🚀 LiveSQLBench-Base-Full-v1

A dynamic, contamination‑free benchmark for evaluating LLMs on complex, real‑world text‑to‑SQL tasks.

🌐 Website/Leaderboard • 📄 Paper (coming soon) • 💻 GitHub • 🗄️ LiveSQLBench-Base-Lite • 🗄️ LiveSQLBench-Large-v1 • 🗄️ Bird-Interact (ICLR 2026 Oral)

Maintained by the 🦜 BIRD Team @ HKU & ☁️ Google Cloud

📊 LiveSQLBench Overview

LiveSQLBench (BIRD-SQL Pro v0.5) is a contamination-free, continuously evolving benchmark designed to evaluate LLMs on complex, real-world text-to-SQL tasks, featuring diverse real-world user queries, including Business Intelligence (BI), CRUD operations, and more. Each release will include around 20 new, fully open-source DBs curated by the BIRD team through expert collaboration and continuous improvement. It will cover a wide range of database sizes, from end-user level (around 127 columns) to industrial level (~1000 columns). LiveSQLBench has been the infrastructure for the BIRD-Interact (ICLR 2026 Oral) , a dynamic human-centric interactive Text-to-SQL benchmark. Here are the features of the LiveSQLBench benchmark:

1. 🗄️ Live Databases: Constructed dynamically from extensive and regularly updated CSV datasets, with both base (user-end level) and large (industrial level) versions (1340+ columns each DB) to test scalability.

2. 💬 Live User Queries and SQL: Each task pairs unambiguous user queries with annotated, gold-standard SQL statements. The user queries are grounded in an external knowledge base, with medium to hard complexity solution SQL statements.

3. 🧠 Contextual Reasoning (HKB): Every DB includes a hierarchical knowledge base (HKB) where each knowledge may have dependencies to others, which requires the multi-hop reasoning ability. Two HKB formats are provided: (1) structured JSON format, and (2) unstructured Document format.

4. 🔍 The First Full SQL Spectrum: Supports not just SELECT (Business Intelligence) queries, but also CRUD (e.g., UPDATE, CREATE, and other database management operations) queries.

5. ⚡ Automated Evaluation: Support fast evaluation via PostgreSQL template & docker. Each question includes verifiable test cases for accurate, reproducible scoring. Soft EX metric is used to evaluate SELECT-ONLY tasks; customized test cases are designed for DBA tasks, such as CRUD (CREATE, READ, UPDATE, DELETE).

6. 🔄 Truly Live & Hidden Test: New databases and tasks are added over time. Each release features both open development and hidden test phases. The hidden test set from each release becomes the open development set for the next release, ensuring continuous evolution and fair evaluation.

Previous Releases: LiveSQLBench-Base-Lite

• LiveSQLBench-Base-Lite
• LiveSQLBench-Base-Lite-Sqlite

🎯 Current Release: LiveSQLBench-Base-Full-v1

Currently, we are pleased to release a LiveSQLBench-Base-Full-v1, containing 22 NEW end-user level databases with 600 NEW tasks (410 SELECT-only, 190 Management tasks), HKB-JSON and the JSON operation in SQL.

Some NEW features:

• More Natural User Tasks: User tasks are more colloquial and natural, making it implicit to mapping to the DB and KB. Some tasks are even reasoning-intensive. That means the model needs to reason more deeply and multi-hop to solve the task.
• More Real and Complex DBs: DBs are more real and complex, containing more N2M relationships and more noisy schema and data.

Changelog

2026-06-13

• Refined task metadata, gold SQL, and test cases for the following instances after data-quality review: cross_border_13, disaster_relief_M_1, households_M_2, households_M_6, households_M_7, households_M_8, households_M_9, museum_artifact_M_1, museum_artifact_M_3, museum_artifact_M_4, museum_artifact_M_5, museum_artifact_M_6, museum_artifact_M_7, museum_artifact_M_8, museum_artifact_M_9, museum_artifact_M_10, and solar_panel_M_5.
• Updated public livesqlbench_data.jsonl with the latest public fields while keeping protected fields empty.
• The latest GT/test-case file is livesqlbench_base_full_v1_gt_kg_testcases_20260613.jsonl, replacing livesqlbench_base_full_v1_gt_kg_testcases_0904.jsonl.
• For evaluation, please use the latest public livesqlbench_data.jsonl together with the latest GT/test-case file received by email.

💻 How to Use the Dataset

Get the Dataset

Download the dataset containing data file livesqlbench_data.jsonl and DB metafiles (including schema, HKB, column meaning files) by:

git clone https://huggingface.co/datasets/birdsql/livesqlbench-base-full-v1

❗️❗️❗️Get the Ground Truth & Test Case by Email❗️❗️❗️

To prevent data leakage through automated crawling, please request access to the ground truth and test cases by emailing 📧 bird.bench25@gmail.com with the subject line [livesqlbench-base-full-v1 GT&Test Cases]. An automated response will provide these data fields within 30 mins.

Get the Database DDL Dumps and Building Scripts

The complete PostgreSQL database dumps and building scripts (init-databases_postgresql.sh) can be download from the Google Drive.

Evaluation

The details of usage and evaluation can be referred to livesqlbench repo.

📁 Directory Structure

Each database has its own directory:

.
├── README.md
├── database_name
│   ├── database_name_column_meaning_base.json
│   ├── database_name_kb.jsonl
│   ├── database_name_schema.txt
...
├── livesqlbench_data.jsonl

📂 Directory Contents:

• *_schema.txt: Database schema.
• *_kb.jsonl: Hierarchical knowledge base entries required to solve the user task.
  • id: The unique identifier for the knowledge.
  • knowledge: The name of the knowledge.
  • description: The description of the knowledge.
  • definition: The clear definition of the knowledge.
  • type: The type of the knowledge.
  • children_knowledge: A list of knowledge IDs that the current knowledge is dependent on. -1 means no children.
• *_column_meaning_base.json: Explanation of database columns.

📋 Dataset Fields (livesqlbench_data.jsonl):

• instance_id: Unique task identifier.
• selected_database: Associated database name.
• query: More natural user query (which is used in evaluation and our leaderboard).
• normal_query: The normal user query, which is more concise and direct. Just for reference.
• sol_sql 🔒: Ground truth SQL solution.
• external_knowledge 🔒: IDs of required external knowledge to solve the user task.
• preprocess_sql: SQL setup queries.
• clean_up_sql: SQL queries to reset database state.
• test_cases 🔒: Test cases to validate the predicted corrected SQL.
• category: "Query" (SELECT-only) or "Management" (CRUD).
• high_level: Boolean indicating whether the user query contains high-level description.
• conditions: Indicates decimal/distinct conditions in the user query.
• difficulty_tier: Task difficulty (Simple, Moderate, Challenging).

🔒 Accessing Complete Data

To avoid data leakage by auto-crawling, certain fields (e.g., sol_sql, test_cases, external_knowledge) are excluded from the public dataset. For the full dataset, please email: 📧 bird.bench25@gmail.com with subject tag [livesqlbench-base-full-v1 GT&Test Cases], which will be sent automatically within 30 mins.

🏆 Model Performance on LiveSQLBench-Base-Full-v1 (2025-09-04)

Please refer to our homepage: 🌐 LiveSQLBench

🔄 Stay Tuned!

Upcoming releases:

• 🔄 LiveSQLBench-Base-Lite-Sqlite: SQLite version of LiveSQLBench-Base-Lite. No need to setup the docker.
• 🔄 LiveSQLBench-Base-Full: 600 tasks with more natural user queries and real DBs.
• 🔄 LiveSQLBench-Large-v1: Industrial-scale databases with ~1000 columns.
• 🔄 Keep it Live!: New DB envs and tasks will be added periodically with hidden test sets, keeping the benchmark truly live and contamination-free.
• 💻 LiveSQLBench-CLI: CLI-interactive variant adapted for coding agents (e.g., Claude Code, OpenHands), where agents explore databases and iteratively solve SQL tasks through terminal workflows.

Want new dialects? Vote for new SQL dialects 🗳️ here!

📄 License:

cc-by-sa-4.0

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license: cc-by-sa-4.0