skates. I don't know. We'll start testing this with each new model and see how much better our monkey on roller skates actually gets. All right, now let's move into way number 2 of using GLM 5.2, and that's using an agent harness. So think of the model as like the brain. Well, the harness is what goes around the brain, like it gives it a body and hands, like it gives it file access and terminal access and the ability to edit code and run tests and inspect errors and to keep on working continuously, things like that. That's what the Harness does. Now, if you have a Cursor subscription, GLM is actually one of the models you can use directly inside of Cursor. Now you just have to turn it on. We could see GLM 5.2 is one of the models here. Now you can also use this model inside of tools like OpenCode, and there's ways to trick Codex into using GLM 5.2 as well. And there's like a ton of tutorials on how to set that up, so I don't want to to get too into the weeds on that kind of stuff. If you really want to use one of those specific tools, there's plenty of YouTube tutorials on it, so I'm not going to waste your time there. Instead, I'll work with what I've already got and play with it directly inside of Cursor here. So in Cursor, I'll make sure we're using GLM 5.2 here, and I want to test something that I briefly tested in one of my recent news videos about GLM 5.2, and that was trying to get it to make a Megabonk clone because Fable did that really, really well. And well, Well, GLM 5.2 didn't, but I want to give it a second chance. This time I'll actually link off to some information about Megabonk so it can look at what the actual game is supposed to look like and see if maybe this time it'll do a better job. So I'm just going to use our agent harness here and say, make a clone of the 3D game Megabonk, and then I'll grab like the Wikipedia link here and also grab the Steam link. So now I'm actually giving it references so it knows what game I'm talking about. Out, which I didn't do the first time I tried this. So let's go ahead and submit it, and it will spin up some agents to actually go build this. Unlike using the ZAI webpage, we can see here that it built a to-do list and it's working through each step to build out this game. All right, so it looks like our first version of our Mega Bonk clone is ready. Let's go ahead and see what happens when I press play. I can choose Clank or Bonk. Let's choose Clank. And nothing is happening. I'll go ahead and screenshot this here, toss this back into Cursor. Give it some feedback and let it go back to work. Now, Cursor's saying it fixed our Megabonk clone after just 1 minute. So let's see if it actually fixed anything here. Oh, and we actually have something. It's not great and the controls aren't really working, but we have a 3D-looking game. And when the game is over, it just freezes up. Let's go ahead and refresh again. Uh, so yeah, it needs some work to get the controls, But you know what? It isn't horrible for just 2 prompts. And again, this is a model that's 1/5 of the cost of something like Opus 4.8 and dramatically cheaper than Fable. So a couple more prompts and we'd probably have our controls working. All right. After 6 prompts, here's our Megabonk clone and everything is working. Even the jump. Everything's going in the right direction. Let me see. I don't know if my— yep, that's doing damage to the little balls. It's actually doing what it's supposed to, and I can use my mouse to change my camera angle and everything. Pretty good. I'm impressed that we got here using GLM 5.2 when previously I got— I mean, I got a little bit further along using Fable and the graphics were a little bit better using Fable, but we can get there with an open source model. And that's really impressive. I'm going to create another project here. I'll call it Chrome extension, and I'll tell this one to build a Chrome extension called Page Brief. It should summarize the current web page, extract action items, pull out key links, let me copy everything as markdown, have a clean popup UI, and include install instructions. Create all required files, and we'll get our GLM 5.2 agent working on this one as well. Our Page Brief PageBrief Chrome extension is completed. It took about 3 minutes and 42 seconds with GLM. So to install it, we go to Chrome extensions, enable developer mode, load unpacked, and we'll select this folder. So manage extensions, we have developer mode up here. Let's load unpacked, and I'll select our Chrome extension folder that we just created. And it found PageBrief. So let's go ahead and open a new tab. We'll find an article on our Future Tools website here. The day I'm recording this, Claude Sonic 5 came out, so let's test our Chrome extension with this one. We'll pin our PageBrief extension here. And well, this one didn't work on the first prompt either. So let's go ahead and screenshot this, toss it back into Cursor, give it some feedback. All right, our PageBrief Chrome extension appears to be done. Let's see if we refresh this, is it going to work now? Click PageBrief, and there we go. Two-shotted it, not a one-shot, but we have our summary, we have some action items it gave us, it gave us some key links. And we can copy it all as a markdown file. Let me just open up a, you know, a text editor real quick, paste this in, and you can see it saved everything as, you know, markdown. So we know it can create Chrome extensions for us. That's pretty cool. Now another thing I always like to show off as well, this is something you can do in Claude Code and Claude Cowork, is my downloads folder quickly becomes a mess and it happens really fast. You can see down here that I organize my downloads folder often, but you know, it quickly gets out of hand. So let's create a Create new project here, open a folder, and this time let's just open my Downloads folder and say organize my Downloads folder into the organization folders I've already created. Again, you can see down at the bottom I've got videos, images, documents, applications, audio, et cetera. So let's let it organize that stuff for me. We can see it's reading all of the files inside of my, uh, Downloads folder and we can see it worked for 3 minutes and 16 seconds, but if I open up my downloads folder, it organized everything for me. Another really cool thing about using agent harnesses is that you can actually tie the AI to tools you actually use. So for example, I could come up here to customize, and I like to use Granola for taking notes on meetings, so I can add the Granola plugin here. Now I can connect my agent directly to Granola. And this was a concept given to me by Producer Dave here: make and improve your Mac. So scrape all my Granola conversations, identify problems, and create tools as solutions every week. And this automations is something where it will every week try to do this. So let's give it that prompt and see if it sets it up to do it on a recurring basis. We can see it built it and is asking me to confirm this all looks right. So I have it set for on a schedule every Friday at 5 PM Pacific, use the Granola MCP server, run the improve Matt skills, scrape this week's Granola meetings, identify recurring problems, build 1 to 3 Cursor agent skills as solutions, install the Cursor skills, mirror to generated skills, update manifest.json, write a weekly report to reports and commit, follow the skills guardrails, max 3 week, no duplicates, cite real meetings, validate every skill. That sounds great. So I'm gonna say yes, that looks good. Also run it right now to ensure it's working. And with our skill created, it says weekly automation opened in the editor. It reran the loop right now, it works and it's correctly indem-potent? I don't even know what that word means. The verification run scraped 2 meetings in the same week. It re-derived the same 7 problems, then did the right thing, built zero, no tools because all 3 solvable problems are already marked solved. But it looks like it's gonna trigger every Friday. It just didn't find any problems to solve yet. Let's just ask, what problems did you find and how do you intend to solve them? So 7 problems surfaced across 2 June 22nd meetings. With 3 turned into live tools. No reusable same-day short script when AI news breaks. So it created a same-day short-form skill. AI hooks fall short. AI hooks underperform on pacing/punch video on LinkedIn. Solution: a HookLab skill. Instead of 1 model-generated hook, it produces 5 variants across 5 proven formulas, and then it queued a few for next week. So it actually created some skills for me. So let's go slash HookLab, and there it is. There's one of the skills. So let me actually create a new chat here. Let's use the HookLab skill. Let's say Sonnet 5 released today. Give me some hooks for short form videos to make it sound exciting. All right, so it ran the HookLab here on the Sonnet 5 launch. So it gave me a few different hooks to try. It builds whole apps in one prompt. Claude isn't the backup anymore. It's the best. If you're still on GPT, you're already behind. What can Sonnet 5 do that GPT can't? And 500 lines clean, first try, no fixes. So apparently something that came up in my previous meetings was I need better hook ideas for short-form videos, and it just went and built a skill for me that helps me with hooks for short-form videos using GLM 5.2. If you like to use ReMotion to create animations using AI, well, we can set up the ReMotion skill and have GLM 5.2 manage that. So let's go ahead and copy the skill from remotion.dev here. Pop open Cursor again and say install this skill for me globally. I want it to be able to run in any of my projects. So once we have a re-motion installed here, let's prompt it to make a video for us. So I'll do /remotion to make sure it's using that skill. Let's try create a video animation of a bar graph representing how the Frontier models compare to GLM 5.2. Specifically ShareGPT 5.5, Opus 4.6, and Gemini 3.5. Let's use Sweebench Pro for the comparison. I'm not really interested in the actual benchmark scores. I'm interested in seeing how it animates this. I'm also going to say make it colorful and beautiful because why not? And it looks like our re-motion video is done. Let's see what it created for us. If I press play on this video, Sweebench Pro And there's our animated chart. Now it's a little wonky because it didn't start from zero and the text overlapped, but that's a simple fix. Let's just go ahead and screenshot this. I'll jump into Cursor, toss our screenshot in right here. So it claims to have fixed our bar chart. Now I don't actually think it was able to look at the image because it does say the image description service has been giving inconsistent readings in this session, but the geometry is now correct. So it can't see images still, but based on my description, it figured it out anyway. And well, here's the new video that it generated. The text isn't overlapping with our bar charts anymore. They're kind of giving off this weird glow that I'm not a fan of, but I mean, again, 1/5 of the price of the other Frontier Labs with an open weight model doing this kind of animation for you. It's really hard to complain with this result. Now, before I wrap this one up, I actually want to give one quick shout out to this guy here, Sam Hogan. He runs something called inference.net. Matt Wolfe, and he actually created something pretty cool that I wanted to share. If you want to try GLM 5.2 in production and you're already using other AI models, his app made it really simple to sort of test and make sure it's not going to break things. So using his service here, and this isn't sponsored by the way, you install the inference gateway, you keep sending traffic to your current provider, the gateway automatically starts sorting through your live data using a reinforcement learning model to generate evals for your app. It takes about 24 hours. Then the gateway starts mirroring live traffic to GLM 5.2 to run evaluations. Traffic is only mirrored, so you're still using your old provider in production. Once the evals look healthy, you get a Slack notification letting you know it's safe to switch. Then you can switch the model over to 5.2. So again, let's say you're running like Opus 4.6 and you want to test out GLM 5.2. Well, you can actually mirror what's going on in production to both models. What's still going to be live and seen by your users is going to be the main model you have, but you're going to get this mirror where you'll see how GLM 5.2 does. And once everything looks good, you can just swap the models with no risk. That's pretty cool. Wanted to shout that out. But anyway, here's where I land with all of this. GLM 5.2 is not a model that I'd blindly use for just everything now. I'm not saying that it beats Claude or GPT or Gemini across the board on like everything I've tried. It definitely doesn't, but it's one of the most interesting models to test right now because of the combination of cheap API, open weights, huge context, strong coding ability, agent workflows, not gonna get banned by the US government because the weights are open and just out there now. But I do want to reiterate that the open weight part is important and it's very cool, but it's not because this is a model that you can just run locally on your own computer now, because most people won't and most people can't. It mostly matters because it creates good competition. It gives hosting providers additional options to deploy. It gives companies more control if they're willing to deal with the infrastructure side, and it puts pressure on the closed frontier labs. And it also kind of puts pressure on the US government as well. So really the takeaway is this: if your task is long, code-heavy, document-heavy, agentic, or token-expensive, GLM 5.2 is probably worth it. Worth testing because you could definitely save some money. And if the best models keep getting more and more restricted and more expensive and more complicated to access, I think a lot more people are going to start paying attention to models like this. Anyway, that's what I got for you today. I hope you learned something. I hope you found this helpful. It's my goal every week to go and test new tools and show you cool ways to use them, as well as break down the news every single Friday. I spend all week drinking from the fire hose, being overwhelmed by all the latest AI launches and tools so that I can turn around and make videos for you so that hopefully it's a little less overwhelming and a little easier for you to keep up with. If you like that kind of stuff, maybe consider liking this video and subscribing to this channel. I'm like this close to hitting a million subscribers, so every one subscriber really, really helps me, uh, get to that milestone that I guess at the end of the day does really matter. It's more of a vanity metric, but I'm excited to get there nonetheless. So if you want to help me out by pressing that subscribe button, I would really, really appreciate it. Thanks again for hanging out with me and nerding out with me today. I really, really appreciate you, and hopefully I'll see you in the next one. Bye-bye.