AICellType: a large language model-based platform for accurate cell type annotation

Abstract Accurate cell type annotation is critical for studying cellular heterogeneity in single-cell and spatial transcriptomics. However, existing methods largely rely on static gene markers, limiting adaptability to diverse biological contexts and data types. To overcome this limitation, we systematically benchmarked 79 large language models (LLMs) over 1130 single-cell and spatial transcriptomics datasets using an evaluation framework combining ontology structure and semantic reasoning to quantify model performance in biological relevance and annotation robustness. Claude 3.5 Sonnet achieved the best overall performance, balancing weighted accuracy (76%), robustness, inference speed, and cost-efficiency. Based on these findings, we developed AICellType (https://AICellType.jinlab.online), a free, open-source R package and web platform that integrates seamlessly with Seurat workflows, supports multiple species and tissues, and enables flexible model deployment via OpenRouter or custom APIs. By leveraging LLMs’ capacity to interpret marker–cell type associations, AICellType provides a scalable, efficient, and accessible solution for real-world cell annotation in both single-cell and spatial omics research.