New 25-Question SQL Benchmark for Evaluating Agentic LLM Performance
Key Takeaways
- ▸The benchmark runs in under 5 minutes and can differentiate performance across models at various capability levels, making it practical for rapid iteration
- ▸The agentic architecture with built-in error correction loops more closely reflects real-world usage patterns than traditional static SQL generation benchmarks
- ▸The tool is browser-based using DuckDB-WASM, allowing developers to evaluate models without requiring local infrastructure or long wait times
Summary
A developer has created a rapid benchmark for evaluating large language models (LLMs) on agentic SQL generation tasks, designed to address gaps in existing benchmarks. The benchmark consists of 25 English-to-SQL questions of varying difficulty (Trivial, Easy, Medium, and Hard) run against a Microsoft AdventureWorks sample database, with questions categorized from simple single-table SELECT statements to complex multi-join queries with aggregations. Unlike traditional text-to-SQL benchmarks that take hours to complete or are already saturated, this benchmark runs in under 5 minutes for most models while effectively differentiating performance across even the strongest LLMs.
What sets this benchmark apart is its explicitly agentic design, featuring a debug loop where the LLM can check results and self-correct errors before final validation. The system uses DuckDB-WASM to execute both the SQL queries and the benchmark itself directly in the browser, and scoring is based on result accuracy rather than SQL syntax, allowing for rounding tolerance. The developer built this tool while creating a self-hosted, in-browser agentic data analyst application and needed a practical way to evaluate which small models and quantization levels would work best for their use case.
- No single model achieves 100% accuracy, but every question is solvable by multiple models, providing meaningful differentiation across the difficulty spectrum
Editorial Opinion
This benchmark addresses a genuine pain point in LLM evaluation—the need for quick, discriminative tests that reflect agentic reasoning patterns. While focused on a specific domain (SQL generation), its emphasis on self-correction loops and practical browser-based execution could serve as a model for other domain-specific LLM benchmarks. The 5-minute runtime is particularly valuable for practitioners iterating on model selection and quantization strategies.
