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Google Research's Gemini 3.1 Pro achieves a significant milestone in specialized capability development by scoring 80.04% on the BIRD single-model leaderboard for text-to-SQL tasks. This advancement demonstrates improved execution accuracy over prior iterations, validating the model's ability to handle complex business contexts and dirty data without relying on external agentic frameworks or ensemble methods. The breakthrough suggests that future integrations into Google Cloud services like BigQuery Studio will likely offer more robust natural language querying capabilities for enterprise users.

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Google Releases Gemini-SQL2: Gemini 3.1 Pro Text-to-SQL Scores 80.04% on BIRD Single-Model Leaderboard

MarkTechPost12 iunie 20265 min de cititArticolul și perspectiva sunt în engleză.

We look at Gemini-SQL2, the text-to-SQL capability Google Research announced on June 12, 2026. Powered by Gemini 3.1 Pro, it posted 80.04% execution accuracy on the BIRD single-model leaderboard. We explain what the score measures, how the leaderboard stacks up, and what Google has not yet disclosed. We also cover use cases and a schema-grounded implementation pattern. The post Google Releases Gemini-SQL2: Gemini 3.1 Pro Text-to-SQL Scores 80.04% on BIRD Single-Model Leaderboard appeared first on MarkTechPost.

Google Research team has announced the launch of Gemini-SQL2 on X. They described this system as a breakthrough text-to-SQL capability powered by Gemini 3.1 Pro. Gemini-SQL2 posted 80.04% execution accuracy on the BIRD Text-to-SQL Leaderboard (Single Model). Google’s chart places it above its own Gemini-SQL, the prior top entry. The metric measures whether generated SQL runs and returns correct results, not whether it looks valid. https://x.com/GoogleResearch/status/2065475343205740911 Gemini-SQL2 is a text-to-SQL capability, not a standalone foundation model…

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