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language 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 11, 2026

Models2
Model coverage2
MetricScore
EvidenceB

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  • FAQ

Spider Ranking

Higher score ranks better on this benchmark.

2 rows
Columns

Show columns

01MACodestral-22BMistral AI63.5%100.0%2CAug 11, 2026
02ACQwen3-Coder 480B A35B InstructAlibaba Cloud / Qwen Team31.1%0.0%2CAug 11, 2026

Spider Score Distribution

A closer view of the leading scores on this benchmark.

Spider

Spider Highlights

The leading models and scores on this benchmark.

Rank #1Codestral-22B63.5%Rank #2Qwen3-Coder 480B A35B Instruct31.1%

What is Spider?

What Spider measures and how its scores work.

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. This benchmark is not independently verified and has an evidence level of B.

Family
Spider
Modality
text
Primary category
language
Score direction
higher
LLMBoard eligible
No
Evaluation key
spider|llm-stats-current

Benchmark scores retain their original unit. Overall score eligibility is shown separately.

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 model results are currently shown.

Does this benchmark affect the overall score?

No. This benchmark is shown for reference but does not contribute to the overall score.