From the source
Elorian AI and UniPat AI built ScienceVision, a 250-task benchmark that asks models to find, measure and interpret structures in 3D scientific artifacts.
- The tasks are split fifty each across biology, chemistry, medical imaging, mechanical CAD and architecture, spanning 22 subdomains, and each keeps the artifact in its native molecular, volumetric, CAD or building representation.
- The benchmark compares three kinds of access to the same artifact: a coding agent with the native file, the same plus an inspection interface exposed through the Model Context Protocol, and six pre-rendered views that the solver cannot change.
- Source access beat fixed views for every model tested, without exception, but adding the inspection tools helped eight of twelve models and hurt four, with margins from +2.17 points for GPT-6.1 Sol to −2.40 for Kimi K3.
- The leader changes with the setting: GPT-6 Astra tops the source-access settings at 0.53, while Claude Fable 5.1 tops the fixed-view condition at 0.30.
- Biology holds the highest single domain score at 0.65, and no model falls below 0.24 in biology or chemistry, while mechanical CAD, medical imaging and architecture have the lowest floors, with their weakest models under 0.10.
- Scores are averaged over the available repeats within each task, then across the 250 tasks, and all 36 reported configurations carry a score for every task.
