Word associations are one of the most established psycholinguistic methods, offering a long-standing window onto how meaning is organised in the human mental lexicon, yet it remains unclear whether large language models (LLMs) represent and structure associative word meaning in comparable ways, particularly outside English. We present WAX-SL, the first Slovenian dataset of cue–association–explanation triplets annotated with semantic relation labels, and use it to compare human associative behaviour against parallel data generated by three open-weights LLMs. All four sources are annotated with a 20-label relational taxonomy, augmented with a validity marker for output that fails the task constraints. Results show human associations are semantically more diverse and spread across all relation types, drawing more often on action, common phrases, and emotional evaluation, whereas LLM outputs concentrate on structurally transparent relations such as causal and definitional links. The differences are also qualitative: model outputs exhibit instruction violations, semantically incoherent constructions, and hallucinated content. Inter-annotator agreement is markedly lower for machine output (κ = 0.32) than for human data (κ = 0.46), reflecting the greater ambiguity of model responses. Language-specific adaptation does not straightforwardly yield more human-like behaviour. We argue that, on this task, LLMs do not echo human associative behaviour but diverge from it in systematic ways, both in the relations they express and in their markedly higher rate of invalid output.