We introduce ChemInsight, a benchmark designed to evaluate large language models (LLMs) on chemistry tasks that require multi-step reasoning and structured chemical understanding. We propose a two-dimensional scoring system (scope and depth) to assess both task coverage and reasoning completeness. ChemInsight consists of 203 challenging chemistry questions organized into 50 bundles, which support the two-dimensional scoring evaluation scheme. We evaluate ten representative LLMs using ChemInsight and observe consistently low performance across models, with scope scores below 30% and depth scores below 11%, substantially lower than human expert performance. We then proceed to ask (1) What factor limits the advancement of current LLMs in chemistry? (2) To what extent are statistically driven language-model training methods effective at modeling chemistry symbolic language such as SMILES? And (3) Can LLM performance in chemistry be improved through intensive training on large, domain-specific corpora?

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