Reasoning efficiency in Text-to-SQL is an important yet underexplored problem. Chain-of-Thought (CoT) reasoning models simplify traditional Text-to-SQL workflows and significantly improve the ability to handle complex queries. However, existing methods typically generate long reasoning processes for all queries. This leads to high token consumption and inference latency, increasing system cost and limiting practical deployment. To address this issue, we propose Ada-SQL, an adaptive reasoning model for Text-to-SQL that jointly optimizes reasoning mode selection and token budget control via a two-stage reinforcement learning framework coupled with a token budget model. This enables the model to adaptively decide whether to reason and how much reasoning budget to allocate based on query difficulty. Experimental results show that Ada-SQL reduces output token consumption by more than 65% while maintaining competitive execution accuracy, achieving a favorable performance-efficiency trade-off. Code and models will be released at https://github.com/sxhText2SQL/Ada-SQL.