Introducing GPT-Rosalind for life sciences research

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) .

Today, we’re introducing GPT‑Rosalind, our frontier reasoning model built to support research across biology, drug discovery, and translational medicine. The life sciences model series is optimized for scientific workflows, combining improved tool use with deeper understanding across chemistry, protein engineering, and genomics.

On average, it takes roughly 10 to 15 years to go from target discovery to regulatory approval for a new drug in the United States. Gains made at the earliest stages of discovery compound downstream in better target selection, stronger biological hypotheses and higher-quality experiments. Progress in the life sciences is constrained not only by the difficulty of the underlying science, but by the complexity of the research workflows themselves. Scientists must work across large volumes of literature, specialized databases, experimental data, and evolving hypotheses in order to generate and evaluate new ideas. These workflows are often time-intensive, fragmented, and difficult to scale.

We evaluated GPT‑Rosalind on a series of public benchmarks. On BixBench, a benchmark designed around real-world bioinformatics and data analysis, GPT‑Rosalind achieved leading performance among models with published scores.

The Life Sciences model is built to help scientific organizations do higher-quality work, faster, in environments that require both technical capability and operational control. Our dedicated Life Sciences team—as well as advisory partners including McKinsey & Company, Boston Consulting Group (BCG), and Bain & Company—help organizations identify high-impact use cases, integrate the model into enterprise environments, and drive measurable outcomes. If you’d like to explore ways OpenAI Life Sciences can support your work, you can contact our Life Sciences team ⁠ .

This is the first release in our Life Sciences model series, and we view it as the beginning of a long-term commitment to building AI that can accelerate scientific discovery in areas that matter deeply to society, from human health to broader biological research. We will continue improving the model’s biological reasoning, expanding support for tool-heavy and long-horizon research workflows, and working closely with leading scientific institutions to evaluate real-world impact. That includes ongoing partnerships with national laboratories such as Los Alamos National Laboratory, where we are exploring AI-guided protein and catalyst design, including the ability of AI systems to modify biological structures while preserving or improving key functional properties.

We evaluated GPT‑Rosalind across a range of capabilities fundamental to scientific discovery and industry research. These evaluations measure core reasoning across scientific subdomains, including chemical reaction mechanisms; protein structure, mutation effects, and interactions; and phylogenetic interpretation of DNA sequences. They also assess whether models can support real research workflows by interpreting experimental outputs, identifying expert-relevant patterns, and synthesizing external information to design follow-up experiments. Finally, they test whether models can select and use the right computational tools, databases, and domain-specific capabilities to augment their reasoning. Taken together, these evaluations show progress across the end-to-end process of scientific research and suggest a stronger ability to help researchers work through challenging discovery tasks.

Scientists can use our new Life Sciences research plugin ⁠ (opens in a new window) for Codex, available today in GitHub. This package includes a broad set of modular skills for most common research workflows, designed to help users work across human genetics, functional genomics, protein structure, biochemistry, clinical evidence, and public study discovery.

We believe advanced AI systems can help researchers move through these workflows faster—not just by making existing work more efficient, but by helping scientists explore more possibilities, surface connections that might otherwise be missed, and arrive at better hypotheses sooner. By supporting evidence synthesis, hypothesis generation, experimental planning, and other multi-step research tasks, this model is designed to help researchers accelerate the early stages of discovery. Over time, these systems could help life sciences organizations discover breakthroughs that wouldn’t otherwise be possible, with a much higher rate of success.

GPT‑Rosalind is available in ChatGPT, Codex, and the API for qualified customers through our trusted access program. We’re also introducing a freely accessible Life Sciences research plugin for Codex, helping scientists connect models to over 50 scientific tools and data sources. We are working with customers like Amgen, Moderna, the Allen Institute, Thermo Fisher Scientific, and others to apply GPT‑Rosalind across workflows that accelerate research and discovery.

The model is named after Rosalind Franklin, whose rigorous research helped reveal the structure of DNA and laid foundations for modern molecular biology.