We’re launching VIALS-AA, a leaderboard for Handshake AI’s VIALS benchmark for visual reasoning in life sciences and biotech research

Visual artifacts are central to how scientists analyze experiments and make research decisions, but interpreting them correctly remains a challenge for frontier models. VIALS’ 161 tasks span eight domains and need models to reason over artifacts including gel blots, flow cytometry plots, plasmid maps, and phylogenies. Each task requires models to interpret an artifact and draw a specific scientific conclusion.

We evaluate models by running each task three times in Stirrup, our open source agent harness, with code execution and image inspection tools. GPT-6 Luna (medium) grades each answer against the reference answer, and we report the share of tasks judged correct in all three repeats (pass^3).

Key takeaways:

➤ The top three models are separated by 3 points: Gemini 4 Carbon (high) from @GoogleDeepMind leads narrowly with 64%, followed by GPT-6 Astra (max) from @OpenAI at 63% and Claude Opus 5.5 (max with fallback) from @AnthropicAI at 61%

➤ Astra and Sol achieve similar scores to Opus and Sonnet at a much lower Cost per Task: Claude Opus 5.5 (max with fallback) averages $1.64 per task, ~29× as much as Sol (~$0.056). This price difference is significantly driven by turn use - GPT-6 Astra (max) and GPT-6.1 Sol (max) average ~8 turns per task, compared with ~27 for Claude Opus 5.5 (max with fallback) and ~25 for Claude Sonnet 5.5 (max with fallback)

➤ Open weights models trail the highest score by ~19 points: Kimi K3.1 Pro leads open weights models on VIALS-AA at 45%, narrowly ahead of MiMo-V2.6-Pro at 43%

➤ 14 of 161 tasks saw safety refusals on at least one model: Claude Sonnet 5.5 (max with fallback) refused on 6 tasks, the most of any model, and half of the 14 affected tasks saw refusals from multiple models

Thank you to @joinHandshakeAI and the VIALS authors for creating the benchmark and collaborating with us on bringing it to Artificial Analysis!