This paper evaluates state-of-the-art models and approaches for Graded Change Detection (GCD) in Lexical Semantic Change (LSC) under equal conditions across eight different languages. The evaluation includes Word-in-Context (WiC) and Word Sense Induction (WSI) tasks, which are crucial for understanding the full spectrum of LSC. The study finds that: 1. **APD (Average Pairwise Distance)** outperforms other approaches for GCD. 2. **XL-LEXEME** outperforms other contextualized models for WiC, WSI, and GCD, and is comparable to GPT-4. 3. There is a need to improve the modeling of word meanings and focus on how, when, and why these meanings change, rather than solely on the extent of semantic change. The paper also highlights the limitations of current evaluations, such as the lack of a unified framework and the need for more sophisticated clustering algorithms. The authors argue that XL-LEXEME can serve as an affordable and scalable solution for LSC tasks, and that future research should focus on both unsupervised and supervised approaches to address the full spectrum of LSC.This paper evaluates state-of-the-art models and approaches for Graded Change Detection (GCD) in Lexical Semantic Change (LSC) under equal conditions across eight different languages. The evaluation includes Word-in-Context (WiC) and Word Sense Induction (WSI) tasks, which are crucial for understanding the full spectrum of LSC. The study finds that: 1. **APD (Average Pairwise Distance)** outperforms other approaches for GCD. 2. **XL-LEXEME** outperforms other contextualized models for WiC, WSI, and GCD, and is comparable to GPT-4. 3. There is a need to improve the modeling of word meanings and focus on how, when, and why these meanings change, rather than solely on the extent of semantic change. The paper also highlights the limitations of current evaluations, such as the lack of a unified framework and the need for more sophisticated clustering algorithms. The authors argue that XL-LEXEME can serve as an affordable and scalable solution for LSC tasks, and that future research should focus on both unsupervised and supervised approaches to address the full spectrum of LSC.