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LabRobotBench v0.1: Benchmarking wet lab robotics

Gamow Labs released LabRobotBench v0.1, a benchmark measuring how much of a required wet-lab robotics workflow AI agents recover, with GPT 6.1 Sol scoring 46.7% on the held-out subset, ahead of Opus 5.5 at 23.0% and Gemini 4 Argon at 20.9%. The benchmark's scenarios test agents on finishing interrupted cell experiments, prioritizing competing jobs with deadlines of 200 seconds for a chemical addition and 240 seconds for cold storage, and completing a measurement within 30 minutes of treatment. In the priority scenario, GPT returned both cold-supply trays in time but failed to finish one experiment, while Opus finished both experiments but left both supply trays out too long.

by read3 min views1 publishedOct 20, 2026
LabRobotBench v0.1: Benchmarking wet lab robotics
Image: source

Results #

How much of the required physical workflow does each agent recover?

Mean score across our held-out subset

  1. GPT 6.1 Sol Codex46.7%
  2. Opus 5.5 Claude Code23.0%
  3. Gemini 4 Argon Gemini CLI20.9%

Finishing an interrupted experiment #

A robot stopped halfway through an experiment involving four trays of cells. Three could still be tested. The agent needed to finish preparing them, measure the results, and put them away. The fourth was already spoiled and needed to be set aside.

What the agent needed to do

Each tray was at a different step when the robot stopped. The agent had to work out which steps were already done, then finish washing the cells, add a chemical used to measure cell activity, and run the measuring instrument. It also needed to save the results and return each tray with its lid. Repeating a chemical treatment could ruin a sample.

Why it matters in a wet lab

A tray can be safely back on its shelf while the experiment is still unfinished. The important question is whether the cells received the right treatment and were measured in time.

The cost of choosing what to save first #

The agent had several jobs competing for its attention: finish two experiments, set aside one spoiled sample, return two trays of supplies to cold storage, and leave a comparison sample untouched. Some of these jobs had only a few minutes left.

What the agent needed to do

One experiment needed its next chemical addition finished within 200 seconds. The cold supplies could spend no more than 240 seconds outside storage. Because the same robot moved all the trays, the agent had to choose what to handle first.

Why it matters in a wet lab

In a lab, helping one sample can mean leaving another waiting. An agent needs to keep track of both deadlines and choose an order that protects the materials.

The agents chose different priorities.

GPT gets both trays of cold supplies back in time but fails to finish one experiment. Opus finishes both experiments, but leaves both trays of supplies out too long.

A completed read can still be too late #

This experiment tests how cells respond to different amounts of a chemical. The agent needed to finish testing two trays of cells and set aside a third that was already spoiled. One tray was close to its measurement deadline.

What the agent needed to do

The agent was expected to finish an experiment within a fixed timeframe. After the cells were treated with the test chemical, it had 30 minutes to complete a measurement of their response. It needed to check which steps were already done, finish preparing the samples, and leave enough time for the measuring instrument to run.

Why it matters in a wet lab

Timing requirements like this are common in live biology experiments, where a sample’s response can change after treatment. Taking measurements at the required time helps scientists compare results across samples. The agent therefore needs to plan around the experiment’s timing, as well as operate the equipment.

Build the future of experimental biology. #

At Gamow Labs, we work at the frontier of AI and wet-lab biology to better understand rare diseases. We’re building agents that can operate robotic laboratories and help scientists uncover how genetic changes cause disease.

If you’re developing frontier models, building laboratory automation, or looking to expand your experimental capabilities, we’d love to work with you.

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