Text-to-SQL models translate natural language questions into SQL, enabling non-technical users to access databases. However, most existing research focuses on correctness, neglecting query efficiency. In this paper, we address the challenge of evaluating the execution efficiency of generated SQL in Text-to-SQL by introducing EESQLBench, a novel benchmark designed to assess both correctness and efficiency. EESQLBench pairs each natural language question with an expert-optimized SQL query, providing a reliable efficiency baseline. We evaluate six representative large language models (LLMs), including four open-source models (SQLCoder, CodeLlama, DeepSeek-Coder, and DeepSeek-R1) and two closed-source models (GPT-5.2 and Gemini-2.5-Pro), using cost-based metrics including Cost Reachability (CR) and Acceptable Reachability at $k$ (AR@$k$). Our results reveal that current LLMs, despite achieving high correctness, struggle to produce efficient queries. We observe substantial efficiency gaps between models and emphasize that semantic correctness alone does not guarantee query efficiency. Furthermore, we provide insights into common inefficiency patterns in LLM-generated SQL queries, such as missing access pruning and inefficient subquery logic.