In this video, I'll be covering the newly released GLM-4.7-Flash, a game-changing sparse MoE model that combines extreme efficiency with powerful tool-calling capabilities. I'll break down its architecture, compare it to Qwen3 and MiniMax, and show you why it's essentially "Gemini 3 Flash at home" for local deployment.

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Key Takeaways:

🚀 GLM-4.7-Flash is a 30B parameter model with only 3B active, making it highly efficient.  
📊 It significantly outperforms Qwen3-30B-A3B on benchmarks like AIME 25, GPQA, and especially SWE-bench.  
🛠️ The model excels at tool calling, successfully building a Minesweeper game in KiloCode on the first try.  
⚡ Supports speculative decoding with MTP and EAGLE for extremely fast inference speeds.  
🏠 Described as "Gemini 3 Flash at home," it brings sparse MoE power to self-hosted environments.  
📜 Released with an MIT license, making it fully open for commercial use and local deployment.  
📉 A massive improvement over previous small coding models like CodeGeeX4.

AICodeKing

GLM-4.7 Flash is a 30B-A3B mixture-of-experts model with 31 billion total parameters but only 3 billion active at once, making it highly efficient. It outperforms competitors like Qwen 3-30B-A3B on benchmarks including SWBench (59.2% vs 22%) and Tau Squared Bench (79.5% vs 49%). The model excels at tool calling and agentic tasks, supports speculative decoding, and can be self-hosted with an MIT license. It demonstrates reliable first-try tool calling capabilities and fast inference speeds, positioning it as the strongest option in the 30B class for coding and agentic work.

GLM-4.7 Flash (30B-A3B): This is THE BEST LOCAL AI CODING MODEL YET!