GLM 5.2: why I’m replacing Opus in Claude Code with this new model

**Claire Vo** (0:00)
What if I told you, you could get Opus level reasoning at a fraction of the cost? That's what we're going to see and test today when I take a look at Glm 5.2. This is our first of many reviews of open-weight and open-source models to see if we should all be paying the tax to Anthropic and OpenAI, or if we can run these models locally and get the same results. Let's dive in. This episode is brought to you by Mercury. Banking redesigned from the ground up, now with Command. So you can just say what you need and the work gets done.
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Before I get Glm 5.2 running against some benchmarks on my own computer, my own projects, I want to talk you through what exactly this model is. Glm stands for General Language Model, and this is a model that's put out by the Beijing-based startup z.ai. So yes, this is a Chinese-trained model. Now, this model is open weight. You may have heard that term before, but not exactly known what it means. And all it means is that the trained model weights are publicly available for download, which allows you to run it on your own hardware. You can fine-tune that model on your own data, and you can inspect how it works. Now, the licenses for open weight models kind of depends, so it doesn't necessarily mean you can use it quote unquote for free. But why this matters for Glm 5.2 is you can self-host this. So let's say you have one of those chunky Mac studios at home, or you want to run some models locally, you could potentially run Glm 5.2 locally.
You can fine-tune it, which means you can adapt it to your purpose. And then the thing that people really love about these open weight models is they're cheap. You can run your own inference, and so it can be much more affordable than API costs from Anthropic or OpenAI. And two, you're not locked into a vendor. So if a specific vendor changes their API terms, you can actually change what provider you use. And there will be a lot of inference providers. I'm going to show one that you can use to run Glm 5.2. Now, why should you be paying attention to Glm 5.2? Well, this is what I'm hearing from the Breathless AI bros. And I guess, again, I am a Breathless AI brother, is that Glm 5.2 is getting people sort of Opus-level intelligence or GPT 5 point whatever intelligence for a fraction of the cost and the ability to be self-hosted. If this is true, this is a very big deal. As we've seen, we can't always rely on the big model providers for consistent model access. And these hyper-intelligent models from the frontier labs are getting very, very expensive. And so, any models where open weights models are catching up to the intelligence of open AI models, anthropic models, especially for coding use cases, which can be quite expensive, is something to pay attention to. Now, let's look at the benchmarks and capabilities of the model, and then I'm going to dive right into actually using them. So, a couple of things you should know about Glm 5.2 just to pay attention to is its context window is big. It has a million-token context window, so that's sufficient, but one limitation is it only takes text in and only takes text out. So, you can't put an image, you can't get out images, it is a text-to-text model. That is one constraint of this model. That being said, it has all the capabilities that you should expect from a modern-day model interface. It has reasoning or thinking mode, it can stream its outputs, it can call functions, it can do context caching to make things more efficient, and it can output structured output and use MCPs. So, at the end of the day, this is a very capable model with the right ergonomics that we've gotten used to. Now, what do we see from the market benchmarks on this model? So, as you see here on Frontier SWE, Post-Train Bench and SWE Marathon, it's inching up there to Opus and right above GPT 5.5 on a lot of these benchmarks. And if you look at SWE-Bench Pro, you can see it's about on par with GPT 5.5 and almost up to Claude Opus 4.8, certainly beating Gemini 3.1 Pro. So, if you look at it against these models that we've all come to know and love, it's in the arena. It's definitely worth testing out. And the external benchmarks say, this is a model with enough intelligence to attack some of our hardest coding problems. So, what I'm telling you is you can get this open-weight model where you can inspect how it's actually built. You can run it locally or at least more cost-efficiently on your own inference or your selected provider's inference. And it's going to code just as well as Opus 4.8. Let's do it. So, how do you actually get Glm 5 in your coding stack? Let's say you're completely new to all this open-weight model stuff, and you want to figure out how to run these in Claude Code or in Cursor. I'm going to show you Claude Code and Cursor. It basically applies to Codex as well. I'm just going to give you those two examples because I think that's going to cover most of your use cases. So first, you need to choose where you're going to get your model from. And I'm still using a hosted API version of Glm 5.2.