ChatGPT and Gemini are constantly breaking new barriers. GPT-4o has become one of the highest-performing AI models available, while Gemini is keeping up with its Ultra and Pro models. Both offer unique benefits while offering many of the same services.
So, what’s the difference, and is one better?
In this comparison, we will delve into what ChatGPT and Gemini are and the details that set them apart.
What is ChatGPT?
ChatGPT is a generative AI chatbot that can generate complex responses to user inputs. Users can converse with ChatGPT or instruct it to perform tasks related to text, coding, and image generation. With the ability to build on the context of previous inputs and outputs, ChatGPT can perform complex tasks and carry on in-depth conversations.
ChatGPT was launched in November 2022. As a sibling model of InstructGPT, it quickly stood apart for its ability to engage in meaningful dialogue. It could take simple inputs and produce thoughtful outputs. Over the course of a conversation, it could self-correct, challenge false inputs from the user, and even apologize for incorrect information. If presented with falsehoods or sensitive inputs, it would reject them.
The original ChatGPT model released in 2022 was trained with a lot of human supervision. Human trainers fine-tuned conversational parameters. Over time, ChatGPT became highly autonomous.
In 2024, ChatGPT’s current models are much more sophisticated than those of late 2022. Users can interact with ChatGPT for many different purposes, such as productivity or entertainment. A free account provides access to GPT-4 mini and limited access to the far more sophisticated GPT-4o. As new models come out, the potential use cases will expand even further.
What is Gemini?
Gemini (formerly Bard) is an AI chatbot and widget with direct access to Google AI. Like ChatGPT, Gemini can assist users with writing tasks, ideation, planning, coding, and more. From the user’s point of view, it’s almost the same as other chatbots; you provide input, and it provides an impressive response.
Gemini was released near the end of 2023. The current Gemini models were built by a team at Google DeepMind, Alphabet’s AI research and development arm. Gemini was released as the most capable and useful AI project released by Google.
In its history, which is shorter than ChatGPT’s, Gemini has quickly become one of the most competitive AI chatbot platforms. The technical expertise and training that went into Gemini is apparent in its responses and its outstanding performance on many of the AI benchmark tests.
At the time of its release, Gemini had already offered the most advanced LLMs from Google.
How ChatGPT, Gemini, and Other Chatbots Work
ChatGPT is a large language model (LLM) developed by OpenAI, while Google created Gemini. That means that GPT models generate text based on inputs received by users. The process for generating those responses is what set ChatGPT aside from previous chatbots.
First, an LLM needs to be trained on textual information to be able to process user inputs and generate responses. So, during this phase, developers train the model on massive quantities of publicly available data. Like a human mind expands from learning, LLMs become more capable after learning from books, articles, research studies, websites, and other content.
The training process involves teaching the model to predict the next word, sentence, and so forth. The neural network, called a generative pre-trained transformer (GPT), helps it understand the intricacies of human language. ChatGPT and Gemini can both pick up on the details of its training material over time, including:
Sentence structure
Grammar
Reasoning
Facts
Slang
Using the training data, a chatbot engages in a step-by-step process to generate an ideal response to a user input.
Tokenization
When you type something into ChatGPT or Gemini and click “Enter,” the tokenization process begins. All the text you enter is broken down into “tokens,” smaller units that can be analyzed. Tokens are words, parts of words, and punctuation that are used to process the input. They form the foundation of natural language processing (NLP).
Each token is a small, understandable unit that can be sorted. According to OpenAI, one token normally includes about three-quarters of one word.
Tokens are given meaning through deep learning. Deep learning algorithms are trained on tokenized data to predict the next token in a sequence. Think of tokens as a series of hints in a puzzle. The deep learning algorithm predicts and adds the next tokens in sequence, in this case, generating relevant, human-like text.
When you look at various AI models, one of the biggest constraints they have is token limits. Most list a maximum number of tokens that they can process in a single input.
Understanding tokens can also help you use ChatGPT or Gemini better. If your prompts are more concise, they will be easier to understand, and your responses will be more useful.
Understanding Language
No currently available AI model can “understand” language in the same way that we do. But they can learn and understand statistical patterns and probabilities. AI models tokenize, contextualize, and process inputs with internal embeddings, then generate relevant outputs.
When a chatbot responds to you, it generates one new token at a time. Each generated token serves as part of the context for the next token.
For the above processes to create meaningful responses, AI models also need fine-tuning and strong guardrails. So, they are trained to understand how basic tasks work. For example, they take different approaches to answering questions, completing equations, generating new ideas, and so on.
Then, there are also additional safety layers. Without additional controls, AI models could easily be abused and produce offensive and wildly inaccurate responses. Part of this process also includes user feedback and training models to improve their usefulness over time. All competitive AI chatbots have a mechanism for reinforcement learning from human feedback (RLHF).
ChatGPT vs Gemini: Performance
Benchmark Performance
To offer a fair comparison, we will focus on the most capable models of each platform in late 2024:
GPT-4o, or GPT-4, where there is insufficient data
Gemini Pro, or Gemini Ultra, where there is insufficient data
Paid subscriptions are required for high-capacity use of any of these models.
Multi-Task Language Understanding (MMLU)
The MMLU benchmark compares different AI models by their ability to solve problems in 57 academic subjects. In doing so, it compares their general knowledge as well as their problem-solving capabilities.
There are 57 different academic subjects covered in the MMLU test, including STEM subjects, humanities, and social sciences. The questions range from elementary school level through university, up to the professional level. Overall, it’s the most comprehensive benchmark for information accuracy and problem-solving.
GPT-4 scored high on the MMLU test, at 86.4%, while Gemini Ultra scored 83.7%, just two places lower on the leaderboard. Gemini Pro is also one of the leading models but scored significantly lower than Ultra at 79.1%. Overall, ChatGPT has consistently kept its models at the top of language understanding models, as well as many others.
HumanEval (Evaluating Large Language Models Trained on Code)
The HumanEval benchmark tests LLMs on their ability to create code. The problems require each model to engage in problem-solving, leading to functional code that correctly synthesizes programs from docstrings. Each model is challenged with 164 original programming problems. The model must understand the language used in presenting the problem and be able to apply mathematics and coding skills.
GPT-4o outcompetes any Gemini models, with a score of 90.2%. Gemini Ultra scores 74.4% on the HumanEval benchmark. Overall, ChatGPT has stayed ahead of the curve in terms of coding capabilities. This is also true in other benchmarks testing coding.
MATH (Math Word Problem Solving)
MATH is a particularly long and comprehensive test of an AI model’s step-by-step language-based mathematical problem-solving. It tests mathematical and language comprehension with over 12,000 mathematical problems of varying lengths.
GPT-4 Turbo scored 87.92% on the MATH benchmark, coming in second place overall, while Gemini Ultra scored 53.2% and Gemini Pro scored 32.6%.
Images
Gemini offers outstanding image recognition. It is able to parse complex visuals, meaning it can understand complex charts and figures. This makes it an exceptional tool for workflows requiring image understanding, summarization, and data analysis. However, you need a subscription to unlock more advanced capabilities.
ChatGPT used to lag in this regard, but now offers impressive image generation, a lot of it for free. The quality can range, but is often quite impressive.
We prompted both ChatGPT and Gemini to generate an image based on the exact same instructions: “Generate an image of a farmer working in a corn field.” These were the results.
ChatGPT took these broad instructions and created quite an artistic, detailed, close-up perspective.
Gemini followed the instructions differently.
Because the prompt was so general, we decided to give Gemini another go, prompting it to generate a similarly detailed, close-up perspective. However, Gemini Flash 1.5 does not enable images that depict people for free.
Google Gemini can produce quality images of any kind with the right direction, and the same can be said of ChatGPT. But they stand out in different ways that require some experimentation to get used to.
Originality
Gemini and ChatGPT were trained on some of the largest quantities of data among AI tools. Naturally, all of their outputs rely on source material that varies widely in copyright protections and accuracy.
For accuracy, we have the benchmarks we’ve already covered, and others, to give us an idea of how accurate different models are when completing different tasks. But we don’t have similar data on originality.
This difficulty is largely due to the nature of LLMs. With few exceptions, most substantive claims they make cannot be cited. The answer that they come up with is not random. As we covered, it’s a prediction based on probability. That means that chatbots may get answers wrong using the training material provided, but they may also use existing information improperly.
Both ChatGPT and Gemini have faced lawsuits over issues regarding copyright-protected materials being used in their training. Such materials can include news articles and academic papers allegedly used without permission.
In reality, it’s difficult to tell where one particular claim came from when using ChatGPT or Gemini. So, it’s up to you to fact-check significant claims. It’s also up to you to find the original source of any significant information and provide the citations when you’re obliged to do so. Copying the responses that you see as-is may result in a number of issues, including inadequate citation or even copyright infringement.
These issues are all addressed by OpenAI, Google, and other companies offering AI chatbots. The fact of the matter is that AI chatbots do not retain ownership over the content of their responses.
Under both EU and US law, AI models do not own copyright and are not recognized as authors. They are not recognized as having a legal personality, meaning they cannot own tangible assets. Ultimately, whatever you prompt an AI LLM to generate becomes your responsibility.
We cannot say that either ChatGPT or Gemini is “better” at originality. Text, image, and code creation are all based on similar processes. Legally, they are also similar.
You don’t need to take it from us, either. Google Gemini and OpenAI both recognize that their models can “hallucinate” or produce inaccurate information. Their chat interfaces both have disclaimers advising you to confirm the information you see due to the risk of mistakes.
AI Detection
Older models of ChatGPT and Gemini can both be detected by AI detectors. Also, professionals like teachers and HR professionals have grown accustomed to reading through AI-produced resumes, essays, and other work. In this regard, ChatGPT-generated content is more easily detected, due in large part to its popularity.
AI detectors like AI Detector can detect any version of ChatGPT. Typically, text from newer models is harder to detect before AI detectors adjust to the new model. However, AI detectors can now detect GPT-4 generated content with nearly 99% accuracy.
The combination of its popularity and updates in top AI detection tools makes ChatGPT content easy to detect.
In the case of Gemini, human eyes have less experience dealing with it. However, there are common threads that connect all AI-generated content. While it’s distinct from other LLMs, Gemini generates content that includes giveaways that can be detected by an AI detector.
In both cases, you should consider using an AI detector. If you’re suspicious that something was written by AI, you may be right—intuition counts for a lot as we all get more accustomed to sifting through AI-generated writing. But AI detectors can help you:
Find warning signs that warrant a closer look
Help confirm any suspicions you have if those suspicions are grounded
Overall, no ChatGPT or Gemini model can claim meaningful superiority at the ability to not get detected.
If you’re curious whether something was likely generated with AI, try AI Detector.
ChatGPT vs. Gemini: The Last Word
ChatGPT and Gemini are similar tools with different parent companies, capabilities, and strengths.
ChatGPT runs on OpenAI’s generative pre-trained transformer architecture. It has kept up and, in many cases, led the way in AI chatbot development, offering multimodal capabilities, for example. It also stayed ahead of the curve, with GPT-4 and GPT-4o topping most of the important performance benchmarks.
Gemini is built on a combination of Google’s language modeling and other multimodal capabilities. Building from Bard and LaMDA, Gemini has kept up with the best of its competition as well, offering excellent image, text, and code generation.
If you want to see which one is better for you, you can get a lot out of either for free. The context windows for the free Gemini and ChatGPT models are large enough to complete a complex language task. So, you can try both!
