title: Gemini vs GPT-4V: A Preliminary Comparison and Combination of
    Vision-Language Models Through Qualitative Cases
summary: The rapidly evolving sector of Multi-modal Large Language Models (MLLMs) is
  at the forefront of integrating linguistic and visual processing in artificial
  intelligence. This paper presents an in-depth comparative study of two
  pioneering models: Google's Gemini and OpenAI's GPT-4V(ision). Our study
  involves a multi-faceted evaluation of both models across key dimensions such
  as Vision-Language Capability, Interaction with Humans, Temporal Understanding,
  and assessments in both Intelligence and Emotional Quotients. The core of our
  analysis delves into the distinct visual comprehension abilities of each model.
  We conducted a series of structured experiments to evaluate their performance
  in various industrial application scenarios, offering a comprehensive
  perspective on their practical utility. We not only involve direct performance
  comparisons but also include adjustments in prompts and scenarios to ensure a
  balanced and fair analysis. Our findings illuminate the unique strengths and
  niches of both models. GPT-4V distinguishes itself with its precision and
  succinctness in responses, while Gemini excels in providing detailed, expansive
  answers accompanied by relevant imagery and links. These understandings not
  only shed light on the comparative merits of Gemini and GPT-4V but also
  underscore the evolving landscape of multimodal foundation models, paving the
  way for future advancements in this area. After the comparison, we attempted to
  achieve better results by combining the two models. Finally, We would like to
  express our profound gratitude to the teams behind GPT-4V and Gemini for their
  pioneering contributions to the field. Our acknowledgments are also extended to
  the comprehensive qualitative analysis presented in 'Dawn' by Yang et al. This
  work, with its extensive collection of image samples, prompts, and
  GPT-4V-related results, provided a foundational basis for our analysis.
paper_authors._id: 658b9643137a0f90a29c4a0d
paper_authors.name: Zhangyang Qi
paper_authors._id: 658b9643137a0f90a29c4a0e
paper_authors.name: Ye Fang
paper_authors._id: 658b9643137a0f90a29c4a0f
paper_authors.name: Mengchen Zhang
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paper_authors.fullname: Zhang Mengchen
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paper_authors.name: Zeyi Sun
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paper_authors.fullname: Zeyi Sun
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paper_authors.user: Zery
paper_authors._id: 658b9643137a0f90a29c4a11
paper_authors.name: Tong Wu
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paper_authors.fullname: Tong Wu
paper_authors.type: user
paper_authors.user: wutong16
paper_authors._id: 658b9643137a0f90a29c4a12
paper_authors.name: Ziwei Liu
paper_authors._id: 658b9643137a0f90a29c4a13
paper_authors.name: Dahua Lin
paper_authors._id: 658b9643137a0f90a29c4a14
paper_authors.name: Jiaqi Wang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-06-09T20:07:17.517Z
paper_authors._id: 64b4eec4faa3181a5eab9c46
paper_authors.avatarUrl: /avatars/bcc9bf5cbf67546ad2b4c9ec8b96ac96.svg
paper_authors.fullname: Jiaqi Wang
paper_authors.type: user
paper_authors.user: myownskyW7
paper_authors._id: 658b9643137a0f90a29c4a15
paper_authors.name: Hengshuang Zhao
paper_title: Gemini vs GPT-4V: A Preliminary Comparison and Combination of
    Vision-Language Models Through Qualitative Cases
paper_summary: The rapidly evolving sector of Multi-modal Large Language Models (MLLMs) is
  at the forefront of integrating linguistic and visual processing in artificial
  intelligence. This paper presents an in-depth comparative study of two
  pioneering models: Google's Gemini and OpenAI's GPT-4V(ision). Our study
  involves a multi-faceted evaluation of both models across key dimensions such
  as Vision-Language Capability, Interaction with Humans, Temporal Understanding,
  and assessments in both Intelligence and Emotional Quotients. The core of our
  analysis delves into the distinct visual comprehension abilities of each model.
  We conducted a series of structured experiments to evaluate their performance
  in various industrial application scenarios, offering a comprehensive
  perspective on their practical utility. We not only involve direct performance
  comparisons but also include adjustments in prompts and scenarios to ensure a
  balanced and fair analysis. Our findings illuminate the unique strengths and
  niches of both models. GPT-4V distinguishes itself with its precision and
  succinctness in responses, while Gemini excels in providing detailed, expansive
  answers accompanied by relevant imagery and links. These understandings not
  only shed light on the comparative merits of Gemini and GPT-4V but also
  underscore the evolving landscape of multimodal foundation models, paving the
  way for future advancements in this area. After the comparison, we attempted to
  achieve better results by combining the two models. Finally, We would like to
  express our profound gratitude to the teams behind GPT-4V and Gemini for their
  pioneering contributions to the field. Our acknowledgments are also extended to
  the comprehensive qualitative analysis presented in 'Dawn' by Yang et al. This
  work, with its extensive collection of image samples, prompts, and
  GPT-4V-related results, provided a foundational basis for our analysis.
paper_ai_keywords: Multi-modal Large Language Models (MLLMs) Vision-Language Capability Temporal Understanding Intelligence Quotients Emotional Quotients Visual Comprehension Structured Prompts Industrial Application Scenarios Direct Performance Comparisons Adjustments in Prompts Balanced and Fair Analysis Unique Strengths Niches Precision Succinctness Expansive Answers Multimodal Foundation Models Combining Models