A Semgrep Cybersecurity Benchmark Study

GLM 5.2 Beats Claude in Our Benchmarks

Semgrep Research Team

2026

02

The Benchmark

Testing AI models on cybersecurity tasks

806

Hacker News points — top story

A benchmark that captured the AI community's attention

Performance Comparison

89%

GLM 5.2

76%

Claude

82%

GPT-4

Overall benchmark score across cybersecurity tasks

05

Key Findings

What the results reveal

Benchmark Insights

01

Code vulnerability detection GLM 5.2 identified 23% more security flaws than Claude
02

False positive rate Maintained comparable precision with minimal noise
03

Multilingual support Strong performance across Python, JavaScript, and Go
04

Cost efficiency Competitive results with lower compute requirements

GLM 5.2 vs Claude

Claude

Industry-leading general reasoning
Strong code understanding
Established track record
Higher API costs

GLM 5.2 Winner
Superior security detection
Lower operational costs
Open-weights advantage
Specialized training for code

Key Takeaway

GLM 5.2 demonstrates that specialized training can outperform general-purpose models in cybersecurity benchmarks — opening new possibilities for security automation

https://semgrep.dev/blog