Introducing new capabilities to GPT-Rosalind

Update on September 11, 2026 : GPT‑Rosalind , our specialized model for life sciences research, is coming out of research preview and is now available globally to eligible organizations through our trusted-access program. All eligible organizations will continue to get access to the latest Rosalind models as they’re released. Published pricing will take effect on October 5, 2026 . Review GPT‑Rosalind pricing⁠ ⁠ (opens in a new window) .

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.

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.

To better leverage Codex as a dynamic workbench for scientists, we added interactive viewers for biologically native file types. The initial set of sequence, alignment, and structure viewers are designed to keep scientists close to the evidence as GPT‑Rosalind reasons across a workflow and directly answer follow-up questions using the active viewer in-context.

The demo above shows these capabilities in action, orchestrated by GPT‑Rosalind. We follow a scientist investigating a liquid tumor biopsy to identify mutations and other molecular changes that could inform treatment. The Life Sciences NGS Analysis plugin turns a review of processed ctDNA records into an interactive notebook, surfacing recurring alterations, low-frequency calls, and sample trajectories that focus the investigation on KRAS G12C. From there, the Life Sciences Research plugin adds sourced target, inhibitor, and resistance context, while the native sequence, alignment, and structure viewers allow the scientist to inspect mutant residue 12, its conservation across the RAS family, and the inhibitor-bound pocket directly. The workflow concludes by translating that evidence into concrete follow-up options, with each step and artifact available for expert review.

As part of this global expansion, we’re excited to help support Novo Nordisk’s mission of bringing innovative treatment options to patients faster by helping scale their medical research with GPT‑Rosalind. Novo Nordisk is leveraging frontier AI capabilities to help researchers analyze complex datasets, uncover useful patterns, and test hypotheses more quickly. GPT‑Rosalind’s stronger biological understanding will help teams connect evidence across literature, genomics, transcriptomics, sequence, structure, and experimental results, making it easier to move from data to clearer research decisions.

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

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

GPT‑Rosalind achieves industry-leading performance in medicinal chemistry, a field focused on turning molecules into useful drugs. We designed MedChemBench to reflect realistic medicinal chemistry workflows, evaluating multimodal chemical structure understanding; structure-activity relationship (SAR); prediction of drug potency, toxicity, and absorption, distribution, metabolism, excretion (ADME); multiparameter lead-optimization decision-making; and retrosynthesis. GPT‑Rosalind out-performs GPT‑5.5 at 27.5% vs. 25.1% on MedChemBench, while using 7.2% fewer tokens.