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.
Codestral-22B from Mistral AI currently leads the Spider leaderboard with a score of 0.635 across 2 evaluated AI models.
What Spider measures
Spider is a text benchmark that evaluates large language models on language and reasoning tasks. LLM Stats tracks 2 models on this benchmark, with a maximum possible score of 1. Current average across reported models is 0.5, with the leader reaching 0.6.
Compare leaders on the best AI for language and best AI for reasoning leaderboards.
Publication
- Paper
- Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task
- Authors
- Tao Yu, Rui Zhang, Kai Yang, Michihiro Yasunaga, and 8 others
- Published
- arXiv
- 1809.08887
Abstract
We present Spider, a large-scale, complex and cross-domain semantic parsing and text-to-SQL dataset annotated by 11 college students. It consists of 10,181 questions and 5,693 unique complex SQL queries on 200 databases with multiple tables, covering 138 different domains. We define a new complex and cross-domain semantic parsing and text-to-SQL task where different complex SQL queries and databases appear in train and test sets. In this way, the task requires the model to generalize well to both new SQL queries and new database schemas. Spider is distinct from most of the previous semantic parsing tasks because they all use a single database and the exact same programs in the train set and the test set. We experiment with various state-of-the-art models and the best model achieves only 12.4% exact matching accuracy on a database split setting. This shows that Spider presents a strong challenge for future research. Our dataset and task are publicly available at https://yale-lily.github.io/spider
Codestral-22B leads with 63.5%, followed by 
Qwen3-Coder 480B A35B Instruct at 31.1%.
Progress Over Time
Interactive timeline showing model performance evolution on Spider
Spider Leaderboard
| Context | Cost | License | ||||
|---|---|---|---|---|---|---|
| 1 | Mistral AI | 22B | — | — | ||
| 2 |  Alibaba Cloud / Qwen Team | 480B | — | — |
FAQ
Common questions about Spider.
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