Benchmarks/Updated 2026-10-30

ScienceVision

250 expert questions across five scientific disciplines, testing whether models can obtain evidence from native 3D objects and reason from it.

DomainAI4SCI
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ScienceVision / Scientific 3D understanding

Can AI observe and reason like a scientist?

ScienceVision evaluates whether models can obtain the visual evidence a scientific question needs directly from native 3D objects — molecular structures, crystal packings, CT volumes, CAD assemblies and building models — and then reason from it. The benchmark contains 250 expert-written questions across five disciplines and 22 subdomains, each paired with a structured reference answer and scored component by component.

0.5291Top mean score / GPT-6 Astra · 3D
250Expert questions / 5 disciplines · 22 subdomains
≈500 hExpert annotation / 66 experts · 482.98 recorded hours

Overall results

Values are the mean answer score on [0,1], with ineligible trials counted zero. 3D gives the agent the source artifact and code execution. 3D + MCP adds inspection tools modelled on expert workflow: view control, target isolation, slice browsing and measurement. 2D supplies six rendered views as a fixed-view control. Selected available repeats are averaged within each task, then tasks are weighted equally.

Model3D3D + MCP2D · 6 views
GPT-6 Astra (Codex)0.52910.52820.2498
GPT-6.1 Sol (Codex)0.48930.51100.2044
Claude Fable 5.10.46190.46310.2965
Claude Opus 5.50.45390.43430.2383
Gemini 4 Argon0.42780.43210.1462
Qwen3.8 Max 09020.38300.37360.2450
Qwen3.8 Flash0.36160.36810.2468
Kimi K30.36090.33690.2148
GLM-5.3 Flash0.28460.28580.1521
DeepSeek V4 Flash0.26520.26780.1661
Doubao Seed Evolving0.24350.25270.1102
Hunyuan-4 Preview0.15980.17380.0824

Harness. GPT-6 Astra and GPT-6.1 Sol were run in a Codex harness; the other ten models in Claude Code. A GPT row therefore measures a different scaffold, not only a different model. The two 3D settings average up to three runs per question; Gemini's carried-over segment has two runs. The 2D control uses three runs, with three Hunyuan-4 questions at two. Scores splice carried-over and re-run segments measured on different days, and model aliases carry no version.

The 2D column uses the same metric but a different evidence route. Compare its ordering with the 3D columns, not its absolute values.

What the results show. The twelve models span 0.53 to 0.16. Adding inspection tools helps 8 of 12 models, but does not change the overall order at the top. Every model scores far lower from six rendered views than from the native source, so the benchmark rewards obtaining evidence from the object itself. Mechanical CAD, medical imaging and architecture have the lowest floors; biology is the strongest discipline across models.