On paper, the Gemini Pro 1.5 significantly outperforms the GPT-4 with its impressive 1 million token context window. In comparison, GPT-4 only supports a context window of 128,000 tokens. This means Gemini can process and analyze much larger data sets, offering detailed insights and a broader memory for data retrieval. However, benchmark tests show that both Gemini and GPT-4 Turbo often perform similarly. In various tasks, one may slightly edge out the other, reflecting the comparable capabilities of their previous versions.
Despite these similarities, the core architectures of Gemini 1.5 Pro and GPT-4 Turbo differ significantly. Gemini uses a mixture of experts (MoE) architecture, which operates like a team with a manager assigning tasks to specialists.
This structure allows for high accuracy in specific tasks but may slow down processing when multiple submodels are required. Conversely, GPT-4 Turbo continues to refine its transformer architecture, prioritizing scalability and adaptability with a single model handling most tasks. This can make GPT-4 Turbo faster, although sometimes at the expense of accuracy.
Access to these models requires subscriptions. GPT-4 Turbo is available to all Plus subscribers, while Gemini Advanced needs to be purchased separately.
In practical tests, both models display strengths and weaknesses. For instance, when asked a simple logic question about cooking ramen, both provided accurate answers, demonstrating their quick processing speeds. However, differences emerged in the number of responses each AI generated. Gemini offers three drafts for each query, providing alternatives if the first response is unsatisfactory. This feature, while useful, sometimes leads to less direct answers.
A visual recognition test revealed challenges for both models. When counting blue dots in an image, GPT-4 Turbo incorrectly identified 12 dots instead of six. After adjustments, it still miscounted. Gemini fared slightly better but still failed to provide the correct count. However, both models gave accurate descriptions of the image, highlighting their potential in assisting visually impaired users.
Further testing with a table of phone specifications showed GPT-4 Turbo’s superior comprehension. It accurately identified the best phone based on specs and correctly guessed the phone models. Gemini struggled with these tasks, often refusing to make a decision or providing incorrect information.
Logic and memory tests showed that both AIs could handle simple reasoning tasks effectively. For example, when asked about the location of pears in a bottomless basket, both gave correct answers. However, generating creative content like a sad story revealed another weakness in Gemini. Instead of creating original content, it plagiarized a well-known Ernest Hemingway piece, raising concerns about its originality and reliability.
When it came to coding tasks, both AIs produced functional Python code for a simple game. However, GPT-4 Turbo went a step further by providing detailed instructions on how to run the code in a browser, demonstrating better user support and practical guidance.
Overall, GPT-4 Turbo emerged as the more reliable and versatile AI. It is faster, more accurate, and offers better user support, making it more suitable for enthusiasts, regular users, and coders alike. While Gemini 1.5 Pro has its unique features and potential, GPT-4 Turbo remains the superior choice for most practical applications.