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

Introduction to GLM 4.7 Flash

Overview of GLM Models

  • The speaker introduces the video, highlighting their previous coverage of GLM models from versions 4.5 to 4.7.
  • Emphasizes that these models have been among the best open-weight options available.

Introduction of GLM 4.7 Flash

  • Introduces GLM 4.7 Flash as a groundbreaking model, describing it as a mixture of experts model with approximately 31 billion parameters, but only 3 billion active at any time.
  • Highlights its efficiency and power, positioning it as the strongest option in the 30B class.

Performance Benchmarks

AIM25 Benchmark Results

  • Reports that on AIM25, a math benchmark, GLM 4.7 Flash scores an impressive 91.6%, outperforming its competitor Quen 3-30B-A3B which scored only 85%.

Real-world Application Testing

  • On swbench verified for GitHub issue resolution, it achieves a score of 59.2%, significantly better than Quen's score of just 22%.
  • In Teu Squared Bench testing agentic capabilities, it scores 79.5% compared to Quen's lower score of 49%.

Comparison with Other Models

Comparison with Miniax M2.1

  • Compares GLM4.7 Flash favorably against Miniax M2.1, noting that while M2.1 had more total parameters (230 billion), it had fewer active ones (10 billion).

Critique of Previous Models

  • Reflects on past criticisms regarding other small models like code GX4 which performed poorly in tests.

Capabilities and Performance Insights

Tool Calling Efficiency

  • Shares personal experience configuring the model using kilo code for testing; successfully created a mind sweeper game on the first attempt.

Speed and Usability

  • Notes that due to having only three billion active parameters, inference speed is notably fast and suitable for real work applications.

Technical Features and Deployment

Advanced Features

  • Mentions support for speculative decoding using MTP and Eagle algorithms to enhance speed further.

Deployment Options

  • Discusses deployment through VLLM or SG lang with proper documentation available; emphasizes usability beyond benchmarks.

Conclusion: Future Directions in Model Development

Industry Trends

  • Advocates for developing smaller models capable of effective tool calling rather than simply increasing model size.

Accessibility

  • Encourages viewers to try out GLM4.7 Flash available on HuggingFace under an MIT license; highlights its affordability and effectiveness for coding tasks compared to larger models like GM4.7 (355B).
Video description

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. -- 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.