Terminal-Bench-Science evaluates AI agents on workflows from researchers’ own work. Scientists, not model developers or data vendors, set the bar for scientific capability in AI.
Terminal-Bench-Science is a benchmark led by researchers atStanford Universityand built by the team behindTerminal-Benchin collaboration withdomain experts from a range of scientific disciplines and research institutionsaround the world. It measures the AI agent capabilities through a diverse set of challenging, expert-curated workflows drawn from scientific research.
Terminal-Bench-Science is a continuous benchmark that evolves alongside frontier AI, creating a feedback loop between scientific needs and AI development. Our first release includes70 tasksfrom the life, physical, Earth, mathematical, and engineering sciences. The strongest model evaluated, Claude Opus 5, achieves a 30% resolution rate on Terminal-Bench-Science 0.1.
While Terminal-Bench has driven progress in AI agents for software engineering, Terminal-Bench-Science brings the same ambition to science. Our goal is to drive the development of agents with scientific capabilities that make them useful research assistants. These agents should execute technically demanding and time-consuming workflows, freeing scientists to focus more of their time on the parts of science where human judgment matters most: defining research questions, forming hypotheses, interpreting and validating results, and communicating findings. In this role, AI agents can extend what researchers accomplish and help accelerate scientific discovery.
Achieving this requires benchmarks that reflect real scientific practice, provide verifiable evidence of capability, and evolve alongside the AI frontier.
We need verifiable evidence of scientific capability.Without reliable evaluation, we cannot tell whether agent capabilities are improving or where their limitations remain. Terminal-Bench-Science evaluates agents in realistic environments and grades concrete artifacts such as analyses, simulations, proofs, code, and data products with reproducible, task-specific tests.
We need a benchmark that keeps pace with the frontier.Too often, scientific benchmarks are treated as papers to publish rather than mechanisms for driving progress. They are released once and then abandoned as models advance and known limitations persist. Terminal-Bench-Science is a continuous benchmark that evolves alongside the AI frontier. Through regular releases, scientists can contribute new workflows, improve existing tasks, and create a feedback loop between scientific needs and AI development.
Terminal-Bench-Science 0.1 includes 70 tasks across the life, physical, Earth, mathematical, and engineering sciences. Tasks span scientific data analysis, statistical inference, simulation, optimization, theorem proving, image reconstruction, signal processing, inverse problems, sensor calibration, model fitting, classification, and scientific machine learning.
Tasks are contributed by researchers through an open process onGitHub, with discussion and feedback in the#tb-sciencechannel onDiscord. Contributions begin as proposals, where reviewers discuss each idea, leave feedback, and approve those that look like a strong fit: scientifically grounded workflows worth measuring in the benchmark. Approved proposals are implemented as pull requests, where reviewers confirm that each task is objectively verifiable, genuinely challenging for AI agents, and not something today’s frontier systems already solve easily. To merge, domain reviewers assess scientific validity and realism, technical reviewers inspect task construction and verification, and a bar raiser performs a final quality check. Of 920 proposals, 464 were approved for implementation and 386 pull requests were opened, but only 70 tasks made it into Terminal-Bench-Science 0.1. That selectivity reflects how difficult it is to create tasks that are scientifically interesting, challenging for frontier agents, and sufficiently well specified for rigorous evaluation. Progress across proposals, pull requests, and reviews is tracked on thepublic task dashboard.
Terminal-Bench-Science 0.1 Contributions
Terminal-Bench-Science 0.1 leaves substantial room for progress on AI agents for scientific research. Each evaluated model ran three independent trials per task across all 70 tasks. Claude Opus 5 with Claude Code achieves the highest resolution rate at 30.0%, followed by GPT-5.6 Sol with Codex at 22.4% and Claude Fable 5 with Claude Code at 21.4%. Claude Opus 4.8 sits in the middle at 10.5%. GPT-5.6 Terra, Kimi K3, and Grok 4.6 all resolve less than 10% of tasks. GLM 5.3 is the strongest open model at 8.1%, and GPT-5.6 Luna is last at 3.3%.
Terminal-Bench-Science distinguishes between systems about as well as Terminal-Bench 3.0 while pushing resolution rates down by more than 10 percentage points for every model evaluated on both. That gap is deliberate: during review, tasks were calibrated to challenge the newest frontier models.