Hi friends,
Hope you are all doing well. After having released the AI Guideline and Code of Conduct for Paradigm, it was my intent to share with you all a comparison of the ChatGPT versions, given that it is the most popular / widely used AI tool, there is. It is almost prophetic that ChatGPT 4o just got released, so while my apologies that this article has been a while in coming, but as they say, “better late than never”. 😊
As mentioned above, hopefully you all are aware by now that ChatGPT has just released its latest version, ChatGPT 4o (pronounced “ChatGPT Four Oh” and also known as GPT-4-Turbo).
So, the obvious question is, how does this compare to the earlier versions, as in ChatGPT 3.5 and ChatGPT 4?
I thought who better to answer this question than ChatGPT itself!!! Here’s how it characterized the comparison, by Parameter / Category and I gave it quite a few parameters to compare, as in:
Performance, Cost, Use Cases, Response Time, Scalability, Resource Utilization, Training and fine-tuning (The LLM Model), The Model Size (# of Parameters), Availability, Data Coverage, Data Security, and finally actual results demonstrated in Example Scenarios / Prompts.
NAME = | ChatGPT 3.5 | ChatGPT 4 | ChatGPT 4o |
Engine powering the Tool = | GPT-3.5 | GPT-4 | GPT-4-Turbo |
Feature | ChatGPT 3.5 | ChatGPT 4 | ChatGPT 4-turbo (GPT-4-turbo) |
Performance | Good for general tasks | High accuracy, coherence, and depth | Fast, efficient, slightly less accurate |
Cost (per 1,000 tokens) | More affordable – Approx. CAD 0.006 | More expensive due to higher computational needs- Approx. CAD 0.03 | Cost-effective, optimized for reduced computational overhead – Approx. CAD 0.015 |
Cost (Monthly Subscription / Usage) | Approx. CAD $25-30 per month for API access with moderate usage | Approx. CAD $50 per month | Approx. CAD $40 per month. |
Use Cases | General-purpose, content generation, customer service | Detailed content generation, complex problem-solving, in-depth analysis | Real-time applications, customer service chatbots, interactive apps |
Response Time | Moderate: ~500-800ms per request | Slightly slower due to complex processing: ~800-1200ms per request | Significantly faster: ~200-500ms per request |
Scalability | Moderate scalability | Requires more resources, less scalable | Highly scalable, optimized for efficiency |
Resource Utilization | Moderate CPU/GPU usage | High CPU/GPU usage | Low to moderate CPU/GPU usage |
Training and Fine-Tuning | Trained on ~570GB of text data, limited fine-tuning | Trained on ~1TB of text data, more fine-tuning options | Trained on ~750GB of text data, optimized for speed, limited fine-tuning |
Model Size | ~175 billion parameters | ~280 billion parameters | ~200 billion parameters |
Availability | Widely available, general access | Premium offering, often enterprise use | Accessible, cost-effective option for developers and businesses |
Data Coverage | Up to September 2021 | Up to April 2023 | Up to April 2023 |
Data Security | Standard security measures, TLS encryption | Advanced security features, TLS encryption, compliance with enterprise standards | Advanced security features, TLS encryption, compliance with enterprise standards |
Example 1: Customer Service | Provides helpful responses, may lack depth | Detailed and nuanced responses, maintains context | Quick, efficient responses, suitable for real-time interaction |
Example 2: Content Generation | Generates coherent content, may require editing | High-quality, detailed content with minimal editing | Generates good content quickly, may require some editing |
Example 3: Complex Problem Solving | Capable, but may struggle with very complex problems | Excellent at handling complex problems and providing detailed solutions | Good, but may sacrifice some depth for speed |
Here is how the Medium (the website – a favorite site of mine for all kinds of information and research) captures the key differences and the main feature enhancements that accompanied each version, which, for the most part, echoes or aligns with the ChatGPT comparison above:
ChatGPT 3.5
Model Architecture and Parameters: Built on the GPT-3.5 architecture with 175 billion parameters, ChatGPT 3.5 offers significant improvements over its predecessors. The model is more coherent and contextually aware, which enhances its ability to generate human-like responses.
Training Data and Performance: Trained on a diverse dataset up to September 2021, ChatGPT 3.5 delivers high performance in generating human-like text. Its capabilities make it suitable for a wide range of applications, from customer service chatbots to content creation tools.
Context Length: The model supports a context length of up to 4,096 tokens. This allows it to handle moderately long conversations and text inputs effectively, making it versatile for various use cases.
Integration and API Access: ChatGPT 3.5 is widely used due to its robust API access, which makes it easy for developers to integrate into different platforms and services. This broad accessibility has contributed to its popularity in numerous applications.
Cost Efficiency: Compared to later models, ChatGPT 3.5 is more cost-effective, making it a good choice for applications where budget constraints are a concern. Its lower cost does not significantly compromise its performance, offering a balanced solution for many users.
ChatGPT 4
Advanced Architecture: With a massive leap to 1 trillion parameters, ChatGPT 4 offers significantly improved language understanding and generation capabilities. This makes it much more powerful than ChatGPT 3.5, capable of handling more complex and nuanced interactions.
Multimodal Capabilities: ChatGPT 4 introduces the ability to handle both text and images, providing a richer interaction experience. This expands its use cases beyond text-only applications, making it suitable for more diverse scenarios such as visual question answering and content generation.
Enhanced Context Length: The model supports up to 8,192 tokens, allowing for even longer and more complex interactions. This is particularly beneficial for in-depth discussions and detailed content generation, enhancing its utility in various professional settings.
Safety and Ethical Considerations: ChatGPT 4 features advanced safety protocols and ethical guidelines, reducing the likelihood of generating harmful or biased content. This makes it a more reliable tool for sensitive applications where content quality and integrity are paramount.
Improved Fine-Tuning: The model offers expanded fine-tuning capabilities, enabling developers to customize it more effectively for specific tasks and domains. This adaptability makes it an excellent choice for specialized applications requiring tailored responses.
ChatGPT 4o
Optimized for Efficiency: ChatGPT 4o is an optimized version of ChatGPT 4, designed to deliver the highest performance with enhanced efficiency. This optimization makes it particularly suitable for real-time applications where quick response times are critical.
Extended Context Handling: Boasting a context length of up to 16,384 tokens, ChatGPT 4o can manage extensive conversations and large documents seamlessly. This extended context handling makes it ideal for applications that require maintaining coherence over long dialogues or processing lengthy texts.
Energy Efficiency: The model is optimized for lower energy consumption, making it a more sustainable choice for large-scale deployments and continuous usage scenarios. This efficiency does not compromise its performance, ensuring that it remains powerful while being environmentally friendly.
Dynamic Knowledge Retrieval: ChatGPT 4o features dynamic knowledge retrieval capabilities, allowing it to incorporate more recent and relevant information during interactions. This makes it highly effective for applications where up-to-date information is crucial.
Advanced Multilingual Support: The model offers optimized support for multiple languages, enhancing its usability in a global context. This multilingual support makes it more versatile for international applications, catering to a broader audience.
A Detailed Overview: ChatGPT 3.5 vs ChatGPT 4 vs ChatGPT 4o
ChatGPT 3.5, released in March 2023, marked a significant improvement over its predecessors with 175 billion parameters and a context length of up to 4,096 tokens. Its training data cut off in September 2021, but it still delivers high performance in generating human-like text. Its cost efficiency makes it suitable for budget-conscious applications, while its robust API access facilitates easy integration across various platforms.
ChatGPT 4, released in March 2024, took a giant leap with 1 trillion parameters, offering a substantial boost in language understanding and generation capabilities. It introduced multimodal capabilities, allowing it to handle both text and images. With an enhanced context length of up to 8,192 tokens, ChatGPT 4 supports longer and more complex interactions. It also features advanced safety protocols and expanded fine-tuning options, making it a powerful and versatile tool for various advanced applications.
ChatGPT 4o, the optimized version of ChatGPT 4, released in May 2024, is designed for maximum efficiency and performance. With a context length of up to 16,384 tokens, it can effortlessly handle extensive conversations and large documents. It is optimized for lower energy consumption, making it a sustainable choice for large-scale deployments. ChatGPT 4o also offers dynamic knowledge retrieval and advanced multilingual support, enhancing its effectiveness and versatility for global applications.
In Conclusion
The progression from ChatGPT 3.5 to ChatGPT 4 and the optimized ChatGPT 4o demonstrates significant advancements in AI language models. Each version builds upon the strengths of its predecessor, introducing new features and improvements that cater to a variety of use cases.
Whether you need a cost-effective solution, advanced multimodal capabilities, or an optimized model for efficiency, a ChatGPT version fits your needs. The continuous improvements in performance, efficiency, and capabilities highlight OpenAI’s commitment to advancing the field of artificial intelligence and providing powerful tools for diverse applications.
What’s Next?
- In this continuing article series, will be a comparison of the major (market leaders) LLMs in the space – think ChatGPT vs. Gemini (Google) vs. CoPilot and all its versions (Microsoft) vs. Claude (Anthropic) vs. Llama 3 (Meta) vs. Grok AI (xAI)
- Best Practice and Lunch and Learn sessions on specific Scenarios (e.g. Data Analysis, Productivity) including how some of our own are using these tools, and how you can start leveraging them.
- We will also start sessions on explaining the science and technology behind how AI works and all its branches
- Gartner, McKinsey etc. research and peek into Real life use cases
- Our internal AI Task Force meeting updates
- Setting up an AI Community of Practice and a Learning Cohort and the ability for this workforce to join them.
Happy reading.
As usual, please feel free to reach out with questions, comments and suggestions, if any, of if you are an AI enthusiast and want to chat about AI.
Till next time.
Thanks and regards,
Suvojit.