MELD: Measuring Multilingual Capability of Language Models through Latent Distribution Geometry

Multilingual large language models exhibit substantial variation in capability across languages, yet evaluating this variation typically requires costly downstream benchmarks. We introduce MELD, an intrinsic metric for estimating multilingual capability directly from the geometry of hidden states. MELD is motivated by growing evidence that multilingual LLMs develop a shared semantic and reasoning space in their intermediate layers, while language-specific structure is more prominent near the input and output boundaries. MELD measures distributional language neutrality by contrasting the separation between reference- and target-language representation distributions with their within-language dispersion. We evaluate MELD across 14 open-weight LLMs spanning multiple model families, parameter scales, depths, and training regimes, and validate it against five multilingual benchmark families covering more than 30 languages. We assess MELD from two complementary perspectives: predicting cross-language performance differences within a model and ranking models by their overall multilingual capability. In the within-model setting, MELD achieves average Pearson and Spearman correlations of about 0.94 and 0.93, respectively, with downstream multilingual performance. We further show that this predictive relationship remains strong when sentence-level correspondence is removed using non-parallel reference data, whereas prior methods degrade substantially. MELD also strongly correlates with multilingual capability across all models, achieving average Pearson and Spearman correlations of about 0.78 and 0.75, respectively, with overall downstream performance. Together, these results demonstrate that MELD provides a robust intrinsic signal of multilingual capability across both languages and models.