Large language models (LLMs) and the human brain show converging features in language processing. This study examines 12 high-performance LLMs with similar parameter sizes to investigate how their hierarchical feature extraction aligns with the brain's language processing mechanisms. As LLMs improve in performance on benchmark tasks, they become more brain-like, with their feature extraction pathways mapping more closely to the brain's structure using fewer layers. The study also compares the feature extraction pathways of LLMs and finds that high-performing models converge toward similar hierarchical processing mechanisms. Contextual information plays a crucial role in improving model performance and brain similarity. The findings reveal converging aspects of language processing in both LLMs and the brain, suggesting that LLMs are increasingly aligned with human cognitive processing. The study highlights the importance of hierarchical feature extraction and contextual processing in achieving brain-like language processing in LLMs. The results indicate that better-performing LLMs extract features using a hierarchy that more linearly aligns with the brain's hierarchical language processing pathway. The study also shows that contextual information significantly influences the alignment between LLMs and the brain, with models that use more contextual information showing higher brain similarity. The findings suggest that both the brain and LLMs extract context along their hierarchies, and that LLMs need contextual information to achieve brain similarity in downstream processing regions. The study provides new insights into the cognitive mechanisms underlying human language processing and offers new directions for developing LLMs that align more closely with human cognitive processing.Large language models (LLMs) and the human brain show converging features in language processing. This study examines 12 high-performance LLMs with similar parameter sizes to investigate how their hierarchical feature extraction aligns with the brain's language processing mechanisms. As LLMs improve in performance on benchmark tasks, they become more brain-like, with their feature extraction pathways mapping more closely to the brain's structure using fewer layers. The study also compares the feature extraction pathways of LLMs and finds that high-performing models converge toward similar hierarchical processing mechanisms. Contextual information plays a crucial role in improving model performance and brain similarity. The findings reveal converging aspects of language processing in both LLMs and the brain, suggesting that LLMs are increasingly aligned with human cognitive processing. The study highlights the importance of hierarchical feature extraction and contextual processing in achieving brain-like language processing in LLMs. The results indicate that better-performing LLMs extract features using a hierarchy that more linearly aligns with the brain's hierarchical language processing pathway. The study also shows that contextual information significantly influences the alignment between LLMs and the brain, with models that use more contextual information showing higher brain similarity. The findings suggest that both the brain and LLMs extract context along their hierarchies, and that LLMs need contextual information to achieve brain similarity in downstream processing regions. The study provides new insights into the cognitive mechanisms underlying human language processing and offers new directions for developing LLMs that align more closely with human cognitive processing.