2-2 Fast Models vs Reasoning Models: Making the Right Choice

This article explores the differences between fast models and reasoning models, helping you understand when to use each type for optimal results. Making the right choice can significantly improve your coding efficiency.

Consider asking GPT's reasoning model a simple question: "What does the spread operator do in JavaScript?" You wait 18 seconds while it thinks through the problem, analyzing edge cases and considering various contexts. When it finally responds, the answer is essentially identical to what GPT's fast model would have given you immediately.

Now imagine debugging a complex state synchronization issue between your React frontend and WebSocket backend. The fast model gives you three different solutions that don't quite work. Then you switch to the reasoning model. It spends 20 seconds analyzing the problem, identifies the root cause you missed, and proposes a solution that works well. This comparison illustrates the key challenge with AI models: knowing when you need speed versus when you need deep thinking.

Understanding the Two Types of AI Models

Modern AI comes in two distinct flavors, each optimized for different types of problems.

Fast Models (Non-Reasoning): these models respond immediately based on their training. They're like that experienced developer who can instantly recognize common patterns and give you quick, accurate answers. Examples include:

• GPT-4o and GPT-4 Turbo
• Claude 4 Sonnet
• DeepSeek V3-1

Reasoning Models (Reasoning): these models actually pause to think through problems before responding. They build internal chains of reasoning, consider multiple approaches, and plan their responses. Examples include:

• OpenAI's o1 series
• Claude 4 Sonnet Thinking
• DeepSeek R1

The core difference between the two is that fast models give you their first answer immediately. Reasoning Models actually work through the problem step-by-step, considering edge cases, planning their approach, and sometimes even reconsidering their initial response.

Choosing Models in Cursor

Cursor gives you access to both types through its model selection interface. You'll typically see options like:

• Claude 4 Sonnet (fast model)
• Claude 4 Sonnet Thinking (reasoning model)
• GPT-4o (fast model)
• o1-preview (reasoning model)

The question becomes: which one should you choose for different coding tasks?

When Fast Models Work Well

For most of your daily coding work, fast models are a good choice. They excel at straightforward code tasks such as adding simple features to functions or writing standard CRUD operations.

For these tasks, the "thinking time" of reasoning models doesn't add value. A fast model already knows the correct patterns and can apply them immediately. Waiting 15-30 seconds for a reasoning model to think through "How do I import React?" is inefficient when the fast model gives you the right answer right away.

When Reasoning Models Shine

Reasoning models become valuable when problems require actual problem-solving rather than pattern recognition. We recommend using reasoning models for complex architecture decisions such as designing system interactions between multiple services.

Reasoning models also handle multi-step problem solving well, like debugging issues that span multiple components, optimizing performance across different layers, or planning migration strategies.

They're particularly valuable for ambiguous or poorly defined problems. Instead of trying to describe every detail, you can give them vague descriptions like "The user interface feels slow but I'm not sure why" or "This feature works sometimes but fails under certain conditions," and they'll work through the analysis systematically.

Developing Your Model Intuition

A practical approach is to experiment and build intuition. Start with fast models for most tasks since they're more efficient, then switch to Reasoning Models when fast models give unsatisfactory results. Pay attention to patterns—which types of problems benefit from extra thinking time? Time your interactions to determine if the wait time is worth the improved answer quality.

After a few weeks of conscious model selection, you'll develop an intuitive sense of when each type serves you well.

Pro Tip: For complex problems, sometimes a good approach is to start with a thinking model to understand the problem deeply, then switch to a fast model for implementation tasks.

The goal isn't to always use the "right" model—it's to match the tool to the task and optimize for your overall productivity.

Support ExplainThis

If you found this content valuable, please consider supporting our work with a one-time donation of whatever amount feels right to you through this Buy Me a Coffee page.

Creating in-depth technical content takes significant time. Your support helps us continue producing high-quality educational content accessible to everyone.