\textbf{Background:} Smart contract vulnerabilities pose significant security risks in blockchain systems. Automated static auditing tools are commonly used in practice, yet their effectiveness varies across vulnerability categories and at scale. Recently, large language model (LLM)-based tools have been proposed, but their performance is rarely evaluated against real-world contracts and alongside established analyzers. \textbf{Aim:} This paper presents a systematic benchmarking study of smart contract auditing tools under a single, explicitly defined task: identifying smart contract vulnerabilities in Solidity contracts. \textbf{Method:} We evaluate static and LLM-based tools ($n=7$) on two benchmarks: SmartBugs Curated ($n=143$) and a filtered FORGE subset of real-world audit-derived contracts ($n=173$). We use a tool-agnostic evaluation pipeline to normalize heterogeneous tool outputs, reporting accuracy, top-$K$ detection, execution robustness, and cross-dataset performance. \textbf{Results:} Our findings expose systematic trade-offs across auditing approaches. Static tools optimized for broad vulnerability coverage tend to over-report categories, potentially increasing triage burden, while exhibiting stability limitations on real-world contracts. LLM-driven auditors demonstrate strong coverage across many vulnerability categories, but similarly suffer from over-prediction. \textbf{Conclusions:} These results show that current smart contract auditing tools differ less in their ability to identify potential vulnerabilities than in their ability to do so precisely and reliably, motivating future auditing workflows for practical smart contract vulnerability detection.