Bringing greater intelligence grounded in real scientific workflows for the life sciences industry.

We’re introducing a new model update to our GPT‑Rosalind series purpose-built for life sciences research at enterprise scale. It combines GPT‑5.5’s agentic coding and tool-use capabilities with stronger model intelligence in core drug-discovery domains such as medicinal chemistry and genomics, while advancing performance across broader life sciences analysis, design, and experimental workflows.

Progress in life sciences depends on synthesizing data and evidence across scales and modalities: molecules, genes, pathways, and living systems. In our evaluations, the updated GPT‑Rosalind shows broad performance gains on research tasks from biology experts, complex medicinal chemistry queries, quantitative biology, and wet lab troubleshooting.

GPT‑Rosalind is now available in research preview to eligible organizations globally through our trusted-access deployment structure.

In order to measure and continuously improve the real-world impact of GPT‑Rosalind, we designed LifeSciBench, an externally expert-judged benchmark focused on foundational aspects in life sciences research. Unlike existing benchmarks that evaluate a single component of model performance or biological domain in isolation, LifeSciBench takes an end-to-end view of scientifically valuable work by drawing tasks from six workflow areas central to life sciences research: evidence handling, analysis, design and optimization, scientific reasoning, validation and operations, and translation and communication. We use this benchmark to align progress with the needs and realities of life sciences research.

GPT‑Rosalind leads performance across scientifically-valuable tasks identified by industry and academic experts.