Evaluation of multilingual large language models has expanded rapidly, yet aggregate benchmark scores often conceal how performance shifts once the surface form of a language changes. We examine this question through Marathi discourse relation classification. Five instruction-following models, Llama-3.1-8B, Llama-3.3-70B, Qwen3.6-27B, GPT-OSS-20B, and GPT-OSS-120B, are evaluated on a manually curated Marathi language dataset spanning six discourse relations, two scripts, eighteen domains, and three difficulty levels, producing 2500 model-level predictions. Qwen3.6-27B obtains the highest overall accuracy at 82.4%, followed by GPT-OSS120B at 80.8%. The central finding is a large script effect as mean accuracy falls from 78.1% on Devanagari script to 49.7% on Romanized Marathi, a decline of 28.5 percentage for content that is otherwise unchanged. Relationlevel results trace to marker form rather than presence; every instance carries an explicit marker, but Temporal and Contrastive relations use a single canonical connective and are handled reliably, while Inferential and Reformulative relations lean on multi-word phrasal markers and are frequently confused with Causal, a 20.4-point accuracy gap that holds across all five models. Llama-3.1-8B shows a more severe failure, defaulting to Contrastive across most gold relations. These results indicate that strong multilingual performance on Marathi is neither script-invariant nor uniform across discourse relations.

Evaluation of multilingual large language models has grown rapidly in recent years, yet Marathi, spoken by over 83 million people across India, has received almost no systematic probing beyond surface-level benchmark tests. Most existing multilingual evaluations either omit Marathi entirely or rely on machine-translated test sets that fail to capture the morphological complexity that defines the language. We evaluate four models, namely Llama-3.1-8B, Llama-3.3-70B, Mistral-7B, and Qwen3-32B, on our manually curated Marathi dataset across three probing dimensions: Devanagari versus Romanized script, Marathi-English code-mixing, and syntactic structures including SOV word order, vibhakti case markers, verb gender agreement, and postpositions. Models are tested under English and Marathi instruction conditions across translation, similarity, grammaticality, and case marker tasks. Translation quality is evaluated using both token-level F1 and BERTScore to capture paraphrase equivalence beyond surface word overlap. All models drop between 7.9% and 20.5% on Romanized input. The negative subjunctive marker nasta is ignored by every model. Vibhakti case markers are consistently replaced with Hindi equivalents, revealing that multilingual training has not produced separate internal representations for Hindi and Marathi despite their distinct morphological systems. These findings reveal structural gaps in how current multilingual LLMs handle morphologically rich, low-resource Indic languages and point to specific areas where dedicated Marathi pretraining data would most benefit future work.