Testing GLM-5.2 on OpenCode: I'm impressed!
I have a confession: I roll my eyes at AI benchmarks. Every other week someone on Twitter posts a chart where a brand new model is suddenly beating Opus and GPT, the replies go crazy, and then you actually use the thing and it falls apart on the first real task. Beautiful numbers, ugly code. So when z.ai shipped GLM 5.2 and the timeline started shouting that an open-weights model was now nipping at the heels of the frontier labs, my instinct was the usual one. Sure. Prove it. This post is me proving it. I gave GLM 5.2 a real feature to build on my actual production website, with almost no hand-holding, and watched what happened. Spoiler: I was not expecting to write the sentence I ended up writing. If you'd rather watch me run the whole thing live (including the part where my tools crashed on camera), the video is right here: The claims I was here to test Let's get the hype out of the way first, because the claims are genuinely big. // Detect dark theme var iframe = document.getElementById('tweet-2066938937344495629-390'); if (document.body.className.includes('dark-theme')) { iframe.src = "https://platform.twitter.com/embed/Tweet.html?id=2066938937344495629&theme=dark" } GLM 5.2 is the same physical size as GLM 5.1 (744B total parameters, 40B active), but on the Artificial Analysis Intelligence Index it jumped 11 points, from 40 to 51. That score makes it the leading open-weights model , ahead of MiniMax-M3 (44), DeepSeek V4 Pro (44) and Kimi K2.6 (43). On the overall leaderboard it sits behind only Claude Fable 5 (60), Claude Opus 4.8 (56) and GPT-5.5 (55). For an open, MIT-licensed model you can download the weights for, that is a wild place to be. Here is the upgrade at a glance: GLM 5.1 GLM 5.2 Intelligence Index (v4.1) 40 51 Context window 200K 1M Total / active params 744B / 40B 744B / 40B Output tokens per task 26k 43k Cost per task ~$0.25 ~$0.46 Price (in / cache / out per 1M) $1.4 / $0.26 / $4.4 $1.4 / $0.26 / $4.4 License MIT MIT Two things jump out. First