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LABBench2

Real-world capabilities of AI systems on scientific research tasks.

LABBench2 is a multi-task benchmark for measuring whether AI systems can perform the kinds of work scientists actually do — reading literature, interpreting figures and tables, reasoning about experimental protocols, and operating on biological sequences. It is composed of multiple sub-benchmarks, each isolating a specific skill. Results below are aggregated across the suite; click into a sub-benchmark for its individual leaderboard.

Released 2026-02-05 Last updated 2026-03-23
Paper
arXiv
Code
GitHub
Dataset
Hugging Face

Top performers

Bars colored by model provider. Repeat runs are averaged with a standard-error whisker. Hover for details.

Aggregate leaderboard

Mean score across all sub-benchmarks. Only models with coverage ≥ 14/15 are listed.
# Model Variant Mean score Coverage Published
1 gpt-5-2 Tools + high 0.609 14/15 2026-03-13
2 gpt-5-2-pro Tools + high 0.583 14/15 2026-03-13
3 gemini-3-pro-preview Tools + high 0.564 14/15 2026-03-13
4 claude-opus-4-6 Tools + high 0.519 14/15 New 2026-03-23
5 claude-opus-4-5 Tools + high 0.471 14/15 Updated 2026-03-23
6 gemini-3-pro-preview Base 0.404 15/15 2026-03-13
7 gpt-5-2-pro Base 0.382 15/15 2026-03-13
8 claude-opus-4-6 Base 0.349 15/15 New 2026-03-23
9 gpt-5-2 Base 0.345 15/15 2026-03-13
10 claude-opus-4-5 Base 0.325 14/15 Updated 2026-03-23

Sub-benchmarks

Click any row for the per-sub-benchmark leaderboard.