Abstract
Motivation
Large Language Models (LLMs), AI agents and co-scientists promise to accelerate scientific discovery across fields ranging from chemistry to biology. Bioinformatics—the analysis of DNA, RNA and protein sequences plays a crucial role in biological research and is especially amenable to AI-driven automation given its computational nature. Here, we assess the bioinformatics capabilities of popular general-purpose LLMs on a set of tasks covering basic analytical questions that include code writing and multi-step reasoning in the domain.Results
Utilizing questions from Rosalind, a bioinformatics educational platform, we compare the performance of the LLMs on 104 questions relative to expert ground truth and 68 760 humans with varying levels of expertise. Among the frontier LLMs evaluated (GPT-5.2, GPT-4o, GPT-3.5, Llama-3-70B, Llama-4 and Claude Sonnet 4.6), GPT-5.2 exhibited top performance, providing correct answers for 80/104 (77%) questions followed by Claude Sonnet 4.6. LLM performance varied widely depending on the biological categories of questions. While the latest generation of frontier LLM models showed overall improvements in performance, their performance in specific categories of biological questions was lower than first generation models. Overall, LLMs performance mirrored that of humans with lower performance in tasks in which humans had low performance and vice versa. However, LLMs also failed in some instances where most humans were correct and, in a few cases, LLMs excelled where most humans failed. To the best of our knowledge, this presents the first assessment of general purpose LLMs on basic bioinformatics tasks in distinct areas relative to both expert and non-expert ground truths.Availability and Implementation
The complete analysis was implemented in Python; all code is available at https://github.com/SiwoResearch/lllm-bioinformatics.