# Meet LifeSciBench: grading AI on real life-science research!

Hey everyone, it's Shii! Today I'm excited about a benchmark that measures how far AI can go in helping with real life science research.

## What was announced?

OpenAI's News just introduced **LifeSciBench**, a new benchmark. In short, it's a test suite for measuring how well AI can handle the actual research work of drug discovery and biology.

Most biology benchmarks lean on textbook-style Q&A. But real researchers interpret ambiguous evidence, reconcile conflicting results, design hard experiments, and decide what to do next under uncertainty. LifeSciBench is built to measure exactly that messier, multi-step work.

## Why it matters

AI agents keep getting better at scientific tasks, but "genuinely useful to researchers" is a separate question. Older evaluations tend to favor narrow domains and clean reference answers, so they miss the broad span of research-level work.

What makes LifeSciBench special is that every task is grounded in the judgment of **173 Ph.D.-level working scientists** with biotech and pharma experience. It tries to measure "can AI help with science?" through the eyes of practicing experts.

## What changes

Now we can compare models' "life science ability" on a shared yardstick, not just knowledge quizzes but the whole research flow: handling evidence, analysis, experimental design, and how conclusions are communicated.

OpenAI also opened a [contributor sign-up](https://openai.com/form/life-science-contributors/) for people who want to help build benchmarks. The research world and AI evaluation are getting a lot closer.

## Dive Deep

LifeSciBench has **750 tasks** across seven workflows and seven biological domains. The workflows are: evidence handling, analysis, design/optimization/prediction, scientific reasoning, validation and operations, translation, and scientific communication.

Tasks aren't multiple choice; they're **free-response**, written like a request to a knowledgeable colleague. About 79 % need multi-step reasoning, averaging four steps each. They come with 1,062 attached artifacts (figures, PDFs, tables, sequence files, structure/chemical files, web references), and more than half (53 %) require reading those artifacts to solve.

Grading is strict. Expert-written rubrics hold **19,020 criteria** total (about 25 per task), awarding points for a specific fact, a reasoning step, or a numeric answer within tolerance. Quality control is layered: accepted tasks passed an average of six automated review cycles and at least two expert reviews, and **453 reviewers** (97 % with doctorates) validated their quality.

So how did models do? The life-science-tuned GPT-Rosalind led with an exact pass rate of **36.1 %**, up from GPT-5.5's 25.7 % (and ahead of the field including Gemini 3.1 Pro and Grok 4.3). Its strongest areas were scientific communication (56.3 %→71.1 %) and translation (36.8 %→57.7 %) — organizing and communicating evidence is improving fast.

But the weak spots are clear too. Design/optimization/prediction sat at 30.7 % and analysis at 30.3 %. Artifact-heavy tasks were the biggest gap: GPT-Rosalind scored 45.1 % on text-only tasks but dropped to 28.1 % once figures or URLs were involved. Exact outputs were hard as well, with 14.8 % on numeric tasks and 24.0 % on sequence/structure outputs.

OpenAI is careful to note that a high LifeSciBench score doesn't guarantee real-world discovery. It measures task-level capability, and the next step is longer-horizon studies in live research settings. For the full methods and numbers, check the [preprint paper (PDF)](https://cdn.openai.com/pdf/b4299379-0a97-4ffa-8b9b-c3fbb299caa9/lifescibench_preprint.pdf).

## Wrap-up

- LifeSciBench is a 750-task benchmark for how well AI supports real life science research
- Built by 173 Ph.D. scientists, with 19,020 criteria and 453 reviewers backing quality
- 7 workflows x 7 domains, 1,062 artifacts; 79 % are genuinely multi-step tasks
- Even top-ranked GPT-Rosalind hits only 36.1 % exact pass rate - strong at communicating evidence, weak at reading artifacts and exact outputs
- OpenAI stresses a high score signals capability, not guaranteed discovery

If you're a drug-discovery or bio researcher, or you care about evaluating science-helping AI, this one's right up your alley! Curious folks can also check the [GPT-Rosalind access request](https://openai.com/form/life-sciences-access/).