Visual reasoning is a core component of human intelligence and a critical capabilityfor advanced multimodal models. Yet current reasoning evaluations of multimodallarge language models (MLLMs) often rely on text descriptions and allow languagebased reasoning shortcuts, failing to measure genuine vision-centric reasoning.To address this, we introduce VisuLogic: a benchmark of 1,000 human-verifiedproblems across six categories (e.g., quantitative shifts, spatial relations, attributecomparisons). These various types of questions can be evaluated to assess the visualreasoning capabilities of MLLMs from multiple perspectives. We evaluate leadingMLLMs on this benchmark and analyze their results to identify common failuremodes. Most models score below 30% accuracy—only slightly above the 25% random baseline and far below the 51.4% achieved by humans—revealing significantgaps in visual reasoning. Furthermore, we provide a supplementary training datasetand a reinforcement-learning baseline to support further progress. Code, data, andbaselines are available at https://visulogic-benchmark.github.io/VisuLogic.

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