text: Q1: Is there a model that outstands on all 
  tasks? 
  
  Models performances for each task are presented 
  in appendix Tables 9, 10, 11, 12 and 13. Figure 
  1 shows the critical difference diagram of average 
  score ranks. 
  
  As in MTEB (Muennighoff et al., 2022), no 
  model claims state-of-the-art in all tasks even if 
  the text-embedding-3-large model is in first place 
  It ranks 
  on average on all tasks (see Table 9). 
  first for the classification and reranking tasks. For 
  the clustering task, text-embedding-ada-002 is the 
  best model. The models voyage-code-2, 
  text- 
  embedding-3-small and mistral-embed share the 
  top positions in the retrieval task ranking. For the 
  pair classification task, laser2 is ahead of its com- 
  petitors. Finally, sentence-camembert-large leads 
  on the STS task and multilingual-e5-small has the 
  best results for summarization. 
  As expected, the score most strongly correlates 
  with whether the evaluated models were trained on 
  a sentence similarity task. Of course, this criterion 
  is connected to the more general Finetuned one. 
  The only top-performing models solely pre-trained 
  are from the E5 family, where the pre-training is, 
  in fact, contrastive and optimized for similarity. 
  Conversely, models pre-trained on token-level tasks 
  and generating embeddings via pooling appear less 
  well-suited for the benchmark tasks. 