The Neo4j Text2Cypher task aims to translate natural language questions into Cypher queries. The team analyzed the evaluation results from various angles, including overall performance, performance across different factors such as data source and database type, and common mistakes made by models. They found that outliers appeared in all the evaluation metrics, and assigned complexity levels to instances based on score distribution. Fine-tuned models showed improved performance compared to baseline models, but struggled with certain datasets and databases. The team identified common error groups, including additional matches, wrong ground truth, naming mismatches, confusion between WHERE and property conditions, and node vs. node properties. They also encountered surprising errors such as ambiguous questions, schema issues, and Cypher-specific challenges. The analysis highlights key areas where models struggle, mainly with the quality of ground-truth data and evaluation metrics, and will guide improvements to the dataset, models, and evaluation process in future updates.