# LifeSciBench — OpenAI's 750-task benchmark for life-science research

> OpenAI's new biology benchmark grades models with expert rubrics, and even GPT-Rosalind passes only one task in three.

- Org: OpenAI
- Date: 2026-06-17
- Category: benchmark, dataset
- Source: https://cdn.openai.com/pdf/b4299379-0a97-4ffa-8b9b-c3fbb299caa9/lifescibench_preprint.pdf
- Canonical page: https://ai-tldr.dev/releases/openai-lifescibench/

## In short

OpenAI's LifeSciBench grades AI models on 750 expert-authored life-science tasks using rubrics. The strongest model, GPT-Rosalind, passes only 36.1%, with attached data files cited as the main bottleneck.

## What it is

LifeSciBench is OpenAI's new evaluation suite for AI in life-science research. It contains 750 free-response tasks across seven biology workflows and seven domains, written by 173 PhD-level scientists from biotech and pharma. Each task is graded by a detailed rubric instead of a single right answer.

## How it works

LifeSciBench grades model output with 19,020 expert-written criteria — about 25 per task — and tracks two scores: a normalized rubric score and a task pass rate at the 70% threshold. About 53% of LifeSciBench tasks include attached artifacts (figures, PDFs, sequence files, chemical structures, tables) that models must read, and 79% need multi-step reasoning averaging four decision steps.

## Why it matters

LifeSciBench gives biotech teams a hard, realistic yardstick for picking models to run scientific workflows. OpenAI's leaderboard shows even the strongest model (GPT-Rosalind, 36.1% pass rate) is far from saturating the benchmark, and that file-handling — not text reasoning — is where current models break down.

## Who it's for

AI eval researchers, biotech and pharma teams deploying LLMs

## Try it

Read the LifeSciBench paper (PDF) on cdn.openai.com

## Quick facts

- Maker: OpenAI
- Tasks: 750 expert-authored
- Rubric criteria: 19,020 (~25 per task)
- Top score: GPT-Rosalind, 36.1% pass rate
- Authors: 173 PhD scientists
- Coverage: 7 workflows × 7 biology domains
- Format: Free-response with rubric scoring

## FAQ

### What is LifeSciBench?

LifeSciBench is OpenAI's 750-task evaluation that grades how well AI models handle real life-science research. Each task uses an expert-written rubric instead of multiple choice, and the benchmark spans seven biological domains and seven research workflows.

### How well do current models do on LifeSciBench?

LifeSciBench's strongest model is GPT-Rosalind at 36.1% task pass rate (rubric score 0.576). GPT-5.5 reaches 25.7%, Gemini 3.1 Pro 23.6%, GPT-5.4 20.7%, and Grok 4.3 13.0%. OpenAI says the benchmark is far from saturated.

### Why is LifeSciBench harder than older biology benchmarks?

LifeSciBench uses free-response tasks graded by 19,020 rubric criteria written by 173 PhD scientists, not multiple-choice answers. About 53% of tasks require models to interpret attached artifacts like figures, PDFs, sequence files, and chemical structures.

### What's the biggest weakness LifeSciBench exposed?

Artifact processing is the main bottleneck. GPT-Rosalind's pass rate drops 17 points from 45.1% on text-only tasks to 28.1% on tasks with attached data files, showing that current models struggle with scientific figures and structured biology files.

## Links

- [Paper (PDF)](https://cdn.openai.com/pdf/b4299379-0a97-4ffa-8b9b-c3fbb299caa9/lifescibench_preprint.pdf)
- [Analysis](https://www.marktechpost.com/2026/06/17/openai-releases-lifescibench-a-750-task-benchmark-grading-ai-models-on-real-life-science-research-with-expert-written-rubric/)
- [Coverage](https://www.techtimes.com/articles/318638/20260618/openai-life-science-benchmark-reveals-ai-passes-only-1-3-scientific-research-tasks.htm)