VIALS-AA: Different Paths to Scientific Image Reasoning

VIALS-AA analyzes how 19 models reason over scientific images, classifying 9,177 rollouts of tool calls. Viewing images accounted for 38.3% of calls and image preparation or measurement 47.3%, while image measurement was just 5–6% of calls for GPT-6 Astra (max) and GPT-6.1 Sol (max) versus 25–27% for Claude Opus 5.5 (max) and Claude Sonnet 5.5 (max). Gemini 3.8 Flash (high) produced 86.7% of the cohort's commentary/no-op calls, and cost per task varies by nearly 100× across models.