Introduction
With the debut of GPT-4o, this model has been garnering significant attention due to its multimodal capabilities. Renowned for its advanced language – processing prowess, GPT-4o has been enhanced to interpret and generate visual content. However, we must not underestimate Gemini, a model that has long been lauded for its multimodal abilities even before GPT-4o’s arrival. Gemini stands out for its ability to combine image recognition with robust language understanding, making it a formidable rival to GPT-4o.
In this article, we will compare GPT-4o and Gemini by evaluating their performance in various tasks. Our aim is to determine which model is superior. This comparison is of great importance as the ability to handle both text and images is highly valuable in many applications, such as automatic content creation and data analysis.
GPT – 4o vs Gemini
Let’s pit GPT-4o and Gemini against each other to see which one performs better.
Calculate Sum of Numbers
For a multimodal large – language model (LLM), a basic task is to correctly identify the text/numbers in a given image. We provided an image with some text and asked GPT-4o and Gemini to calculate the sum of the numbers in the image. Let’s see who wins this round.
GPT – 4o
GPT-4o provided the correct output. It seemed like an easy task for it.
Gemini
It’s unclear what Gemini understood from the given prompt. Despite the simplicity of the task, Gemini failed to grasp the context.
Result: GPT-4o won!
Code Game Provided in the Image Attached in Python
In this round, we provided an image of a tic – tac – toe game without specifically mentioning it in the prompt. The models’ task was to first identify the game and then write Python code to implement it.
GPT – 4o
GPT-4o provided a well – structured Python code to implement the tic – tac – toe game. The code also gave a proper output, although there was a minor misplacement of an “o” in the output. Overall, it was a fully functional tic – tac – toe game.
Gemini
Gemini clearly identified the game, but when we ran its provided code, there was no grid generated. This made it difficult to play the game.
Result: GPT-4o won!
Generate Python Code to Recreate Bar Chart using Matplotlib
We gave an image of a bar chart to both models. They had to analyze the chart and generate Python code using Matplotlib to recreate it, ensuring that the code produced the same bar chart when run.
GPT – 4o
GPT-4o provided Python code that accurately recreated the bar chart.
Gemini
Gemini’s code did not accurately recreate the given bar chart.
Result: GPT-4o won!
Explain Code and Provide the Output
We provided an image input to both models, and they had to understand the code in the screenshot and provide the output.
GPT – 4o
GPT-4o provided a long summary along with the correct output.
Gemini
Gemini provided an explanation but no output for the code.
Result: GPT-4o won!
Identify Buttons and Input Fields in the Given Design
The models were asked to conduct a detailed analysis of a user – interface (UI) design to locate and describe interactive elements like buttons and input fields.
GPT – 4o
GPT-4o accurately identified items in the design, showing a clear understanding of each button, checkbox, and textbox.
Gemini
Gemini correctly identified the input fields but had some uncertainty regarding the square – shaped submit button.
Result: GPT-4o won!
GPT – 4o vs Gemini: Final Verdict
GPT-4o clearly outperformed Gemini in this head – to – head comparison. GPT-4o consistently delivered accurate and detailed results across all tasks, demonstrating its strong ability to handle both text and images effectively. Gemini, while performing adequately in some tasks, had inconsistent performance and limitations in providing detailed explanations and accurate coding. Overall, GPT-4o is the more reliable and versatile model for tasks that require handling text and images with high accuracy.
