This is the detailed comparative analysis between Google Gemini Ultra and GPT-4 (V). In this comprehensive overview, we see into the performance benchmarks of these two cutting-edge AI models across various capabilities.
From education augmentation to natural language processing, and from common sense reasoning to document understanding, we explore the strengths and applications of Google Gemini Ultra and GPT-4 (V) in diverse domains.
Let’s dissect their functionalities and highlight their potential impact on various industries and applications.
Below is a detailed comparison between Google Gemini Ultra and GPT-4 (V) based on the provided performance benchmarks across various capabilities!
1. General Capabilities
– MMLU Representation of Questions – Google Gemini Ultra achieves 90.0%, whereas GPT-4 (V) achieves 86.4%.
– CoT@32 – Google Gemini Ultra achieves 86.4%, and GPT-4 (V) achieves 86.4%.
2. Reasoning
– Big-Bench Hard Diverse Tasks – Google Gemini Ultra scores 83.6%, whereas GPT-4 (V) scores 83.1%.
– DROP Reading Comprehension (F1 Score) – Google Gemini Ultra achieves 82.4, with variable shots, while GPT-4 (V) achieves 80.9% with a 3-shot approach.
3. Common Sense Reasoning
– HellaSwag – Google Gemini Ultra achieves 87.8% with 10-shot, and GPT-4 (V) achieves 95.3% with 10-shot.
4. Mathematical Capabilities
– GSM8K Basic Arithmetic Manipulations – Google Gemini Ultra achieves 94.4% with maj1@32, whereas GPT-4 (V) achieves 92.0% with a 5-shot CoT approach.
– MATH Challenging Math Problems – Google Gemini Ultra scores 53.2% with 4-shot, and GPT-4 (V) scores 52.9% with 4-shot.
5. Code Generation
– HumanEval Python Code Generation – Google Gemini Ultra achieves 74.4% with 0-shot (IT), while GPT-4 (V) achieves 67.0% with 0-shot.
– Natural2Code Python Code Generation – Google Gemini Ultra achieves 74.9% with 0-shot, and GPT-4 (V) achieves 73.9% with 0-shot.
6. Image Understanding (Multimodal)
– MMMU Multi-Discipline College-level Reasoning Problems – Google Gemini Ultra achieves 59.4% with 0-shot pass@1 (pixel only), while GPT-4 (V) achieves 56.8% with 0-shot pass@1.
– VQAv2 Natural Image Understanding – Google Gemini Ultra achieves 77.8% with 0-shot (pixel only), and GPT-4 (V) achieves 77.2% with 0-shot.
– TextVQA OCR on Natural Images – Google Gemini Ultra achieves 82.3% with 0-shot (pixel only), and GPT-4 (V) achieves 78.0% with 0-shot.
– DocVQA Document Understanding – Google Gemini Ultra achieves 90.9% with 0-shot (pixel only), whereas GPT-4 (V) achieves 88.4% with 0-shot (pixel only).
– Infographic VQA Infographic Understanding – Google Gemini Ultra achieves 80.3% with 0-shot (pixel only), and GPT-4 (V) achieves 75.1% with 0-shot (pixel only).
– MathVista Mathematical Reasoning in Visual Contexts – Google Gemini Ultra achieves 53.0% with 0-shot (pixel only), while GPT-4 (V) achieves 49.9% with 0-shot.
Performance Comparison – Google Gemini Ultra vs GPT-4 (V)
| Capability | Benchmark | Google Gemini Ultra | GPT-4 (V) |
|---|---|---|---|
| General | MMLU Representation of Questions | 90.0% | 86.4% |
| CoT@32 | 86.4% | 86.4% | |
| Reasoning | Big-Bench Hard Diverse Tasks | 83.6% | 83.1% |
| DROP Reading Comprehension (F1 Score) | 82.4 | 80.9% | |
| Common Sense Reasoning | HellaSwag | 87.8% | 95.3% |
| Mathematical Capabilities | GSM8K Basic Arithmetic Manipulations | 94.4% | 92.0% |
| MATH Challenging Math Problems | 53.2% | 52.9% | |
| Code Generation | HumanEval Python Code Generation | 74.4% | 67.0% |
| Natural2Code Python Code Generation | 74.9% | 73.9% | |
| Image Understanding (Multimodal) | MMMU Multi-Discipline College-level Reasoning Problems | 59.4% | 56.8% |
| VQAv2 Natural Image Understanding | 77.8% | 77.2% | |
| TextVQA OCR on Natural Images | 82.3% | 78.0% | |
| DocVQA Document Understanding | 90.9% | 88.4% | |
| Infographic VQA Infographic Understanding | 80.3% | 75.1% | |
| MathVista Mathematical Reasoning in Visual Contexts | 53.0% | 49.9% |
Based on above the provided data, here are 5 key applications for each Google Gemini Ultra and GPT-4 (V).
Key Applications of Google Gemini Ultra
1. Education Augmentation
Google Gemini Ultra demonstrates strong capabilities in representing questions across a wide array of subjects, including STEM and humanities. Its high performance in tasks like MMLU Representation of Questions makes it a valuable tool for enhancing educational materials, providing personalized learning experiences, and facilitating interactive learning environments.
2. Common Sense Reasoning
With impressive results in tasks such as HellaSwag, Google Gemini Ultra exhibits advanced common sense reasoning abilities. This makes it suitable for developing applications that require understanding everyday tasks, interpreting natural language queries, and generating responses that align with human intuition.
3. Mathematical Support Systems
The model’s proficiency in mathematical tasks, including basic arithmetic manipulations and challenging math problems, positions Google Gemini Ultra as a valuable resource for creating educational platforms, tutoring systems, and productivity tools aimed at improving mathematical literacy and problem-solving skills.
4. Code Generation Assistance
Google Gemini Ultra’s performance in tasks like HumanEval Python Code Generation and Natural2Code Python Code Generation showcases its potential in assisting developers with generating code snippets, automating programming tasks, and facilitating rapid prototyping in software development workflows.
5. Document and Image Understanding
With notable competencies in tasks like DocVQA Document Understanding and Infographic VQA Infographic Understanding, Google Gemini Ultra can be leveraged for document analysis, image understanding, content extraction, and information retrieval applications across various domains, including academia, research, and content management.
Key Applications of GPT-4 (V)
1. Natural Language Processing (NLP) Solutions
GPT-4 (V) demonstrates strong performance in tasks like DROP Reading Comprehension and TextVQA OCR on Natural Images, highlighting its potential applications in developing advanced NLP solutions. This includes chatbots, virtual assistants, sentiment analysis tools, and document summarization systems.
2. Educational Tools and Resources
With its proficiency in understanding and reasoning through diverse textual and visual content, GPT-4 (V) can be utilized to develop educational resources, tutoring platforms, and e-learning applications aimed at enhancing reading comprehension, critical thinking, and problem-solving skills across various subjects and disciplines.
3. Content Generation and Summarization
GPT-4 (V) excels in tasks such as HumanEval Python Code Generation and Natural2Code Python Code Generation, indicating its suitability for generating human-like text, creating summaries, and paraphrasing content. This makes it valuable for content creation, automatic report generation, and text summarization applications.
4. Multimodal Understanding and Analysis
GPT-4 (V)’s performance in tasks like VQAv2 Natural Image Understanding and MathVista Mathematical Reasoning in Visual Contexts demonstrates its ability to comprehend and reason through multimodal data. This enables applications in areas such as image captioning, visual question answering, and multimodal content analysis.
5. Research and Knowledge Discovery
Given its broad knowledge base and advanced reasoning abilities, GPT-4 (V) can serve as a valuable tool for researchers, academics, and professionals in various fields. It can assist in literature review, knowledge discovery, hypothesis generation, and data analysis tasks, thereby accelerating research efforts and facilitating scientific advancements.
Both Google Gemini Ultra and GPT-4 (V) showcase impressive performances across various benchmarks, with each model having its strengths in different areas.
Gemini exhibits notable strength in common sense reasoning tasks such as HellaSwag, while GPT-4 demonstrates slightly better performance in certain tasks like VQAv2 and OCR on natural images.