GLM 5.2 Clearly Explained (and how to set it up)

**Greg Isenberg** (0:00)
Okay, you've probably heard of Glm 5.2 that's going viral everywhere on Twitter. Yes, it's this new open source local AI model that people are saying is the ChatGPT moment for local AI. But no one's actually gone and shown you how to use it, and how do you actually set it up. So I figured I'd bring on my friend Amir. He tells us exactly how you should think about running Glm 5.2, how you should think about running local models, how that integrates to something called OpenRouter, how you can use it with your Codex or Cursor or Cloud Code. In this episode, in 20 minutes or less, you're going to get everything you need to know about local AI models, why Glm 5.2 is crushing benchmarks, and how you can set it up today, so you can go and build your startup and build your business, and be more productive and be more efficient. Enjoy the episode and I'll see you at the end of it. Hit a like, comment and subscribe for more of this sort of stuff in your feed. Enjoy.
Welcome to the show, Amir.
By the end of this episode, what are we going to learn?

**Amir** (1:14)
We're going to talk about, essentially learn about how local models are kind of keeping up now with the pace of these closed models as well, and how you can kind of use compounding models or fusion models as OpenRouter calls it to be able to do sequencing between a more extensive thinking model and a more execution-based model. We'll show you how GLM 5.2 actually compares and stacks up against other models, and how you can effectively use it and get set up with it as well.

**Greg Isenberg** (1:41)
Cool. I did an episode on local models. It was a hit. People wanted more tactical.
How should I think about local models? How do I implement local models? How I can actually make this a part of my daily workflow. So I brought on Amir. Welcome to the show. I give you two welcomes, by the way. That's how excited I am to have you share with everyone everything. So let's get into it.

**Amir** (2:07)
I'm super excited to be here. Let's jump right into it. So let's talk about what happened this week. Zed AI came out with Glm 5.2.
I think this was a big inflection point because we've typically seen with local models, it's either storage-extensive that you can't essentially run it or install on your computer, or you need a better GPU RAM performance to be able to actually run it locally as well.
Now, Glm 5.2 is also resource-extensive, but what we're seeing is with open source models like open source providers like OpenRouter or Lama, being able to help you run these models in the cloud and effectively being able to essentially pay slightly less for input and output tokens compared to the more closed models. Now, what I want people to take away from this session is, one, how to actually get set up with it. We're not going to go through the detailed setup process, but I'm going to just cover how you can do it in Cursor using OpenRouter or in Codex, and then effectively talk about how Glm 5.2 stacks up against the other models, and then how you can effectively use it as well to do model chaining. And then we'll do model chaining, and then we'll do just kind of a maybe a quick walkthrough of how I'm currently using it. You know, I want to be very honest, like these local models still have a lot of work to do in terms of having tool capabilities to be able to have the modalities to be able to see images and conceptualize on what they're looking at, and I'm going to tell you how you can effectively circumvent some of that, where you can use other models to explain what the image is back to GLM and then have GLM work on it. And also just have a very live test on how this stacks up against other models. You know, benchmarks are great. Personally, I'm not an expert in it. I don't know what any of these benchmarks actually mean. The way I do is off-laps, like let's build it out and see how this actually looks and how it stacks up against other models. Sound good?

**Greg Isenberg** (4:00)
Yes, sir.

**Amir** (4:01)
Let's do it. Okay, so Glm 5.2 came out and essentially it has a 1 million context window and it scores 81 points on the Terminal Bench 2.1. It's just about four points behind Opus 4.8. And it does quite well on the long horizon task evaluation. So this is essentially projects you have that you want to run long sequence tasks on. And you know, I think it's taking an account like the thinking parameters and how it can think through and plan through some of the tasks it has ahead. So you can see that across all these different kind of benchmark reporting or reports.