GPT3.5 vs GPT4 - When to use GPT4?

What's AI by Louis-François Bouchard
15 May 202300:56

TLDRThe script discusses the decision-making process regarding the use of different GPT models, highlighting the similarities and differences between GPT-3.5 and GPT-4. It suggests using GPT-3.5 for non-complicated tasks due to its lower cost and better latency, while GPT-4 is recommended for more complex, multi-step problems. The choice depends on cost considerations and the complexity of the task at hand.

Takeaways

  • 🤖 GPT models vary in complexity and cost, with GPT-3.5 and GPT-4 being common choices.
  • 🚀 For most users, GPT-3.5 is a cost-effective choice for general problem-solving.
  • 📈 GPT-4 is more expensive but excels at handling complex, multi-step tasks with specific constraints.
  • 🔍 The output quality between GPT-3.5 and GPT-4 is similar for non-complicated tasks.
  • 💡 GPT-4 is recommended when the application requires advanced problem-solving capabilities.
  • ⏱️ GPT-3.5 offers better latency and lower costs, making it suitable for budget-conscious users.
  • 📊 The decision to use GPT-3.5 or GPT-4 hinges on the complexity of the task and the user's budget.
  • 🌟 GPT-4's advantages become apparent in intricate problem-solving scenarios.
  • 💰 Cost is a significant factor in choosing between GPT-3.5 and GPT-4.
  • 📋 When comparing models, consider the trade-off between performance and cost.
  • 🛠️ Users should align their choice of GPT model with the specific requirements of their application.

Q & A

  • What is the primary decision users face when choosing between GPT models?

    -The main decision is between using GPT-3.5 and GPT-4, considering factors like cost, problem complexity, and performance.

  • Why might someone choose GPT-3.5 over GPT-4?

    -GPT-3.5 is generally more cost-effective and has better latency, making it suitable for non-complicated tasks where the cost of GPT-4 might not be justified.

  • In what scenarios would GPT-4 be the preferred choice?

    -GPT-4 is better for solving more complicated and intricate problems, especially those involving multi-step constraints and specific requirements.

  • How do the outputs of GPT-3.5 and GPT-4 compare for non-complicated tasks?

    -For non-complicated tasks, the outputs of GPT-3.5 and GPT-4 are generally very similar.

  • What are the advantages of GPT-4 in terms of problem-solving?

    -GPT-4 excels at handling complex problem-solving scenarios, which may require advanced understanding and processing capabilities beyond those of GPT-3.5.

  • What is latency in the context of GPT models?

    -Latency refers to the delay between the input of a query and the generation of a response by the AI model. Lower latency means faster responses.

  • How does cost factor into the decision between GPT-3.5 and GPT-4?

    -GPT-4 is much costlier than GPT-3.5, so users must weigh the benefits of GPT-4's enhanced capabilities against their budget constraints.

  • What are some examples of tasks that might require GPT-4's advanced capabilities?

    -Tasks that involve complex reasoning, understanding of nuanced language, or the ability to handle multiple steps with specific constraints would benefit from GPT-4.

  • How does GPT-3.5 perform in comparison to GPT-4 for complex tasks?

    -While GPT-3.5 can handle many tasks, it may not perform as effectively as GPT-4 when it comes to very complex problem-solving scenarios.

  • What is the general recommendation for users with limited budgets?

    -For users with limited budgets, it is recommended to use GPT-3.5, as it can solve many problems effectively at a lower cost.

  • How can users determine the complexity of their tasks?

    -Users should analyze the number of steps, the specificity of the requirements, and the intricacy of the problem to determine if it is complex enough to warrant using GPT-4.

Outlines

00:00

🤖 Understanding GPT Models

This paragraph discusses the differences between GPT-3.5 and GPT-4, highlighting the decision-making process for choosing the appropriate model. It suggests that GPT-3.5 is generally sufficient for non-complicated tasks due to its lower cost and better latency. However, for more complex problem-solving involving multi-step constraints and specific requirements, GPT-4 is recommended despite its higher cost.

Mindmap

Keywords

💡GPT models

GPT models refer to a series of natural language processing models developed by OpenAI, with 'GPT' standing for Generative Pre-trained Transformer. These models are designed to generate human-like text and are used in various applications, such as chatbots, content creation, and language translation. In the context of the video, the discussion is centered on the decision between using GPT-3.5 and GPT-4, highlighting their differences in performance and cost.

💡GPT-3.5

GPT-3.5 is a specific version of the GPT model series. It is known for its ability to perform a wide range of language tasks, but at a lower cost compared to its successors. The video script suggests that for non-complicated tasks, GPT-3.5 can be a more cost-effective choice, as its output is generally similar to that of more advanced models.

💡GPT-4

GPT-4 is an upgraded version of the GPT models, offering improved capabilities, particularly in solving more complex and intricate problems. It is designed to handle multi-step constraints and specific requirements, making it superior to GPT-3.5 for applications that require advanced problem-solving skills. The video emphasizes that while GPT-4 is more powerful, it also comes with higher costs.

💡Cost

In the context of the video, cost refers to the financial expenditure associated with using different GPT models. GPT-4, being a more advanced model, is much costlier to use than GPT-3.5. The decision to use one model over the other often depends on the budget and the complexity of the tasks at hand.

💡Latency

Latency, in this context, refers to the delay before a GPT model produces a response. The video script mentions that GPT-3.5 has better latency, meaning it can respond more quickly than GPT-4. This can be an important factor for applications where real-time responses are crucial.

💡Problem Solving

Problem solving is the process of finding solutions to given tasks or challenges. The video discusses the varying capabilities of GPT-3.5 and GPT-4 in problem-solving, with GPT-4 being more adept at handling complex and multi-step problems. This is a key consideration for users deciding which model to use based on the nature of their tasks.

💡Output

Output in the context of GPT models refers to the text or response generated by the model. The video script compares the output of GPT-3.5 and GPT-4, noting that for non-complicated tasks, the outputs are very similar, but for more complex tasks, GPT-4 provides superior results.

💡Complicated Tasks

Complicated tasks are those that require advanced cognitive abilities, such as understanding complex instructions, managing multiple steps, or dealing with intricate constraints. The video highlights that GPT-4 is better suited for these types of tasks compared to GPT-3.5.

💡Incentive

An incentive in this context refers to a motivation or reason for choosing one option over another. The video suggests that there is an incentive to use GPT-3.5 due to its lower cost, which can be a significant factor for users with budget constraints.

💡Non-complicated Tasks

These are tasks that are relatively straightforward and do not require advanced problem-solving skills. The video indicates that for such tasks, GPT-3.5 is often sufficient and can produce outputs that are very similar to those of GPT-4, making it a cost-effective choice.

💡Multi-step Constraints

Multi-step constraints refer to problems that involve a series of steps or conditions that must be met in a specific order or combination. The video emphasizes that GPT-4 is more capable of handling these types of constraints, which are beyond the scope of simpler models like GPT-3.5.

💡Specific Requirements

Specific requirements are detailed and particular needs that a user might have for a task. In the context of the video, GPT-4 is better equipped to meet these requirements, as it can understand and execute complex instructions more effectively than GPT-3.5.

Highlights

Demystifying the different GPT models

Decision-making between GPT-3.5 and GPT-4

GPT-3.5 is generally more cost-effective

Output similarity between GPT-3.5 and GPT-4 for non-complicated tasks

GPT-4 excels in complex problem-solving

GPT-4 handles multi-step constraints and specific requirements better

GPT-4 is recommended for complex applications if affordable

GPT-3.5 offers better latency and lower cost

Latency and cost considerations for GPT-3.5

GPT-4's enhanced capabilities for intricate tasks

The trade-off between using GPT-3.5 and GPT-4

Optimizing for cost and performance

Choosing the right GPT model based on task complexity

GPT-3.5 as a viable option for simpler tasks

The potential for GPT-4 to outperform GPT-3.5

Cost-benefit analysis for selecting GPT models

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