ASAPGoogle Unveils Gemini-SQL2: A Text-to-SQL Model That Turns Natural Language Into SQL
Google unveiled Gemini-SQL2 in 2026 — a model that converts natural-language questions into SQL queries — and it recorded an execution accuracy of 80.04% on the BIRD benchmark. According to AI Times, that figure is a new record in the single trained-model category, raising the previous Gemini-SQL's 76.13% by about four points. Text-to-SQL lets even users who don't know SQL ask their data questions in everyday language and get answers.
What Is Gemini-SQL2?
Gemini-SQL2 is Google's text-to-SQL model that converts a user's natural-language question into an executable SQL query. According to AI Times, the model was developed on top of Gemini 3.1 Pro, released in 2026. When a user asks a question in plain language — for example, "Show me the top 10 best-selling products last month" — the model translates it into a SQL statement that can run directly against the database. The key is that you can work with data without knowing SQL syntax.
What Does 80% Accuracy Mean?
An accuracy of 80.04% means that about 80 out of every 100 SQL queries Gemini-SQL2 produced executed to the same result as the correct answer. According to AI Times, this figure is the execution accuracy on the BIRD benchmark, which measures not merely whether the query text looks similar but whether running it against the database actually yields the correct result. The 80.04% recorded in 2026 is 12.9 points below the human-expert level of 92.96%, showing that the model is still at a stage of assisting people rather than fully replacing them.
Which Benchmark Was Used?
Gemini-SQL2's performance was measured on the BIRD leaderboard, the standard evaluation in the text-to-SQL field. According to AI Times, the BIRD benchmark consists of 37 specialized domains, 95 databases, and 12,751 question–SQL pairs, with a total size reaching 33.4 GB. As of 2026, this benchmark is highly trusted because it handles complex queries close to real-world industry data, evaluating realistic data-analysis difficulty rather than simple examples.
What Is It Used For?
Gemini-SQL2 is used for enterprise business-data analysis and data-engineering work. According to AI Times, its 2026 use cases include enterprise business-data analysis, data engineering, and the "ask your data" feature of SaaS companies. Business users who can't work with SQL can query data directly in natural language, reducing the bottleneck of handing analysis requests off to the data team and waiting.
How Is It Different From the Previous Approach?
Gemini-SQL2 raised the BIRD execution accuracy from 76.13% to 80.04% compared with the previous Gemini-SQL. According to AI Times, the new model, built on Gemini 3.1 Pro, set a new record in the single trained-model category. The differences between the two 2026 models are shown in the table below.
| Category | Gemini-SQL | Gemini-SQL2 |
|---|---|---|
| BIRD execution accuracy | 76.13% | 80.04% |
| Base model | Previous-generation Gemini | Gemini 3.1 Pro |
| Gap from human experts | Larger | 12.9 points |
| Category record | - | New record for single trained models |