In February 2024, the AI world was buzzing with excitement as Google and OpenAI renewed their rivalry. OpenAI’s GPT-4 had been setting the standard in generative AI for a year, but then Google introduced Gemini Pro, aiming to shake things up.
Initially called Bard, Google’s first version didn’t make much of a splash, prompting them to revamp and relaunch as Gemini Pro. While it started strong and showed promising potential, Gemini Pro quickly encountered criticism over some notable errors, putting Google’s comeback in a tricky spot.
What does Gemini Pro bring to the table compared to GPT-4? Let’s unpack the competition and see what each contender offers.
Gemini Pro vs GPT 4: Understanding the Differences
Google’s Gemini Pro and GPT-4 are both large language models (LLMs). They are algorithms trained on massive amounts of data to produce high-quality text, translate languages, write code, etc.
Let’s understand the key differences between them and where their specialties lie:
Gemini Pro Model: Multimodal & Expansive
Gemini Pro is the most recent large language model (LLM) released by Google AI, known for its versatility and efficiency. It follows Gemini 1.0 and is far superior to its predecessor.
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Key Features of Gemini Pro:
- Context Length: It can handle an impressive context length of 1 million tokens, surpassing GPT-4 Turbo’s 128K and Claude 2.1’s 200K token context lengths.
- Multimodal Capability: Gemini Pro natively supports multimodal inputs, allowing it to process videos, images, and various file formats seamlessly.
- Advanced Reasoning: In logical reasoning tests, Gemini Pro has shown improvement over its predecessors, correctly answering questions that previously stumped it.
- Retrieval Capability: Google internally tested Gemini Pro with up to 10 million tokens, demonstrating its robust retrieval capability.
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GPT-4: The Established Contender in AI
Developed by OpenAI and released in 2023, GPT-4 is a prominent large language model (LLM) known for its diverse capabilities. While it shares some functionalities with Gemini Pro, it exhibits distinct strengths and areas of expertise.
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Key Features of GPT-4:
- Text Generation: Excels in generating different text formats. GPT-4 often displays a more detailed and user-prompt driven approach as compared to Gemini Pro.
- Multilingual Communication: Understands and translates text across numerous languages, similar to Gemini Pro.
- Code Generation: Demonstrates a slight edge in generating complex and intricate code formats. This has huge appeal to developers seeking assistance with challenging coding tasks.
- Question Answering: Provides comprehensive and informative answers to user queries, similar to Gemini Pro. However, GPT-4 is claimed to be more accurate than Gemini Pro in this field.
While both Gemini Pro and GPT-4 have remarkable features, it’s important to remember that no generative AI model is perfect. They’re still in early development, similar to the original iPhone versus the Samsung Omnia Windows phone.
Gemini Pro vs GPT 4: Use Cases and Applications
Moving on to specific use cases, let’s explore how each model excels in different domains. Both models are adept at text generation, translation, and question-answering. Some anecdotal evidence suggests Gemini Pro demonstrates a slight edge in reasoning.
Gemini Pro Use Cases
Gemini Pro has captured the interest of the AI community, that hopes its performance can be better than the existing models.
Gemini is constantly evolving. While it may not have made as large an impact as GPT-4, Gemini Pro is great at:
- Generating human-friendly content: Gemini Pro offers a wide range of applications, including generating reports, articles, and blog posts with its strong grasp of factual language. Its proficiency in understanding information allows it to produce accurate and informative content across different fields.
- Research and administrative tasks: Gemini Pro proves valuable in research assistance tasks such as analyzing large datasets, summarizing research papers, and extracting essential information. Moreover, businesses can leverage Gemini Pro for translation and localization purposes. It is also proficient at translating content for various audiences or creating localized marketing materials tailored for international markets.
- Business intelligence and analysis: The LLM offers a suite of functionalities to support informed decision-making. Gemini Pro excels in market research and analysis by processing large volumes of data, enabling businesses to identify emerging trends and patterns.
GPT-4 Use Cases
GPT-4’s success lies in its ability to perform a wide range of tasks with minimal human intervention
GPT-4’s primary advantage lies in its plugins and API. Developers are familiar with its capabilities and adept at customizing the large language model (LLM) to suit specific needs. It can browse the web and offer recent information, while Gemini Pro, till now, has had to depend on training data.
- Creative content generation: GPT-4 has diverse applications across marketing, storytelling, and product design. It allows users to generate engaging marketing copy, captivating ad content, and innovative marketing campaigns.
- Software coding: GPT-4 acts as an indispensable tool. It assists developers by generating various types of code and identifying potential issues in existing code. Moreover, developers can utilize GPT-4 to brainstorm new coding approaches and experiment with innovative frameworks. GPT-4 automates the generation of documentation and comments within code, enhancing readability and maintainability.
- CustomGPT: This powerful service allows you to build your own ChatGPT chatbot tailored specifically to your business needs. It empowers businesses to provide accurate interactions while leveraging their own content. CustomGPT provides accurate answers without hallucinations, ensuring brand integrity. You can embed CustomGPT on your website, integrate it into workflows via API, or sell it using your pricing models.
Remember, this is not an exhaustive list, and both models can be applied creatively across various domains.
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Gemini 1.5 Pro vs GPT-4: Benchmark Showdown
In evaluating the capabilities of AI models, particularly large language models (LLMs), benchmarks play a crucial role. Like grading systems used for humans, benchmarks serve as rigorous tests that push these models to their limits.
Thus, the question arises: How does Gemini stack up against GPT-4 in the realm of AI benchmarks?
This table provides comparisons between Gemini Ultra, Gemini Pro, GPT-4, and GPT-3.5 across a range of benchmarks.
| Benchmark | Gemini Ultra | Gemini Pro | GPT-4 | GPT-3.5 |
|---|
| MMLU | 90.04% | 79.13% | 87.29% | 70% |
| GSM8K | 94.4% | 86.5% | 92.0% | 57.1% |
| MATH | 53.2% | 32.6% | 52.9% | 34.1% |
| BIG-Bench-Hard | 8 3.6% | 75.0% | 83.1% | 66.6% |
| HumanEval | 74.4% | 67.7% | 67.0% | 48.1% |
| Natural2Code | 74.9% | 69.6% | 73.9% | 62.3% |
| DROP | 82.4 | 74.1 | 80.9 | 64.1 |
| Hellaswag | 87.8% | 84.7% | 95.3% | 85.5% |
| WMT23 | 74.4 | 71.7 | 73.8 | – |
Here’s a simplified interpretation of the table:
- MMLU: Measures how well the models understand and respond to different language tasks. GPT-4 scored the highest (among LLMs available today for deployment).
- GSM8K: Evaluates their ability to solve grade-school level math problems. Again, GPT-4 performed the best.
- MATH: Tests their math skills. GPT-4 and Gemini Ultra performed similarly here.
- BIG-Bench-Hard: Challenges them with tough language understanding and reasoning tasks. All models did quite well.
- HumanEval: Measures how closely their text resembles human responses. GPT-4 scored the highest.
- Natural2Code: Checks their ability to turn human instructions into code. GPT-4 performed the best.
- DROP: Assesses their ability to answer questions based on text passages. GPT-4 did the best.
- Hellaswag: Tests their common sense and ability to predict outcomes. GPT-4 scored highest.
- WMT23: This one likely tests translation accuracy between languages. All models performed similarly except GPT-4, which wasn’t tested.
Please note:
- These are general figures based on publicly available information and may not represent the true performance of each model.
- Different benchmarks measure different aspects of language model performance. It’s important to consider the specific task at hand when evaluating performance. One might be better at writing screenplays and the other at translating entire websites.
- Language models are constantly evolving, so these benchmarks may not be completely accurate.
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There are several other factors to be taken into consideration when discussing Gemini AI vs ChatGPT.
These are not purely related to AI (that is, how intelligent the model appears) but nevertheless affect end-user performance.
Context Length
Gemini Pro: Can handle a massive context length of 1 million tokens. This surpasses GPT-4 Turbo’s 128K and Claude 2.1’s 200K token context lengths. However, Google has stated that the public release model can handle only 128,000 tokens.
GPT-4: Has a context window of 128K tokens by default.
Read More – Everything You Need to Know About Building a GPT Models
Multimodal Capability
Gemini Pro: Natively supports multimodal inputs, including text and images.
GPT-4: Primarily focuses on text-based inputs.
Retrieval Capability
Gemini Pro: Tested internally with up to 10 million tokens, showcasing robust retrieval capability.
GPT-4: Does not have the same tested retrieval capacity. The past year has shown it forgets information quite quickly.
Is Gemini better than ChatGPT?
It entirely depends on your use case.
Gemini Pro exhibits exceptional multimodality. This allows it to process and comprehend various data types, such as text, images, audio, and video, simultaneously.
This feature is favorable for tasks requiring a detailed understanding of mixed data, such as analyzing and generating multimedia content.
In contrast, GPT-4 demonstrates remarkable proficiency in language-related tasks. It excels in tasks requiring in-depth textual analysis, intricate language comprehension, and creative text generation.
Its strength lies in its capacity to manage complex language structures and sustain context in extensive conversations. This makes it suitable for applications like conversational AI, content creation, and detailed text summarization.
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Gemini Pro vs GPT 4: Comparison Summary
Here’s a summary of the key differences and comparisons between Gemini Pro and GPT-4, including token size, parameters, and cost:
| Feature/Aspect | Gemini Pro | GPT-4 |
|---|
| Developer | Google DeepMind | OpenAI |
| Token Size | Gemini 1.5 Pro is noted for its 1M token context window. | The GPT-4 model offers a context window of 8,000 tokens by default, with an extended version supporting up to 32,000 tokens. |
| Parameters | Specific details on the number of parameters for Gemini Pro are not readily available. However, it’s part of Google’s large-scale AI models. | GPT-4 is rumored to have around 1.76 trillion parameters, making it one of the largest models in terms of parameter count. |
| Cost | Gemini Pro text output costs $0.000375 per 1,000 characters. Text prompt to image is priced at $0.020 per image. Since it is multimodal there is a huge price list for different tasks. | The cost for using GPT-4 varies based on usage and access method, with OpenAI offering different pricing tiers for API access. Text only prompts cost $0.03 per 1,000 tokens. |
| Multimodal Capabilities | Strong in processing multiple data types (text, images, audio, video) simultaneously. | Primarily focused on text, with advanced language understanding and generation capabilities. |
| Application Areas | Ideal for multimodal tasks and applications requiring holistic data analysis. | Best suited for conversational AI, content creation, and detailed text summarization. |
Gemini Pro vs GPT 4: Which One is Right for You?
GPT-4 continues to dominate as the preferred generative AI tool, despite some performance hiccups like slow responses and occasional inaccuracies.
On the other side, Google’s Gemini Pro, initially launched to outperform GPT-4, faced setbacks due to rushed deployment and inconsistent user experiences.
This early release aimed to capitalize on GPT-4’s popularity but fell short in practice. Although Gemini Pro hasn’t disappeared and is expected to undergo significant enhancements, for the moment, GPT-4 retains a substantial lead, favored for its reliability and overall user satisfaction.
When choosing between them, consider your specific needs for accuracy, response time, and overall user experience. While GPT-4 offers stability, Gemini Pro’s anticipated updates might make it a strong contender in the near future.
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FAQs
Is Gemini better than GPT-4?
It's difficult to definitively say whether Gemini is "better" than GPT-4. They excel in different areas. Gemini boasts superior reasoning and problem-solving capabilities, while GPT-4 shines in creative writing and language translation. Ultimately, the "better" model depends on your specific needs and application.
Is Gemini 1.5 Pro better than ChatGPT 4o?
While both models prioritize user experience, Gemini 1.5 Pro's integration offers added convenience for users within Google's suite of tools, whereas ChatGPT 4o emphasizes simplicity and versatility in its standalone functionality.
Is Claude better than ChatGPT and Gemini?
It's impossible to definitively say which AI chatbot is "better" – it depends on your needs. Claude excels in nuanced conversation and creative tasks. ChatGPT is widely accessible and known for its strong language generation capabilities. Gemini, still in development, promises advanced reasoning and multimodality.
Is GPT or Gemini better for coding?
Both GPT and Gemini are powerful language models capable of coding, but they have different strengths. GPT excels in generating code based on natural language instructions, while Gemini is more adept at understanding and manipulating existing code. Ultimately, the best choice depends on your specific coding needs. For simple tasks like generating code snippets or converting code between languages, GPT may be sufficient. However, for complex projects requiring deep code understanding and manipulation, Gemini might be the better choice.
Which is better, OpenAI or Gemini?
"Better" depends on your specific needs. OpenAI offers a wider range of tools and models, like ChatGPT and DALL-E, while Gemini focuses on advanced reasoning and multi-modal capabilities. Gemini is still in development, but shows promise for complex tasks like scientific research or creative writing. Choose OpenAI if you need a versatile tool for everyday tasks, and Gemini for cutting-edge research or creative projects.
Which is better, ChatGPT or Gemini or Microsoft Copilot ?
Choosing between ChatGPT, Gemini, and Microsoft Copilot depends on your specific needs. ChatGPT excels in creative writing and general conversation, while Gemini focuses on factual accuracy and code generation. Copilot, designed for developers, aids in coding tasks and code completion. Ultimately, the best choice relies on your priorities - creative expression, accuracy, or coding assistance.
