Modern software relies on a multitude of automated testing and quality assurance tools to prevent errors, bugs and potential vulnerabilities. This study sets out to provide a head-to-head, quantitative and qualitative evaluation of six automated approaches: three industry-standard rule-based static code-analysis tools (SonarQube, CodeQL and Snyk Code) and three state-of-the-art large language models hosted on the GitHub Models platform (GPT-4.1, Mistral Large and DeepSeek V3). Using a curated suite of ten real-world C# projects that embed 63 vulnerabilities across common categories such as SQL injection, hard-coded secrets and outdated dependencies, we measure classical detection accuracy (precision, recall, F-score), analysis latency, and the developer effort required to vet true positives. The language-based scanners achieve higher mean F-1 scores,0.797, 0.753 and 0.750, than their static counterparts, which score 0.260, 0.386 and 0.546, respectively. LLMs' advantage originates from superior recall, confirming an ability to reason across broader code contexts. However, this benefit comes with substantial trade-offs: DeepSeek V3 exhibits the highest false-positive ratio, all language models mislocate issues at line-or-column granularity due to tokenisation artefacts. Overall, language models successfully rival traditional static analysers in finding real vulnerabilities. Still, their noisier output and imprecise localisation limit their standalone use in safety-critical audits. We therefore recommend a hybrid pipeline: employ language models early in development for broad, context-aware triage, while reserving deterministic rule-based scanners for high-assurance verification. The open benchmark and JSON-based result harness released with this paper lay a foundation for reproducible, practitioner-centric research into next-generation automated code security.