GPT-Rosalind life sciences is a purpose-built artificial intelligence model introduced by OpenAI to support research across biology, drug discovery, and translational medicine. The model combines improved tool use with deeper understanding across chemistry, protein engineering, and genomics to accelerate scientific workflows.
The model addresses a critical bottleneck in pharmaceutical development. On average, it takes 10 to 15 years to move from target discovery to regulatory approval for a new drug in the United States. Progress in early discovery stages compounds downstream, leading to better target selection, stronger biological hypotheses, and higher-quality experiments.
Scientists currently work across large volumes of literature, specialized databases, experimental data, and evolving hypotheses. These workflows are often time-intensive, fragmented, and difficult to scale. GPT-Rosalind is designed to help researchers move through these workflows faster by supporting evidence synthesis, hypothesis generation, experimental planning, and other multi-step research tasks.
GPT-Rosalind life sciences capabilities
The model delivers strong performance on tasks requiring reasoning over molecules, proteins, genes, pathways, and disease-relevant biology. It is more effective at using scientific tools and databases in multi-step workflows such as literature review, sequence-to-function interpretation, experimental planning, and data analysis.
OpenAI evaluated GPT-Rosalind across a range of capabilities fundamental to scientific discovery. These evaluations measured core reasoning across chemical reaction mechanisms, protein structure and mutation effects, and phylogenetic interpretation of DNA sequences. The assessments also tested whether the model could support real research workflows by interpreting experimental outputs and synthesizing external information to design follow-up experiments.
Performance benchmarks and industry evaluations
On BixBench, a benchmark designed around real-world bioinformatics and data analysis, GPT-Rosalind achieved leading performance among models with published scores. On LABBench2, which measures performance on research tasks such as literature retrieval, database access, sequence manipulation, and protocol design, the model outperforms GPT-5.4 on 6 out of 11 tasks.
The most notable improvement comes from CloningQA, which requires end-to-end design of DNA and enzyme reagents for molecular cloning protocols. OpenAI partnered with Dyno Therapeutics to evaluate the model on RNA sequence-to-function prediction and generation tasks using unpublished sequences. Best-of-ten model submissions ranked above the 95th percentile of human experts on the prediction task and around the 84th percentile on the sequence generation task.
Availability and access structure
GPT-Rosalind is now available as a research preview in ChatGPT, Codex, and the API for qualified customers through a trusted access program. OpenAI is also introducing a freely accessible Life Sciences research plugin for Codex, helping scientists connect models to over 50 scientific tools and data sources.
The model launches through a trusted-access deployment structure for qualified Enterprise customers in the United States. Access controls include eligibility requirements, access management, and organizational governance. Participating organizations must be conducting legitimate scientific research with clear public benefit, maintain appropriate governance and compliance controls, and restrict access to approved users within secure environments.
During the research preview, use of the model will not consume existing credits or tokens, subject to abuse guardrails. OpenAI will share more details on pricing and availability as the program expands.
Industry partnerships and future outlook
OpenAI is working with customers including Amgen, Moderna, the Allen Institute, and Thermo Fisher Scientific to apply GPT-Rosalind across workflows that accelerate research and discovery. Sean Bruich, Senior Vice President of Artificial Intelligence and Data at Amgen, stated: “The life sciences field demands precision at every step. Our unique collaboration with OpenAI enables us to apply their most advanced capabilities and tools in new and innovative ways with the potential to accelerate how we deliver medicines to patients.”
The model is named after Rosalind Franklin, whose rigorous research helped reveal the structure of DNA and laid foundations for modern molecular biology. OpenAI views this as the beginning of a long-term commitment to building artificial intelligence that can accelerate scientific discovery. The company is exploring ongoing partnerships with national laboratories such as Los Alamos National Laboratory, where it is exploring AI-guided protein and catalyst design.
