We're expanding access to GPT-Rosalind for academic research. The new Rosalind Academic Plan helps scientific labs bring AI into the work of understanding biology, exploring ideas, and deciding what to test next—with sponsored access for eligible labs at U.S. universities. Academic research starts with questions whose impact may take years to impact human health.
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GPT-Rosalind is built to support that work: helping researchers reason across scientific literature and biological data, with scientists bringing the expertise and judgment to evaluate the evidence. Eligible labs can receive up to 12 months of sponsored access to OpenAI tools, with GPT-Rosalind access subject to trusted-access approval. Research data shared through the Rosalind Academic Plan is not used to train our models by default.

Oct 15, 2026 · 9:15 PM UTC

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Learn about the Rosalind Academic Plan, check your lab's eligibility, and find out how to apply: openai.com/index/introducing…

Bringing GPT-Rosalind to academic research labs

Introducing the Rosalind Academic Plan, offering eligible academic labs sponsored access to GPT-Rosalind and tools to advance life sciences research.

openai.com
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GPT Rosalind pointed at real scientific work is where I want to see these models go. Helping researchers move faster on biology and chemistry beats another chatbot benchmark every time. Looking forward to the first lab results.
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Awesome! When are we expanding access to independent researchers?
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you got to stop doing this to people, this is fraud. This is like buying a Corvette and then someone put a fucking Honda Civic engine in it and told you it was a V8. It's not legal. You're committing consumer fraud.
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Why would anyone want to create something that would become smarter than humans ,we are all here because of smart humans,I guess these people creating these so called agents are not as smart as they think they are
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good that scientists still make the final call. learning anatomy right now, i use ai to quiz me but i still check everything against the textbook because it gets details wrong
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How will your latent sexism affect research when you discriminate between men and women?
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gmorning how does the approval process work?
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how does rosalind handle citation chains where paper A cites B which cites C? does it surface that transitive evidence or just direct citations?
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zero data retention for model training is the absolute minimum required before academic labs will feed proprietary research data into LLMs
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reasoning across the literature is the easy part to picture. who checks the reasoning when the lab is wrong?
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Just want to make fun chemistry education available to all So that's what I'm doing
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数据默认不入训练这步很关键，生物医学顾虑的不是模型用不用，而是敏感临床数据回流；科学家保留判断、模型只做证据串联，比让模型直接下结论稳。
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I want to use a low-cost small model to validate a non-autoregressive route based on a “half-MoE + half-self-reasoning” hybrid architecture: one half relies on MoE for sparse, efficient capacity scaling, while the other half relies on model self-reasoning for explicit inference a
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Consumer fraud. The product is easily 5x worse than 3 years ago. It's deliberate. It is consumer fraud. You cannot sell a producrt and then degrade it over time and pretend that's lawful. It's not. It's illegal.
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“Not used to train by default” but labs still have to know what happens if they opt in or if data ends up in logs. Is there a clear way for a lab to confirm after the fact that their data stayed out of training runs?
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Just fix Sol's voice, please.
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Smart move putting the tools inside academic infrastructure instead of another API gate. I collaborate with a university lab and the data-governance overhead kills more projects than the compute limits do.
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12 months of sponsored tokens is a compute subsidy, not a science grant. The labs that clear the eligibility bar don't need the discount, and the ones that need it won't clear the bar. Classic market-widening play wearing a research lab coat.
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biology labs just got a sponsored ai assistant
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把专家判断留在人这边，AI只负责跨文献和生物数据推理，这个分工是对的；12个月赞助访问能不能留住实验室，看数据合规和复现效率。
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“由科学家提供专业知识和判断力来评估证据”这句很关键——AI 在科研里最好的位置是副驾驶而不是自动驾驶。文献推理做得再好，最后拍板的还是人的直觉和品味，工具越强这个分工反而越重要。
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Đưa AI vào đánh giá bằng chứng khoa học thì phần khó nhất là ai chịu trách nhiệm khi kết luận sai. Nhà khoa học vẫn phải giữ quyền phản biện. Bạn nghĩ chuẩn nào để một phòng thí nghiệm đủ điều kiện dùng?
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