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scBench: A Benchmark for Single-Cell RNA-seq Analysis
Evaluates whether models can solve practical single-cell RNA-seq analysis tasks with deterministic grading. Tasks require empirical interaction with .h5ad data files - agents must load and analyze the data to produce correct answers. Covers 30 canonical tasks across 5 sequencing
single-celltranscriptomicstabularcode
Status
curated
Saturation
unsaturated
Reproduction
not yet
Published results
10 models
Editor's note
Practical single-cell RNA-seq analysis tasks — directly in the dbverse / spatial-omics wheelhouse and a high-value target for a bio-specialist leaderboard. The live verified leaderboard now has GPT-5.6 Sol at 62.1% with Pi and Opus 5 at 60.1% with Claude Code.
Published results
| Model | Score | Variant | Provenance |
|---|---|---|---|
| GPT-5.6 Sol | 62.1% accuracy | Pi harness | external |
| Opus 5 | 60.1% accuracy | Claude Code harness | external |
| Claude Opus 4.6 | 52.8% accuracy | default | paper |
| Claude Opus 4.5 | 49.9% accuracy | default | paper |
| GPT-5.2 | 45.2% accuracy | default | paper |
| Claude Sonnet 4.5 | 44.2% accuracy | default | paper |
| GPT-5.1 | 37.9% accuracy | default | paper |
| Grok-4.1 | 35.6% accuracy | default | paper |
| Grok-4 | 33.9% accuracy | default | paper |
| Gemini 2.5 Pro | 29.2% accuracy | default | paper |