GLM-5.2 is probably the most powerful text-only open weights LLM

Chinese AI lab Z.ai released GLM-5.2, a 753B-parameter open-weights text-only LLM with a 1 million token context window, under an MIT license. The model leads the Artificial Analysis Intelligence Index among open-weights models and ranks second on the Code Arena WebDev leaderboard, but uses more output tokens per task than peers.

Chinese AI lab Z.ai https://z.ai/ released GLM-5.2 to their coding plan subscribers https://x.com/Zai org/status/2065704919299235870 on June 13th, and then yesterday June 16th released the full open weights under an MIT license. Similar in size to their previous GLM-5 and GLM-5.1 releases, this is 753B parameter, 1.51TB https://huggingface.co/zai-org/GLM-5.2 monster - with 40 active parameters Mixture of Experts . GLM-5.2 is a text input only model - Z.ai have a separate vision family most recently represented by GLM-5V-Turbo https://x.com/Zai org/status/2039371126984360085 , but that one isn't open weights. GLM-5.2 has a 1 million token context window, up from GLM-5.1's 200,000. The buzz around this model is strong. Artificial Analysis, who run one of the most widely respected independent benchmarks: GLM-5.2 is the new leading open weights model on the Artificial Analysis Intelligence Index https://artificialanalysis.ai/articles/glm-5-2-is-the-new-leading-open-weights-model-on-the-artificial-analysis-intelligence-index . GLM-5.2 is the leading open weights model on the Intelligence Index v4.1.At 51, it leads MiniMax-M3 44 , DeepSeek V4 Pro max, 44 and Kimi K2.6 43 They did however find it to be quite token-hungry: GLM-5.2 uses more output tokens per task than other leading open weights models:the model uses 43k output tokens per Intelligence Index task, up from GLM-5.1 26k and above MiniMax-M3 24k , Kimi K2.6 35k and DeepSeek V4 Pro max, 37k The model is also now ranked 2nd on the Code Arena WebDev leaderboard https://arena.ai/leaderboard/code/webdev , behind only Claude Fable 5. That leaderboard measures "front-end web development tasks, including agentic coding workflows". I'm impressed to see it rank so highly given the lack of image input, which I had incorrectly assumed was a key part of building a truly great frontend coding model. I've been trying it out via OpenRouter https://openrouter.ai/z-ai/glm-5.2 , which has it from 9 different providers, almost all of which are charging $1.40/million for input and $4.40/million for output. For comparison, GPT-5.5 is $5/$30 and Claude Opus 4.5-4.8 is $5/$25. GLM-5.1 gave me one of my favorite pelicans https://simonwillison.net/2026/Apr/7/glm-51/ and my all time favorite opossum https://simonwillison.net/2026/Apr/7/glm-51/ opossum for the prompt "Generate an SVG of a NORTH VIRGINIA OPOSSUM ON AN E-SCOOTER". Interestingly, in both of those cases the model chose to return SVG wrapped in an HTML document that added additional animations using CSS. Let's try GLM-5.2. For "Generate an SVG of a pelican riding a bicycle" I got this https://gist.github.com/simonw/5c989366b796f054d9ae1ad7e38dc03a : It's a self-contained fully animated SVG, and the animations aren't broken Often I'll see eyes falling off or wheels rotating independently of the bicycle but here everything works great. It's a very nice vector illustration of a pelican too. Very impressive. Sadly, the NORTH VIRGINIA OPOSSUM ON AN E-SCOOTER did not come out nearly as well https://gist.github.com/simonw/5913b56e3d0ba9a2ece75ce1471f87bb : This is such a step down from GLM-5.1 As a reminder, that possum looked like this: 5.2 didn't even try to animate it. Tags: ai https://simonwillison.net/tags/ai , generative-ai https://simonwillison.net/tags/generative-ai , llms https://simonwillison.net/tags/llms , pelican-riding-a-bicycle https://simonwillison.net/tags/pelican-riding-a-bicycle , llm-release https://simonwillison.net/tags/llm-release , openrouter https://simonwillison.net/tags/openrouter , ai-in-china https://simonwillison.net/tags/ai-in-china , glm https://simonwillison.net/tags/glm