Named after the crystallographer who helped reveal the structure of DNA, GPT-Rosalind is OpenAI’s first domain-specific model series, fine-tuned for biochemistry, genomics, and protein engineering. Access is restricted to a trusted-access programme for vetted enterprise customers including Amgen, Moderna, and Thermo Fisher Scientific.
OpenAI has launched GPT-Rosalind, a frontier reasoning model built specifically for life sciences research, the company announced on Thursday.
The model is designed to support evidence synthesis, hypothesis generation, experimental planning, and multi-step scientific workflows across biochemistry, genomics, and protein engineering, representing OpenAI’s first purpose-built domain-specific model series.
It is available as a research preview in ChatGPT, Codex, and the OpenAI API, but access is restricted to a trusted-access programme for qualified enterprise customers in the United States.
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The model is named after Rosalind Franklin, the British chemist and X-ray crystallographer whose diffraction imaging of DNA was instrumental in revealing the double helix structure, and whose contribution was notably absent from the 1962 Nobel Prize awarded to Watson, Crick, and Wilkins.
The naming is a pointed act of recognition: Franklin’s work is now widely regarded as foundational to modern molecular biology, and she remains a touchstone in discussions about the erasure of women from scientific history.
OpenAI is framing GPT-Rosalind as a tool to compress the timeline from scientific idea to clinical evidence. The company estimates it currently takes roughly 10 to 15 years to move a drug from target discovery to regulatory approval in the United States.
GPT-Rosalind is positioned to help at the early stages: it can query specialised databases, parse scientific literature, interact with computational tools, and suggest new experimental pathways within a single interface.
Alongside the model itself, OpenAI is also introducing a Life Sciences research plugin for Codex that connects models to more than 50 scientific tools and data sources, giving researchers programmatic access to biological databases and computational pipelines.
Launch partners include Amgen, Moderna, Thermo Fisher Scientific, and the Allen Institute. OpenAI is also working with Los Alamos National Laboratory on AI-guided protein and catalyst design.
Benchmark performance, as reported by OpenAI, shows GPT-Rosalind achieving a 0.751 pass rate on BixBench, a bioinformatics benchmark developed by Edison Scientific that evaluates models on real-world computational biology tasks.
On LABBench2, a broader research task benchmark, the model outperformed GPT-5.4 on six of eleven tasks, with its most significant advantage on CloningQA, a task requiring the end-to-end design of reagents for molecular cloning protocols.
The most striking performance signal came from a third-party evaluation conducted with Dyno Therapeutics, a gene therapy company focused on designing AAV capsid proteins.
Using unpublished, previously unseen RNA sequences to guard against benchmark contamination, GPT-Rosalind was tested on sequence-to-function prediction and sequence generation tasks.
The best-of-ten model submissions ranked above the 95th percentile of human experts on the prediction task and around the 84th percentile on sequence generation, according to OpenAI and confirmed by multiple outlets covering the launch.
The launch carries significant dual-use caveats that OpenAI has addressed through its access model. Researchers have warned that AI models trained on biological data could be misused to help design dangerous pathogens.
OpenAI’s decision to restrict access exclusively to a vetted trusted-access programme, with organisations required to demonstrate they are working towards improving human health outcomes and maintaining strong security and governance controls, is a direct response to that risk. During the research preview phase, usage will not consume existing API credits.
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