GLM-5.2 Is The New Best Open Model

2023-08-29 · Source: Don't Worry About the Vase · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Software Development & Engineering · Depth: Advanced, long

Summary

GLM-5.2, a new open model, has emerged with impressive benchmarks, positioning it as potentially the strongest open model currently available. On Artificial Analysis v4.1, it scores 51, placing it near frontier models like Opus 4.7 (54) and GPT-5.4 (55), and achieving a speed index of 95. Its API costs are \$1.40/\$0.26/\$4.40 for input, cached input, and output, with monthly subscriptions from \$10 to \$160. While user reports highlight its strong coding, debugging, and long-context capabilities, often comparing it favorably to Opus 4.8 and GPT 5.5 for specific tasks, it is noted to be distilled from Claude Opus. This distillation suggests potential limitations in generalization and a tendency to overperform on benchmark-like tasks. Critics also point to its "benchmaxxed" nature, lack of common sense, and absence of native vision, making its practical niche tricky despite its strong performance.

Key takeaway

For Machine Learning Engineers evaluating open-source models for deployment, GLM-5.2 presents a compelling option with near-frontier benchmark performance, especially for coding and long-context tasks. However, you must account for its distillation from Claude Opus, which suggests potential limitations in generalization and a "benchmaxxed" performance profile. Prioritize rigorous testing on your specific, less common use cases beyond standard benchmarks to confirm its real-world utility and manage expectations regarding its broader applicability.

Key insights

GLM-5.2 sets a new benchmark for open models, but its Claude distillation implies generalization limits and a "benchmaxxed" performance profile.

Principles

• Distilled models often generalize poorly.
• Benchmarks may not reflect real-world utility.
• Open models can approach frontier capabilities.

In practice

• Evaluate open models for specific coding tasks.
• Consider distillation effects on generalization.
• Compare cost-performance for niche applications.

Topics

• GLM-5.2
• Open-source LLMs
• Model Benchmarking
• Claude Opus
• Model Distillation
• AI Regulation

Best for: AI Engineer, NLP Engineer, CTO, AI Scientist, Machine Learning Engineer, Director of AI/ML

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Editorial summary, takeaway, and curation by AIssential. Original article published by Don't Worry About the Vase.