Gemini 2.0 Flash vs GPT 4o: Which is Better?
In this comparison, we delve into the capabilities of two leading AI models: Google’s Gemini 2.0 Flash and OpenAI’s GPT-4o. Gemini 2.0 Flash boasts a massive 1 million token context window, significantly larger than GPT-4o’s 128K, offering enhanced capacity for handling complex data. Released more recently, Gemini 2.0 Flash also supports a broader range of modalities including video, which isn’t available with GPT-4o. Let’s explore the difference between Gemini 2.0 Flash vs GPT 4o based on 5 tasks.
Table of contents
- Gemini 2.0 Flash vs GPT 4o: Overview
- Task 1: HTML Code for Website
- Task 2: Summarize Content
- Task 3: Solve a Puzzle
- Task 4: Search the Web
- Task 5: Image Generation
- Result: Gemini 2.0 vs GPT 4o
- Conclusion
Gemini 2.0 Flash vs GPT 4o: Overview
| Feature | GPT-4o | Gemini 2.0 |
|---|---|---|
| Input Context Window | 128K tokens | 1M tokens |
| Maximum Output Tokens | 16.4K tokens | 8,192 tokens |
| Release Date | August 6, 2024 (5 months ago) | December 11, 2024 (1 month ago) |
| Knowledge Cut-off Date | October 2023 | August 2024 |
| API Providers | OpenAI, Azure OpenAI Service | Google AI Studio, Vertex AI |
| Supported Modalities | Text, Image, Audio | Text, Image, Audio, Video |
Task 1: HTML Code for Website
Prompt: Basic HTML code for a word-counting website.
Gemini 2.0 Response:
<!DOCTYPE html><br><html><br><head><br> <title>Word Counter</title><br></head><br><body><br><br> <h1>Word Counter</h1><br><br> <textarea rows="10" cols="50" placeholder="Enter your text here"></textarea><br><br><br><br> <button onclick="countWords()">Count Words</button><br><br> <p ></p><br><br> <script><br> function countWords() {<br> const text = document.getElementById("text-input").value;<br> const words = text.trim().split(/\s+/); <br> const wordCount = words.filter(word => word !== '').length; <br><br> document.getElementById("word-count").innerHTML = "Word Count: " + wordCount;<br> }<br> </script><br><br></body><br></html>Front End Design:
GPT 4o Response:
<!DOCTYPE html><br><html lang="en"><br><head><br> <meta charset="UTF-8"><br> <meta name="viewport" content="width=device-width, initial-scale=1.0"><br> <title>Word Count Tool</title><br> <style><br> body {<br> font-family: Arial, sans-serif;<br> margin: 0;<br> padding: 0;<br> display: flex;<br> justify-content: center;<br> align-items: center;<br> height: 100vh;<br> background-color: #f4f4f9;<br> }<br> .container {<br> text-align: center;<br> padding: 20px;<br> background-color: white;<br> border-radius: 8px;<br> box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1);<br> }<br> textarea {<br> width: 100%;<br> height: 150px;<br> margin-bottom: 20px;<br> padding: 10px;<br> font-size: 16px;<br> border: 1px solid #ccc;<br> border-radius: 4px;<br> resize: none;<br> }<br> button {<br> padding: 10px 20px;<br> font-size: 16px;<br> background-color: #4CAF50;<br> color: white;<br> border: none;<br> border-radius: 4px;<br> cursor: pointer;<br> }<br> button:hover {<br> background-color: #45a049;<br> }<br> .result {<br> margin-top: 20px;<br> font-size: 18px;<br> font-weight: bold;<br> }<br> </style><br></head><br><body><br><br> <div ><br> <h1>Word Count Tool</h1><br> <textarea placeholder="Type or paste your text here..."></textarea><br><br> <button onclick="countWords()">Count Words</button><br> <div ></div><br> </div><br><br> <script><br> function countWords() {<br> let text = document.getElementById('textInput').value;<br> let wordCount = text.trim().split(/\s+/).length;<br><br> // If there's no text, set word count to 0<br> if (text.trim() === '') {<br> wordCount = 0;<br> }<br><br> document.getElementById('wordCountResult').innerText = `Word Count: ${wordCount}`;<br> }<br> </script><br><br></body><br></html>Front End Design:
Observation:
For a production environment, the GPT-4o version would provide a much better user experience and would require less additional work to make it production-ready. It also follows modern web development best practices.
Verdict:
Gemini 2.0 ❌ | GPT 4o✅
Also Read: Gemini vs ChatGPT: Which is Better for Coding?
Task 2: Summarize Content
Prompt: Read the article athttps://www.analyticsvidhya.com/blog/2024/07/building-agentic-rag-systems-with-langgraph/to understand the process of creating a vector database for Wikipedia data. Then, provide a concise summary of the key steps.
Gemini 2.0 Response:
Here are the key steps to create a vector database for Wikipedia data according to the article:
- Load and chunk the documents
- Create a vector DB and persist on the disk
- Setup a vector database retriever
- Create a query retrieval grader
- Build a QA RAG chain
- Create a query rephraser
- Load web search tool
- Build Agentic RAG components
- Graph state retrieve function for retrieval from Vector DB
- Grade documents
- Rewrite query
- Web Search
- Generate Answer
- Decide to Generate
- Build the Agent Graph with LangGraph
- Test our Agentic RAG System
GPT 4o Response:
Observation:
GPT 4o’s response is more practical and implementable for someone specifically looking to build a Wikipedia vector database, while the Gemini response seems to be addressing a broader RAG system implementation that goes beyond the core database creation task.
Verdict:
Gemini 2.0 ❌ | GPT 4o✅
Task 3: Solve a Puzzle
Prompt: You are in a rush to work. You pour yourself a cup of black coffee, but it is too hot. You intend to add a fixed amount of cold milk to it, but you know that even after that, the coffee will need to cool down for a few minutes before you can drink it.
In which case does the coffee cool down more:
1) Add milk right away, then wait a few minutes before drinking.
2) Wait a few minutes, then add milk just before drinking.
Gemini 2.0 Response:
GPT 4o Response:
Observation:
For someone wanting to understand not just the answer but also the physics behind it, the Gemini response provides a more educational and comprehensive explanation. It helps readers understand both what happens and why it happens.
Verdict:
Gemini 2.0 ✅ | GPT 4o ❌
Task 4: Search the Web
Prompt: What did Sam Altman say on AGI in 2025?
Gemini 2.0 Response:
In 2025, Sam Altman, CEO of OpenAI, made several significant statements regarding Artificial General Intelligence (AGI):
- Confidence in Achieving AGI: Altman expressed confidence that OpenAI has identified the path to building AGI, as traditionally understood.
- AI Agents Entering the Workforce: He predicted that AI agents, capable of autonomously completing tasks, would begin entering the workforce in 2025, significantly impacting businesses’ output.
- “AGI” as a Sloppy Term: Altman acknowledged that the term “AGI” has become somewhat ambiguous, with different interpretations and definitions.
- Focus on Iterative Development and Safety: He emphasized the importance of iterative development and rigorous safety research as crucial aspects of responsible AGI development.
These statements highlight OpenAI’s ambitious goals and the evolving nature of the AGI field.
GPT 4o Response:
As of my last update, Sam Altman, the CEO of OpenAI, has frequently shared insights about artificial general intelligence (AGI), its development, and potential impacts. However, I don’t have access to specific events or statements beyond 2023.
Sam Altman has previously stated that AGI could dramatically transform society, making everything from healthcare to education more efficient. He has also emphasized the importance of aligning AGI with human values and ensuring safety protocols are in place. In 2023, he spoke about OpenAI’s commitment to ensuring AGI is developed responsibly, focusing on its long-term impact.
For the most accurate and up-to-date information, I recommend checking his most recent statements on platforms like Twitter or in interviews.
Would you like to know more about his past views on AGI?
Observation:
Gemini’s response is better as it accurately captured Sam Altman’s 2025 statements, provided specific insights on AGI, and organized the information logically. It directly addressed the question, included key AGI development points, and highlighted nuances like his view on “AGI” being a “sloppy term.”
Verdict:
Gemini 2.0 ✅ | GPT 4o ❌
Task 5: Image Generation
Prompt: Create image of a cat.
Gemini 2.0 Response:
GPT 4o Response:
Observation:
The image created by Gemini 2.0 is significantly clearer and better than GPT-4o, showcasing superior quality, detail, and overall visual appeal.
Verdict:
Gemini 2.0 ✅ | GPT 4o ❌
Result: Gemini 2.0 vs GPT 4o
| Task | Winner |
|---|---|
| HTML Code for Website | GPT-4o |
| Summarize Content | GPT-4o |
| Solve a Puzzle | Gemini 2.0 |
| Search the Web | Gemini 2.0 |
| Image Generation | Gemini 2.0 |
Also Read:
- DeepSeek V3 vs GPT-4o: Can Open-Source AI Compete with GPT-4o’s Power?
- o1 vs o1 pro: Is it worth spending $200?
- Is the New o1 Model Better than GPT-4o?
- DeepSeek V3 vs Claude Sonnet 3.5: Which is Better?
Conclusion
This comparison between Gemini 2.0 Flash and GPT-4o showcases their advanced capabilities and raises important questions about AI’s impact on creativity, decision-making, and ethics. As machines become smarter, we must ensure they empower humans responsibly, with clear ethical guidelines and transparency. The future of AI depends on balancing innovation with human values, ensuring technology evolves in harmony with society.
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