reasoning benchmark
Spider
A large-scale, complex and cross-domain semantic parsing and text-to-SQL dataset annotated by 11 college students. Contains 10,181 questions and 5,693 unique complex SQL queries on 200 databases with multiple tables, covering 138 different domains. Requires models to generalize to both new SQL queries and new database schemas, making it distinct from previous semantic parsing tasks that use single databases.
Updated Aug 7, 2026
Published models2
Registry coverage2
MetricScore
EvidenceB
Spider leaderboard
Sorted by the source-provided rank. Higher score is better according to the registry.
| 1 | MA | 63.5% | 100.0% | 2 | C | |
| 2 | AC | 31.1% | 0.0% | 2 | C |
Score distribution
Top published rows on the benchmark's original scale.
Spider highlights
The top published results on this benchmark's own scale.
What is Spider?
Definition and scoring fields from the benchmark registry.
A large-scale, complex and cross-domain semantic parsing and text-to-SQL dataset annotated by 11 college students. Contains 10,181 questions and 5,693 unique complex SQL queries on 200 databases with multiple tables, covering 138 different domains. Requires models to generalize to both new SQL queries and new database schemas, making it distinct from previous semantic parsing tasks that use single databases.
Scores are shown in ratio. The current registry marks this benchmark as not independently verified with evidence level B.
- Family
- Spider
- Modality
- text
- Primary category
- reasoning
- Score direction
- higher
- LLMBoard eligible
- No
- Evaluation key
- spider|llm-stats-current
Source-native results are preserved. Eligibility for the overall LLMBoard score is a separate policy decision.
FAQ
Common questions about Spider.
Which model scores highest on Spider?
Codestral-22B is currently ranked first with 63.5%.
What does Spider measure?
A large-scale, complex and cross-domain semantic parsing and text-to-SQL dataset annotated by 11 college students. Contains 10,181 questions and 5,693 unique complex SQL queries on 200 databases with multiple tables, covering 138 different domains. Requires models to generalize to both new SQL queries and new database schemas, making it distinct from previous semantic parsing tasks that use single databases.
Is a higher score better?
Yes. Higher values rank better for this benchmark.
How many models are compared?
2 unique published model results are currently shown.
Does this benchmark affect the overall score?
This benchmark is preserved as source-native evidence but is not eligible for the current overall score.