openai/MultiLangBench

Evaluate the accuracy of LLM generated outputs

XiYanSQL models for Text-to-SQL.

Online SQLite playground

Agentic text-to-SQL SDK: hand the LLM one execute_sql tool and let it explore the schema, test queries, and self-correct — no RAG, no semantic layer. 20/20 on an 80-table Spider run. Built-in tracing and scenario support.

Skills for AI agents working with databases

DuckDB Community Extension to prompt LLMs from SQL

Open-source text-to-SQL engine you tune and measure on your own database

We assessed the ability of popular LLMs to generate accurate and efficient SQL from natural language prompts. Using a 200 million record dataset from the GH Archive uploaded to Tinybird, we asked the LLMs to generate SQL based on 50 prompts.

[ICLR 2025 Oral] Spider 2.0: Evaluating Language Models on Real-World Enterprise Text-to-SQL Workflows