BenchBench-Protocol: Evaluating Real-World Wet-Lab Protocol Reasoning and Modification
Organizations: Benchling
Abstract
We introduce BenchBench-Protocol, a benchmark for large language models of 149 protocol-modification tasks recovered from modifications that scientists made to published protocols during real experimental work. Adapting a published protocol to a new experiment is a routine task for a wet-lab scientist, and a correct modification requires accounting for prior choices and downstream steps. Recent life-science benchmarks have moved toward open-ended, rubric-graded tasks, but tasks are typically elicited from experts rather than reconstructed from real-world modifications. BenchBench-Protocol tasks are derived from differences between a published protocol and a version a scientist modified, which provides the basis for the query and the weighted rubric elements for a correct response. The benchmark draws from 96 source protocols across nine domains of wet-lab biology and only includes tasks rated highly after review by domain experts. We evaluate nine closed and open models; Claude Opus 5 scores highest at 59.2% normalized rubric score, with other models between 34.1% and 47.1%, and the benchmark remains unsaturated when taking the best of ten attempts. As models are increasingly helpful in life-sciences research, evaluating them on routine wet-lab tasks becomes correspondingly important. We present BenchBench-Protocol as both a grounded assessment of wet-lab reasoning and evidence for the utility of real-world experiments to construct benchmark tasks.
Figures & tables
| Protocol domain | Tasks | Share | Protocols |
|---|---|---|---|
| Protein biochemistry and purification | 32 | 21.5% | 21 |
| Cell culture and cell-based assays | 27 | 18.1% | 16 |
| Molecular biology and cloning | 26 | 17.4% | 17 |
| Staining, imaging, and cytometry | 24 | 16.1% | 17 |
| Genomics, transcriptomics, and sequencing | 19 | 12.8% | 13 |
| Microbiology and microbial methods | 14 | 9.4% | 8 |
| Primary task intent | Tasks | Share |
|---|---|---|
| Plan audit | 38 | 25.5% |
| Measurement and result interpretation | 35 | 23.5% |
| Troubleshooting | 29 | 19.5% |
| Recordkeeping | 23 | 15.4% |
| Procedure specification and adaptation | 14 | 9.4% |
| Quantitative formulation | 6 | 4.0% |
Appendix figures & tables10 assets
Supplementary material from the paper’s appendix.
Appendix
| Model | Access | Reasoning | Search provider |
|---|---|---|---|
| Claude Opus 5 | Anthropic API | max | First party / native |
| GPT-5.6 Sol | OpenAI API | max | First party / native |
| GPT-5.6 Terra | OpenAI API | max | First party / native |
| GPT-5.6 Luna | OpenAI API | max | First party / native |
| Gemini 3.6 Flash | Vertex AI | HIGH | First party / native |
| Grok 4.5 | xAI Responses | high | First party / native |
| System | @1 | @2 | @5 | @10 | Gain | 95% CI @10 |
|---|---|---|---|---|---|---|
| Claude Opus 5 | .592 | .651 | .714 | .753 | +.161 | [.728,.777] |
| GPT-5.6 Sol | .471 | .509 | .551 | .575 | +.104 | [.552,.598] |
| Kimi K3 | .457 | .528 | .592 | .628 | +.171 | [.601,.654] |
| GPT-5.6 Terra | .453 | .497 | .546 | .577 | +.123 | [.550,.603] |
| GPT-5.6 Luna | .413 | .453 | .498 | .527 | +.115 | [.501,.553] |
| Inkling | .394 | .449 | .506 | .541 | +.147 | [.512,.569] |
| Model | Misformatted | Refusals | Total |
|---|---|---|---|
| Claude Opus 5 | 0 / 1,490 | 0 / 1,490 | 0.0% |
| GPT-5.6 Sol | 0 / 1,490 | 0 / 1,490 | 0.0% |
| Kimi K3 | 62 / 1,490 | 0 / 1,490 | 4.2% |
| GPT-5.6 Terra | 0 / 1,490 | 0 / 1,490 | 0.0% |
| GPT-5.6 Luna | 0 / 1,490 | 0 / 1,490 | 0.0% |
| Inkling | 28 / 1,490 | 0 / 1,490 | 1.9% |
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Large language model evaluation measures what models can do, how reliably they do it, and whether benchmark scores predict real-world performance.
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