Google Research’s Gemini-SQL2 tops text-to-SQL benchmarks by a wide margin

Researchers published new findings on Google Research’s Gemini-SQL2 tops text-to-SQL benchmarks by a wide margin: google Research’s Gemini-SQL2 turns natural language into executable SQL queries. Built on Gemini 3.1 Pro, it tops the BIRD benchmark at 80.04 percent accuracy, well ahead of OpenAI and Anthropic.

On the BIRD benchmark, which measures how accurately these translations work, Gemini-SQL2 hits an execution accuracy of 80.04 percent, putting it in first place, according to Google. OpenAI’s GPT-5.5-xhigh scores about 72.8 percent, and Anthropic’s Claude Opus 4.6 lands around 70.9 percent. Models from Databricks, AWS, Tencent, and Alibaba all trail well behind. Google Research points out that turning natural language into correct SQL is especially hard because data is often layered and queries need to account for complex business logic. The generated SQL queries both look correct and execute successfully, the company says. Better SQL understanding could improve natural language features across Google’s data services more broadly, according to Google. The research team hasn’t said anything about a public release of the model, and there’s no paper yet either.AdDEC_D_Incontent-1Ad Follow The Decoder for AI news, background stories and expert analyses.