This paper introduces CoCoT, a novel prompting strategy for large multimodal models (LMMs) to enhance their ability to process and understand multiple image inputs. LMMs face two main challenges: (1) lack of fine-grained perception and (2) tendency to blend information across images. To address these issues, the authors propose a Contrastive Chain-of-Thought (CoCoT) prompting method that requires LMMs to compare similarities and differences among multiple images, then use this information to answer detailed questions about the images. The method improves the models' ability to discern nuanced details and perform accurate reasoning. The study evaluates CoCoT on two tasks: image-to-image matching and multi-image-to-text matching. For image-to-image matching, the task is to determine if two images match based on their content. For multi-image-to-text matching, the task is to match images with their corresponding text descriptions. The experiments show that CoCoT significantly improves performance on both tasks compared to existing methods like DDCoT and CCoT. CoCoT outperforms these methods in most scenarios, particularly in tasks requiring the identification of subtle differences between images. The results indicate that CoCoT enhances the models' ability to extract detailed information from images, especially when the images are very similar. However, some models, like GEMINI, still struggle with summarizing image information effectively, leading to poor performance on certain tasks. The study also highlights the importance of the visual encoder in LMMs, as it plays a crucial role in extracting detailed information from images. Despite these challenges, CoCoT demonstrates the potential of LMMs to better understand and reason about multi-image inputs. The paper concludes that CoCoT is an effective prompting strategy that enhances the performance of LMMs in multi-image tasks. However, further research is needed to refine CoCoT for more complex scenarios and integrate it with other AI technologies to advance multimodal understanding and AGI development.This paper introduces CoCoT, a novel prompting strategy for large multimodal models (LMMs) to enhance their ability to process and understand multiple image inputs. LMMs face two main challenges: (1) lack of fine-grained perception and (2) tendency to blend information across images. To address these issues, the authors propose a Contrastive Chain-of-Thought (CoCoT) prompting method that requires LMMs to compare similarities and differences among multiple images, then use this information to answer detailed questions about the images. The method improves the models' ability to discern nuanced details and perform accurate reasoning. The study evaluates CoCoT on two tasks: image-to-image matching and multi-image-to-text matching. For image-to-image matching, the task is to determine if two images match based on their content. For multi-image-to-text matching, the task is to match images with their corresponding text descriptions. The experiments show that CoCoT significantly improves performance on both tasks compared to existing methods like DDCoT and CCoT. CoCoT outperforms these methods in most scenarios, particularly in tasks requiring the identification of subtle differences between images. The results indicate that CoCoT enhances the models' ability to extract detailed information from images, especially when the images are very similar. However, some models, like GEMINI, still struggle with summarizing image information effectively, leading to poor performance on certain tasks. The study also highlights the importance of the visual encoder in LMMs, as it plays a crucial role in extracting detailed information from images. Despite these challenges, CoCoT demonstrates the potential of LMMs to better understand and reason about multi-image inputs. The paper concludes that CoCoT is an effective prompting strategy that enhances the performance of LMMs in multi-image tasks. However, further research is needed to refine CoCoT for more complex scenarios and integrate it with other AI technologies to advance multimodal understanding and AGI development.