According to God of Prompt on Twitter, a recent comparison between the Grassmann model and Transformer model on Wikitext-2 language modeling and SNLI natural language inference tasks reveals distinct performance trends. The 13M parameter Grassmann model achieved a perplexity of 275.7 on Wikitext-2, while the similarly sized Transformer model scored 248.4, making the Grassmann model about 11% less effective in language modeling. However, in SNLI validation accuracy, the Grassmann head slightly surpassed the Transformer head with 85.50% versus 85.45%, indicating that Grassmann may outperform attention mechanisms in certain inference tasks. These results suggest opportunities for alternative architectures in specific AI applications, according to God of Prompt.