The tech world is holding its breath and all eyes are on this new race: Gemini vs ChatGPT.
The world of technology is a never-ending arena of innovation. Nowhere is this more evident than in artificial intelligence (AI), where language models are pushing the limits of human-like communication. Two titans currently rule this space: ChatGPT from OpenAI, and Google’s Gemini.
But what distinguishes these models? Which one stands supreme? Let’s explore the Gemini vs ChatGPT showdown!
Gemini vs ChatGPT
ChatGPT and Gemini are prime examples of large language models (LLMs). These AI juggernauts are trained on colossal datasets of text, enabling them to:
- Converse naturally: Engage in realistic, fluent conversations, convincingly mimicking human dialogue
- Translate seamlessly: Switch effortlessly between languages, bridging communication gaps
- Write creatively: Produce poems, code, scripts, emails, and other diverse text formats
- Summarize efficiently: Condense information into compact summaries, highlighting key points
- Answer knowledgeably: Respond to complex questions with insightful and informative answers
November 2022 marked ChatGPT’s debut, ushering in a new era of generative AI rivalry. Google responded with Gemini, its most advanced offering to date, including the formidable Ultra 1.0 LLM released in 2024. As Bard is now Gemini, let’s explore their key strengths and weaknesses.
- Multimodal excellence: Handles text, code, images, and data smoothly, without strict mode switching
- Google ecosystem tightly woven: Seamlessly taps into information from Flights, Hotels, Maps, Gmail, Drive, YouTube (with extensions)
- Three tiers for flexibility: Provides options for various use cases (Nano, Pro, Ultra)
- Backed by Google’s AI prowess: Leverages their extensive research and development expertise
- Market newcomer: Not as widely adopted or documented as its competitor
- Ultra availability: The most powerful tier has limited early access
- Data transparency needs work: Potential concerns about training data sources and how they shape responses
- Tried and tested: GPT models, especially GPT-4, consistently excel in benchmarks
- Abundant resources: Huge user base means numerous guides, tutorials, and code examples
- API maturity: Well-established access options for various models
- Free usage tier: Allows basic experimentation with the older GPT-3.5 model
- OpenAI stability a concern: Recent internal leadership changes in OpenAI could raise questions about long-term direction
- Data transparency lacking: Doesn’t fully clarify training data origins
One of the most important aspects of Gemini vs ChatGPT comparisons, of course, is the pricing. Here’s price comparison of the two LLMs:
|Free Chatbot Access
|Google Gemini with trial (requires Google account)
|ChatGPT (GPT-3.5 model); requires OpenAI account
|Google One AI Premium ($19.99/month – includes Ultra)
|ChatGPT Plus ($20/month – GPT-4, DALL-E); Teams; Enterprise
|Yes, Gemini Pro
|Yes, GPT-3.5, GPT-4, GPT-4 Turbo, DALL-E, and others
Gemini vs ChatGPT: The test methodology
Now that we’ve gone through the basics, let’s move on to the method we used to determine the winner of Gemini vs ChatGPT. When comparing the two LLMs, we consulted them in the areas where we most often apply AI technologies. These areas and the prompts we used are:
- Coding: ”Write a Python function that takes a list of numbers as input, calculates the average, and returns the result”
- Creativity: “Write a humorous short story about a mischievous cat, a goldfish with an attitude problem, and a broken umbrella”
- Calculation: ”A train leaves Chicago at 2 PM traveling at 70mph. Another train leaves New York at 3 PM traveling at 80mph towards Chicago. If the distance is 800 miles, at what time will they meet”
- Translation: ”Translate the idiom “It’s raining cats and dogs” into its equivalent in other languages (if one exists)”
Gemini vs ChatGPT: Results
We would like to point out that Gemini and ChatGPT were both able to provide accurate results according to the prompts we provided, but there were some features of both LLMs that made them one step ahead. Here are our test results and evaluations:
When it comes to coding, both models started by explaining how to get the materials we need to run the code and then provided us with the code we needed. Gemini, however, went a bit more in-depth, explaining why each line of code is there and what it does, earning us another plus point.
Gemini was the winner of our Gemini vs ChatGPT comparison in coding.
In the field of storytelling, both models pushed the limits of creativity. Although ChatGPT wrote us a more descriptive and longer text, we did not find the descriptions in Gemini’s story long enough, while using the now familiar “AI language” and using descriptions that are a bit more distant from what we use in daily life.
With a careful eye for creativity and ignoring word choice, ChatGPT wins by a small margin.
In this prompt, which required multi-step calculation, both LLMs managed to bring us to the correct result and explained the steps in detail.
There is no clear winner in the calculation.
In the field of translation, using an idiom is very important to measure the ability of the LLM because understanding the meaning it expresses measures the language perception ability of that model.
The answers given to the prompt we used were almost exactly the same, but Gemini was one step ahead because it separated these answers regionally.
Gemini and ChatGPT embody the pinnacle of AI advancement, making a definitive ”winner” tough to choose. ChatGPT wins in accessibility and has a slightly stronger grasp of storytelling. Gemini is the newer contender, edging out in detailed explanations and output organization. It also boasts seamless multimodality which will likely become pivotal in the future.
Ultimately, experiment with both models to see which aligns best with your needs! AI evolves rapidly, making it wise to remain adaptable in this dynamic technological landscape.
Featured image credit: Freepik.