I noticed all the English-speaking KOLs in the AI field are sharing GLM-4.7—it seems super impressive. The discussion on Hacker News is more objective and detailed. Here are some of the current viewpoints for reference. == Pros == 1. Clear positioning: Focused on programming and Agents, not general chat models. 2. Effective MoE architecture: Small activation parameters, highly efficient compute power. 3. Strong long-context capability: 200k-level window, ideal for complex code and multi-file tasks. 4. Tool-friendly: Compatible with OpenAI-style tool calling, easy to integrate into existing Agent frameworks. 5. Preserved Thinking: Multi-turn state retention, better suited for long-chain Agents. == Cons == 1. Extremely high memory requirements: MoE saves compute power but not weight size. 2. Local interaction struggles: Prefill, offload, and KV cache are major bottlenecks. 3. Quantization side effects: Low-bit saves memory but causes significant stability and quality fluctuations. 4. Heavy engineering dependencies: Inference performance strongly relies on vLLM / SGLang / offload details. 5. Production reliability concerns: Hallucination rate, stability, and tool ecosystem are still weaker than mature closed-source models. Source: https://newshacker.me/story?id=46357287