Cyber threat intelligence (CTI) is crucial in today's cybersecurity landscape, providing essential insights to understand and counter the ever-evolving nature of cyber threats. The recent rise of Large Language Models (LLMs) have demonstrated potential in diverse applications including cybersecurity, but concerns about their reliability, hallucinations and truthfulness persists. While existing benchmarks provide general evaluations of LLMs, there are no benchmarks that address the practical and applied aspects cybersecurity-specific tasks. To address this gap, we introduce CTIBench, a benchmark designed to assess LLMs performance in cyber threat intelligence. CTIBench includes multiple datasets focused on evaluating knowledge acquired by LLMs in cyber-threat landscape. Our study evaluates several state-of-the-art models on these tasks, providing insights into their strengths and weaknesses in cybersecurity contexts.