GLM 5.2: What Makes it So Special?

GLM 5.2 Explained: 1M Context, MoE Efficiency, Sparse Attention & Cheap Inference In this video, I break down GLM 5.2 and why it’s one of the most impressive open-weight releases so far, focusing on the architecture behind its low cost and strong coding performance. I cover its MIT-licensed 744B Mixture-of-Experts design with 384 experts (about 40B active per token), the 1M token context window, and how sparse attention with an “indexer” reduces attention cost. I explain “index share,” which reuses indexing across four layers for 2.9× fewer compute ops at full context, plus multi-token prediction that boosts acceptance rate ~20% for faster inference. I also discuss thinking effort modes, agentic coding results like 74.4% on Frontier SWE, pricing vs US models, self-hosting, data-sharing concerns, and limitations like being text-only. My voice to text App: whryte.com Website: https://engineerprompt.ai/ RAG Beyond Basics Course: https://prompt-s-site.thinkific.com/courses/rag Signup for Newsletter, localgpt: https://tally.so/r/3y9bb0 Let's Connect: 🦾 Discord: https://discord.com/invite/t4eYQRUcXB ☕ Buy me a Coffee: https://ko-fi.com/promptengineering |🔴 Patreon: https://www.patreon.com/PromptEngineering 💼Consulting: https://calendly.com/engineerprompt/consulting-call 📧 Business Contact: [email protected] Become Member: http://tinyurl.com/y5h28s6h 💻 Pre-configured localGPT VM: https://bit.ly/localGPT (use Code: PromptEngineering for 50% off). Signup for Newsletter, localgpt: https://tally.so/r/3y9bb0 TIMESTAMP: 00:00 Why GLM 5.2 Matters 00:29 Efficiency Over Scale 01:02 MoE Architecture Explained 01:59 Million-Token Sparse Attention 04:07 Faster Output with Multi-Token Prediction 05:37 Benchmarks and Coding Strengths 06:29 Pricing Tradeoffs and Final Take