Diffusion Language Models (DLMs) enable iterative global refinement, but their effectiveness in Natural Language to SQL (NL2SQL) remains unclear because heterogeneous architectures and evaluation setups hinder fair comparison with autoregressive (AR) models. Their iterative denoising process permits sequence-wide revision, offering a different way to handle the long-range structural dependencies in SQL. We present a systematic study of DLM-based NL2SQL under a unified framework that standardizes generation and execution across DLM architectures. We further introduce SQL-D1, an agentic inference framework combining database-aware retrieval, multi-trajectory denoising, execution-guided verification, and selection based on execution denotations. Across diverse benchmark settings, strong DLMs are competitive with some open-source AR models and offer flexible trade-offs between efficiency and accuracy, although they still trail the strongest AR systems on difficult queries. Test-time scaling yields substantial gains, whereas standard supervised fine-tuning yields unstable outcomes across the evaluated DLMs. Stronger model variants exhibit fewer schema-grounding errors, but failures involving complex clauses and query nesting persist. These results clarify the capabilities and limitations of DLMs for database analysis and provide a standardized foundation for future work. Our code is available at https://anonymous.4open.science/r/SQL-D1-82DF.

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