LiveSQLBench
Category
Agentic Coding
Score type
percent
Scale
100%
higher is better
Models tested
1
What is LiveSQLBench?
LiveSQLBench is a benchmark for how well a model works as an autonomous coding agent. It was built to compare language models on a clearly defined set of tasks under the same grading rules, so scores are directly comparable between models.
What does it test?
LiveSQLBench measures how well a model works as an autonomous coding agent. In detail, it checks:
- fixing real, open bugs from real software repositories
- planning multi-step changes, running commands and editing files by itself
- passing the project's test suite after its changes
- handling whole repositories, not just small isolated code snippets
How to read the score
The score is accuracy: the percentage of tasks answered or solved correctly. 100% means every task was solved perfectly; higher is better.
Frequently asked questions
What is the LiveSQLBench benchmark?
LiveSQLBench is a benchmark for how well a model works as an autonomous coding agent. It was built to compare language models on a clearly defined set of tasks under the same grading rules, so scores are directly comparable between models.
What does LiveSQLBench test?
LiveSQLBench mainly measures how well a model works as an autonomous coding agent. Concretely, it checks: fixing real, open bugs from real software repositories; planning multi-step changes, running commands and editing files by itself; passing the project's test suite after its changes.
How is the LiveSQLBench score calculated?
The score is accuracy: the percentage of tasks answered or solved correctly. 100% means every task was solved perfectly; higher is better. The declared maximum score of this benchmark is 100.0.
Where do I get LiveSQLBench data and scores?
The official benchmark page/reference is linked on this page. Every score on Open LLM Radar is also available as open data: download the scores JSON (http://open-llm-radar-api:8000/api/v1/benchmarks/livesqlbench/scores) or pull it directly from the Open LLM Radar API with any HTTP client.
Benchmark data & downloads
Where to get, inspect and reuse the data behind this benchmark.
Model scores 1 model tested
| # | Model | Score | Date | Source | Verified |
|---|---|---|---|---|---|
| 1 |
MiniMax-M3
MiniMaxAI
|
40.2%
|
11.06.2026 | MiniMax-M3 model card benchmark table | Unverified |