GLM 5.2 outperforms Claude in Semgrep security benchmarks

Semgrep’s June 28 benchmark pits GLM 5.2 against Anthropic’s Claude on a suite of security‑focused code‑analysis tasks. GLM 5.2 delivered lower latency and higher throughput across every metric, giving engineers a clear performance edge [Semgrep Blog].

Benchmark methodology

The blog post explains that the test harness evaluated both models on a corpus of 10,000 real‑world code snippets containing known security patterns. Each model generated detection rules, and the system measured end‑to‑end response time, CPU usage, and the number of snippets processed per second. The same prompt template and hardware configuration (dual‑CPU, 64 GB RAM) were used for both runs, ensuring a fair comparison [Semgrep Blog].

Results

Across the board, GLM 5.2 completed the workload faster than Claude, shaving milliseconds off average latency and handling a larger volume of snippets per second. In the CPU‑intensity test, GLM 5.2 stayed under the 70 % utilization threshold where Claude hovered near 85 %. The benchmark also recorded fewer timeout incidents for GLM 5.2, indicating more stable performance under load [Semgrep Blog].

Implications for model selection

For teams that embed LLMs in security tooling, raw speed translates directly into quicker vulnerability detection and lower operational costs. The data‑driven gap shown here suggests that GLM 5.2 is currently the more pragmatic choice for high‑throughput scanning pipelines. At the same time, the benchmark underscores how quickly the LLM landscape can shift; today’s leader can be overtaken as models evolve and new optimizations emerge.

Poll: Which large language model do you currently trust for your projects?

• GLM 5.2
• Claude
• Other (please specify in comments)