DNA sequence design for transcription factor bindingBrowse 284 families
DNA sequence design for transcription factor binding
Can the model reason accurately about difficult scientific material?
- Domain
- Science
- Ranked models
- 2
- Reported values
- 2
- Reports
- 1 · lab
- Direction
- Higher is better
- Status
- Latest stable
Rankings
Best reported score per model across effort settings
| # | Model | Best setting | Score |
|---|---|---|---|
| 1 | GPT-5.6 SolOpenAI | reportedcost n/a1 value · Sep 3 · source | 13.70% |
| 2 | GPT-6 AstraOpenAI | reportedcost n/a1 value · Sep 3 · source | 11.27% |
Effort curve
Score vs. reported cost. Lines connect complete sweeps only.
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 ↗