Aims/Objectives: According to Multilink, words from the first (L1) and (L2) second languages share a common store and their access is non-selective. Thus, the presentation of a target word activates in parallel lexical candidates from both languages that share form and semantic overlap. The degree of words’ activation also depends on their resting levels of activation (words that are more used have a higher resting levels of activation). Since non-cognate translations and synonyms share meaning, they may be seen as qualitatively similar lexical representations, and consequently subject to similar processing if their frequency levels are matched. However, whereas masked priming lexical decision studies with synonyms failed to find reliable masked priming effects, the majority of those with non-cognate translations (especially in the forward direction, i.e., from L1 to L2) showed significant effects. The present study extends those findings by directly comparing the processing of synonyms and translations in bilinguals. Design/methodology: A masked priming lexical decision task (targets were preceded by a related 50-ms word [an L1 translation or an L2 synonym] or by a 50-ms unrelated word) was conducted. Lexical frequency of usage was higher for primes than for targets. Data and analysis: Reaction times and accuracy from 24 sequential (highly proficient) European Portuguese-English bilinguals were analyzed with linear mixed effects models. Findings/conclusions: Results showed priming effects for translations, but not for synonyms, indicating a differential processing of synonyms and non-cognate translations. Originality: This is the first empirical work that directly compares the processing of synonyms and translations in bilinguals by using the same targets words for both prime types. Significance/implications: The findings contradict the Multilink model, since they index a differential representational nature of lexico-semantic links for translations and synonyms. Modifications in the model are needed to account for the data.