We’re in an exciting time for language technology, with big names like Google and OpenAI all competing to develop the best AI language models. These companies are at the forefront, each bringing its unique strengths to the table.
OpenAI’s ChatGPT has already created a buzz and is being used by millions of users. In the meantime, Google, better known for its search engine and AI expertise, released Google Bard after ChatGPT, but it was not well received. But now, Google is working on a language model, Gemini, which is not only powerful but also overtakes the ChatGPT 4.0 version.
This large language model (LLM), also aims to overcome the flaws that their previous versions have. Meanwhile, OpenAI has made a name for itself with the GPT series, especially ChatGPT. This model has changed the game in conversational AI, known for its advanced features and flexibility.
This intense competition among these tech giants means they’re pouring in resources, talent, and new ideas. We’re seeing rapid advancements in language models, moving us closer to more advanced, human-like AI capabilities.
In the latest developments of AI, Google and OpenAI are leading the pack with their advanced models: Google’s Gemini and OpenAI’s ChatGPT-4. These models are some of the most powerful AI tools out there.
This blog will dive into their key features and discuss how Google and OpenAI price these tools. We will also compare these models head-to-head, including their language models, performance, etc. So, let’s get started!
What is Google Gemini?
Google has introduced a new AI called Gemini, and it’s making waves in the tech world. Gemini is an advanced AI created by Google.
What’s special about it is that it can understand not just text, but also images, videos, and even audio. It’s super versatile and can handle complex tasks in areas like math, physics, and even different programming languages.
You can now find Gemini working with Google Bard and in the Google Pixel 8 phone. In the future, Google is planning to bring Gemini into more of its services.
Here’s a quick rundown of what Gemini can do:
- It’s a “multimodal” AI, which means it can handle text, images, audio, and video all at once. This makes it useful for a whole bunch of different things.
- Gemini comes in three versions: Ultra, Pro, and Nano. Each one is designed for different needs and levels of performance.
- Google’s bigwigs say Gemini is even better than OpenAI’s GPT-3.5. That’s a big deal because it shows just how capable Gemini is.
- Google is also planning to let businesses use Gemini through Google Cloud. This means companies can add Gemini’s smarts to their apps.
What is OpenAI ChatGPT?
OpenAI’s ChatGPT-4 stands out as a competent language model, renowned for its sophisticated handling of language tasks. Here’s a quick rundown of what makes ChatGPT-4 special:
- Language Mastery: ChatGPT-4 is a pro at both creating and understanding text. This makes it incredibly versatile, and able to tackle a broad spectrum of language-based tasks.
- Real-World Uses: You’ll find ChatGPT-4 being used in all sorts of everyday applications. From powering virtual assistants to supporting educational tools, aiding in finding information, and even streamlining tasks, it’s got a wide array of practical uses.
- Extra Strength: What sets ChatGPT-4 apart is its strength. It’s more advanced than many other models out there, having been thoroughly tested and compared to its peers in the field.
Comparison of Google Gemini vs ChatGPT – Differences To Be Aware Of
The main difference between Open AI ChatGPT and Google’s Gemini is that ChatGPT focuses on text generation and conversation, excelling in creative writing, translation, and engaging in open-ended, informative dialogue, whereas Gemini emphasizes multimodality, meaning it can seamlessly handle and generate text, images, audio, and video.
Google’s new AI, Gemini, seems to be stepping up the game against ChatGPT. It has outperformed ChatGPT in almost all academic tests, like understanding text, images, videos, and even speech.
Specifically, when tested on a wide range of topics like maths, physics, and law, Gemini scored 90%, which is higher than ChatGPT’s 86.4%, which is quite impressive. This includes doing better in text and reasoning, image understanding, video understanding, and speech benchmarks.
But comparing them isn’t straightforward because they were tested differently. Gemini used a method called ‘Chain of Thoughts,’ while ChatGPT used the ‘5-shots’ technique. This difference in testing methods could have affected their scores.
The less powerful Pro model of Gemini AI still performed better than GPT-3.5 in most tests. Let’s dive into the details to compare them.
Capability
Gemini AI is emerging as a strong rival to ChatGPT, potentially shaking up the world of large language models.
| Benchmark (Higher is Better) |
Description | Gemini Ultra | ChatGPT-4 | |
|---|---|---|---|---|
| General | MMLU | Representation of questions in 57 subjects (incl. STEM, humanities, and others) | 90.0% CoT@32* | 86.4% 5-shot* (reported) |
| Reasoning | Big-Bench Hard | Diverse set of challenging tasks requiring multi-step reasoning | 83.6% 3-shot | 83.1% 3-shot (API) |
| Reasoning | DROP | Reading comprehension (F1 Score) | 82.4 Variable shots | 80.9 3-shot (reported) |
| Reasoning | HellaSwag | Commonsense reasoning for everyday tasks | 87.8% 10-shot* | 95.3% 10-shot* (reported) |
| Math | GSM8K | Basic arithmetic manipulations (incl. Grade School math problems) | 94.4% maj1@32 | 92.0% 5-shot CoT (reported) |
| Math | MATH | Challenging math problems (incl. algebra, geometry, pre-calculus, and others) | 53.2% 4-shot | 52.9% 4-shot (API) |
| Code | HumanEval | Python code generation | 74.4% 0-shot (IT)* | 67.0% 0-shot* (reported) |
| Code | Natural2Code | Python code generation. New held out dataset HumanEval-like, not leaked on the web | 74.9% 0-shot | 73.9% 0-shot (API) |
Source: DeepMind – Gemini AI
Multimodality
- ChatGPT: GPT can understand and work with visual information, interpreting images and responding based on them.
- Gemini: This model handles various data types such as text, code, audio, images, and videos. Available in different sizes from Ultra to Nano.
Availability
- ChatGPT: Widely available on several platforms and through APIs, including free and paid options.
- Gemini: Still in development, not available for public use. Expected to have free and paid options.
Data Sources and Language Models
- Gemini: Bard using Bard LLM and Gemini Pro LLM, introducing the Gemini family into all Google products.
- ChatGPT: Knowledge based on internet data until September 2021.
How Does ChatGPT Differ From Gemini AI?
Just like ChatGPT and other AI models, Gemini AI learns from various sources, including the internet. What sets Gemini apart is how it’s updated and what it can do.
While ChatGPT, using the GPT-3.5 model, was trained with information available only until September 2022, Gemini AI is different. It uses the latest data from the web, which allows it to provide up-to-date answers. It’s like comparing a library with books from last year to one that gets new books every day.
But, there is a difference that ChatGPT’s capabilities can be enhanced through the integration of various ChatGPT plugins. However, as of now, there are no indications of similar advancements for Google Bard.
Gemini AI’s training involves a huge amount of text and code, much more than what ChatGPT has seen. This makes Gemini not just more current but also smarter in some ways. It can handle complex tasks like translating languages and summarizing information better than ChatGPT.
Comparison of OpenAI ChatGPT and Gemini AI
| Feature | OpenAI ChatGPT | Gemini AI |
|---|---|---|
| Architecture | Unimodal, focusing solely on text. Designed for various text applications, offering versatility in handling Natural Language Processing (NLP). | Multimodal, integrating both text and images, enabling more dynamic interactions and a broader range of applications in NLP. |
| Performance | Provides fast and accurate text generation, delivering coherent and contextually relevant responses. | Anticipated to be faster and more precise than ChatGPT, potentially enhancing user experience significantly. |
| Techniques | Utilizes deep learning for text processing, effective in various language tasks. | Employs AlphaGo-inspired techniques for problem-solving, allowing for advanced reasoning and planning in complex tasks. |
| Creativity | Limited creative responses to the scope of its training data. | Aims to transcend training data limitations, seeking innovative and imaginative responses. |
| Development | Developed by OpenAI, has undergone multiple iterations for capability enhancement. | A DeepMind project, Gemini is in training, expected to introduce groundbreaking AI advancements upon release. |
| Data Handling | Trained on a vast dataset up to a certain cut-off date, limiting current event knowledge. | Trained on real-time data, allowing for up-to-date responses and insights. |
| Interactivity | Primarily text-based interactions. | Potentially offers more interactive capabilities, integrating visual and textual responses. |
| Customization | Offers some level of customization in responses based on user inputs. | May provide advanced customization options due to its broader data integration and learning capabilities. |
| Learning Capability | Incremental learning through version updates. | Continuous learning from real-time data, potentially leading to rapid knowledge updates. |
| Application Scope | Primarily used for text-based applications, customer service, content creation, and educational purposes. | Expected to have a wider application scope including image processing, complex problem solving, and dynamic content generation. |
| Task Proficiency | General conversation, content creation, simple tasks. | Advanced tasks like translation, summarization, and handling nuanced text. |
| Strengths | Good at general knowledge and conversation. | Excels in providing current information and complex tasks. |
So, while both are smart, Gemini AI has a bit of an edge because it’s like it’s always learning from what’s happening right now.
The Future of AI: Beyond the Battleground
While the ChatGPT vs. Gemini clash may seem like a competition for dominance, the true potential lies in their harmonious synergy. Imagine them not as rivals, but as complementary engines powering an even more advanced AI future.
Ethical Considerations:
- Bias Busters: Both models need continued bias detection and mitigation efforts, ensuring they represent diverse perspectives and avoid perpetuating existing societal inequalities.
- Transparency and Explainability: Users deserve to understand how AI models arrive at their outputs, increasing trust and facilitating responsible engagement.
- Human Oversight: While AI advances, human oversight, and accountability remain crucial, ensuring ethical frameworks guide development and deployment.
Predictions and Challenges:
- The Rise of Specialized AI: We may see a shift from “one-size-fits-all” AI to specialized models designed for specific tasks, with ChatGPT and Gemini serving as building blocks for tailored solutions.
- Beyond Language: The focus might expand to understanding and interacting with the physical world through sensory data, pushing the boundaries of AI embodiment and interaction.
- The Explainability Gap: Bridging the gap between what AI models do and how they do it will be crucial to gain public acceptance and foster trust in AI-driven decisions.
So, in a digital coliseum bathed in algorithms, two titans clash – ChatGPT, the weaver of words, and Gemini, the architect of realities. Who will write the next chapter of AI?
Conclusion!
The comparison between Google’s GEMINI and OpenAI’s GPT-4 shows some exciting steps forward in AI technology. GEMINI is all about handling both text and images (multimodality) and being efficient, while GPT-4 focuses on being safe, well-aligned with our values, and good at solving creative problems.
Both of these AI models are great at thinking things through and are being used in real-world situations, thanks to some smart partnerships. This shows us that AI is moving in a really promising direction.
As we keep developing AI, it’s super important to deal with issues like biases and how these models handle tricky prompts. Making sure everything is transparent and teaching users about these models is key to making AI that’s responsible and ethical.
The future of AI looks exciting, with even more amazing developments expected. Watching how these models grow and how they’re used around the world is something to look forward to.
If you’re interested in developing ChatGPT models, Redblink has a team of skilled ChatGPT developers ready to assist you. Reach out to us to discuss your AI project and explore the possibilities of ChatGPT technology.
References:
Director of Digital Marketing | NLP Entity SEO Specialist | Data Scientist | Growth Ninja
With more than 15 years of experience, Loveneet Singh is a seasoned digital marketing director, NLP entity SEO specialist, and data scientist. With a passion for all things Google, WordPress, SEO services, web development, and digital marketing, he brings a wealth of knowledge and expertise to every project. Loveneet’s commitment to creating people-first content that aligns with Google’s guidelines ensures that his articles provide a satisfying experience for readers. Stay updated with his insights and strategies to boost your online presence.