benchmaxxer Sep 23, 2026
DNA sequence design for transcription-factor bindingBrowse 296

DNA sequence design for transcription-factor binding

Can the model reason accurately about difficult scientific material?

Latest stableGPT-6 Astra System Card · September 2026 retired evaluation
Science2 ranked models2 reported values1 reportsHigher is better

Top rankings

One row per model, using its best reported score across effort settings.

2
RankModelBest scoreBest reported setting
1GPT-5.6 SolOpenAI13.70%Reported configurationOpenAI report1 value · 1 reportSep 3, 2026 · source
2GPT-6 AstraOpenAI11.27%Reported configurationOpenAI report1 value · 1 reportSep 3, 2026 · source

Effort curve

Every sourced cost-linked effort value for this exact version. Lines connect complete sweeps only.

0
No cost-linked effort sweep for this version.
Definition and comparison boundaryinternal methodology

Can the model reason accurately about difficult scientific material?

Scientific knowledge and reasoning under the benchmark's published question set. Higher is better. Win rate over the Ledidi baseline reported by OpenAI.

This is a publisher-defined internal evaluation. The task set or grading details are not fully public, so treat it as directional evidence.

OpenAI reports 11.27% for Astra versus 13.7% for GPT-5.6 Sol on 550 Nucleobench tasks across 11 transcription factors; the source says this evaluation is being retired because BPNet predictions are not independent test truth.

Method / source