OpenAI and Google have been challenging each other with their LLMs since 2023. Initially, it seemed that OpenAI had dominated the market with GPT 3.5. But then Google came up with Bard (now Gemini), and tried one upping the game by introducing its own capabilities. As of now the battle has gained a higher ground with GPT 4 Turbo vs Gemini 1.5 being the topic of discussion. How do these giants stack up against each other? Does one offer more features than the other? And what about accessibility? Don’t worry as we’ll find out the differences as we go along. The following blog will take a deep dive into these LLMs and find out which who’s winning among GPT 4 Turbo vs Gemini 1.5.
Understanding the contenders: GPT 4 Turbo and Gemini 1.5
In order to bring out the differences in a more pronounced manner, we must understand what exactly these two technologies are. Let’s have a look:
GPT 4 Turbo
Open AI’s GPT 4 Turbo is an enhanced version of their GPT 4 model. The GPT here stands for Generative Pre-trained Transformer, a crucial component of the AI model itself. ChatGPT sprung into popularity back in late 2022 with its GPT 3 model. A few months later it evolved to GPT 3.5, and later a paid version called GPT 4 hit the market with more robust features.
GPT 4 had a few crucial improvements over GPT 3.5. It could take image inputs and generate text captions based on the same. Doubling down on the accuracy and relevancy of the data set in use, OpenAI launched GPT 4 Turbo in November 2023. As of now, GPT 4 Turbo is equipped with an updated data set that is accurate till April 2023.
Gemini 1.5
Launched in early 2024, Google’s Gemini 1.5 is an evolved form of the previous Gemini 1.0 generative AI. Gemini doesn’t operate on a GPT model, instead, it houses a different architecture, but more on that later. Fun fact: Gemini was better known as Bard upon launch, the name change has happened only recently.
Just like ChatGPT though, Gemini has the ability to understand user text, read and create captions from images, identify characters, and much more. Although Google itself hasn’t given a clear answer on how recent is Gemini’s data set, it surely has said that it is up-to-date till February 2024 with more updates on the way.
GPT 4 Turbo vs Gemini 1.5: The differences
Let’s address the elephant in the room, GPT 4 Turbo and Gemini 1.5 when pitted against each other give interesting points to look at. This section of the blog will focus on settling the debate between GPT 4 Turbo vs Gemini 1.5:
Differences based on architecture
Architecture in AI refers to the underlying organizational structure of the model at hand. Different AIs have different architectures depending on their use, design, and development period. Let’s take a look at the architectural differences themselves:
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GPT-4 Turbo: The architecture of GPT 4 Turbo consists of a single but huge neural network. Now, this neural network houses billions of parameters for text generation and other processes. GPT 4 Turbo, in this case has been reported to have about 178 billion parameters and is quite efficient at generating text based responses. You can also say that GPT 4 Turbo’s architecture is more like that of a “Decoder-only” nature.
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Gemini 1.5: Gemini 1.5 differs from GPT 4 Turbo’s architecture since it doesn’t use a single, huge neural network. What it does instead is that it uses a Mixture of Experts (MoE) architecture. This architecture involves splitting the neural network into different “Experts”, each capable of handling specific tasks. This architecture works on a mind boggling 1.25 trillion parameters. This allows Gemini 1.5 to process huge pieces of text, images, audio and video files.
Differences based on general capabilities
This parameter is self-explanatory, every AI model comes equipped with its own set of capabilities. Here we’ll differentiate the LLMs over their respective capabilities:
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GPT 4 Turbo: GPT 4 excels at creating text pieces such as scripts, poems, paragraphs, emails, and much more. It is highly interactive and is very fluent in giving human-like outputs. It feels more personalized and is favored by a number of businesses globally for Customer Support Chatbot development. GPT 4 Turbo can also read images and give text based output on the same.
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Gemini 1.5: Gemini 1.5 can generate detailed answers through more parameters being used. It can also read images, videos and audio files and give text based outputs on them. Gemini 1.5 focuses more on the accuracy front than the human-like tone of the conversation taking place. It could be a little too direct for people looking for a conversational tone in a generative AI. It doesn’t take away the added abilities listed above though.
Differences based on answering capabilities
While capabilities in general are a good enough parameter to judge a LLM, it is the answering capability that creates the most difference. Let’s take a look at this side of the GPT 4 Turbo vs Gemini 1.5 debate:
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GPT 4 Turbo: GPT 4 Turbo answers questions with ease. It has a low response time and a more friendly tone. You can also train it to answer questions in a specific manner. As of now it is equipped with a 128,000 token content window. This translates to roughly 96,000 words for each prompt. And it answers in a 4,000 token long output window. Impressive!
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Gemini 1.5: While Gemini has the ability to answer complex queries and give accurate answers, its tone leaves something to be desired. The standard Gemini 1.5 token size is the same as GPT 4 Turbo at 128,000. However a few developers can have access to a 1 million token window as well. It isn’t the standard feature yet. So far Google hasn’t given a clear answer on the exact size of the output token size.
Differences based on reasoning prowess and multilingual support
Here, we’ll take a look at how good these LLMs are at reasoning and understanding more than 1 language.
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GPT 4 Turbo: GPT 4 is great at generating simple code snippets for a wide variety of languages. This helps a lot while analyzing code or writing blogs about certain features. However, as far as its reasoning prowess is concerned, it takes at least a couple of attempts at answering a mathematical problem accurately. It’s not that it can’t answer them at all, it’s just that it answers them upon intervention. As far as multilingual support is concerned, GPT 4 Turbo supports a slew of languages which is why the GPT model is in popular use among developers.
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Gemini 1.5: Gemini 1.5 with its MoE architecture is good at solving logical and reasoning-related questions. While it does better at solving mathematical problems, it lacks GPT 4 Turbo’s code generation prowess. Gemini 1.5 supports a number of languages as well, it can be said that there’s virtually no difference between the two on this parameter.
Choosing the “better” one
To be honest, it is a little difficult to give a definitive answer to the GPT 4 Turbo vs Gemini 1.5 question. Your definition of better might not resonate with someone else’s. However, we can list a few points where these two LLMs can be ranked against each other:
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Availability: As of now, GPT 4 Turbo is available for you to gain access. Gemini 1.5 is yet to reach the public domain.
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Fluency: While both LLMs are good at this, GPT 4 Turbo has a light edge due to its thorough training on the existing language model. It can host organic conversations with users quite easily.
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Computing power required: If you are someone running a mid-tier system, you can still run GPT 4 Turbo on it quite easily. Due to its smaller architecture, it doesn’t take that many resources.
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Result accuracy: This is where Gemini 1.5 takes the lead as its MoE architecture gives accurate results. It can also solve logical problems quite easily.
Once again, choosing between GPT 4 Turbo and Gemini 1.5 is subjective and dependent on your usage of the LLM for different tasks. There is no definitive “better” option here.
Conclusion
For now we can assume that the debate on GPT 4 Turbo vs Gemini 1.5 will rage on until further updates happen. Both LLMs are extremely capable of handling multiple queries and giving useful results. There’s a reason why their previous versions have gained so much popularity and are being used by businesses globally. If you really want to pick one among the two then choose one that satisfies your business needs the most. Whether its architecture favors you or its task handling ability fits your requirements, pick one that fits your business like a glove.