GPT-4 costs roughly 20x more than GPT-3.5-turbo through the API. Via ChatGPT Plus, you pay $20/month for limited GPT-4 access versus free for GPT-3.5. The question everyone's asking: is the upgrade worth it for coding tasks?

I ran an actual test. Same 20 coding tasks, both models, scored on correctness and quality. Here's what I found.

The Test Setup

I designed 20 tasks across five categories: simple utilities (4 tasks), data transformations (4 tasks), algorithm implementation (4 tasks), debugging (4 tasks), and system design (4 tasks). Each task got the same prompt for both models. I scored each response on a 1-5 scale for correctness and a 1-5 scale for code quality.

I used the API for consistent testing. GPT-4 (gpt-4) and GPT-3.5-turbo, both with temperature set to 0 for reproducibility.

Simple Utilities: Tie

"Write a function to validate email addresses." "Write a debounce function in JavaScript." "Parse a URL and extract query parameters." Stuff like that.

Both models scored nearly identically. 4.5 average on correctness for both. GPT-3.5 was slightly more verbose, adding extra validation that wasn't asked for, but the code was correct and clean. For standard utility functions, there's no reason to use GPT-4.

Data Transformations: Slight GPT-4 Edge

"Transform this nested JSON into a flat CSV format." "Write a function that groups these objects by multiple keys." The tasks were more complex but still well-defined.

GPT-4 averaged 4.5, GPT-3.5 averaged 3.8. The difference showed up in edge case handling. GPT-4 naturally handled null values and empty arrays. GPT-3.5 wrote code that worked for the happy path but crashed on edge cases I didn't explicitly mention in the prompt.

Algorithm Implementation: Clear GPT-4 Win

"Implement a trie with insert, search, and prefix matching." "Write a function to find the shortest path in a weighted graph." This is where the gap was obvious.

GPT-4 averaged 4.8. GPT-3.5 averaged 3.2. On the graph algorithm task, GPT-3.5 wrote code that looked correct but had a bug in the priority queue comparison that would produce wrong results for certain graph structures. GPT-4 got it right the first time and even added optimization for early termination.

For anything involving complex logic, multiple interacting data structures, or algorithmic thinking, GPT-4 is significantly better.

Debugging: GPT-4 Wins Big

I gave both models buggy code and asked them to find and fix the issues. GPT-4 averaged 4.5, GPT-3.5 averaged 2.8. This was the biggest gap.

GPT-3.5 tended to identify the obvious surface-level bug but miss the deeper issue. One task had a race condition and an off-by-one error. GPT-3.5 found the off-by-one but missed the race condition entirely. GPT-4 found both and explained why the race condition was particularly dangerous.

System Design: GPT-4 Wins

"Design the architecture for a real-time notification system." "How would you handle rate limiting across multiple API servers?"

GPT-4 gave more thoughtful, nuanced answers. It considered trade-offs that GPT-3.5 glossed over. GPT-3.5's answers read like a textbook summary. GPT-4's answers read like a conversation with a senior engineer who's actually built these systems. Averages: GPT-4 at 4.3, GPT-3.5 at 3.0.

The Cost Math

Through the API: GPT-3.5-turbo costs about $0.002 per 1K tokens. GPT-4 costs about $0.03-0.06 per 1K tokens. For a typical coding task (500 token prompt, 1000 token response), that's roughly $0.003 for GPT-3.5 and $0.05-0.09 for GPT-4.

If you make 50 API calls per day, GPT-3.5 costs about $4.50/month. GPT-4 costs about $75-135/month. That's a meaningful difference.

Through ChatGPT Plus ($20/month), you get GPT-4 with usage caps. Currently 25 messages per 3 hours. That's enough for most individual developers but not enough for heavy use.

My Recommendation

Use GPT-3.5 for: boilerplate, simple utilities, data transformations, test generation, documentation, and any task where the pattern is standard and well-known.

Use GPT-4 for: debugging complex issues, algorithm implementation, system design questions, code review, and anything that requires reasoning about multiple interacting concerns.

In practice, about 60-70% of my daily AI coding queries work fine with GPT-3.5. The remaining 30-40% genuinely benefit from GPT-4. My approach is to start with GPT-3.5 and escalate to GPT-4 when the answer isn't good enough. This keeps costs low while getting the quality when I need it.

The short answer: yes, GPT-4 is better for coding. No, it's not 20x better. Use them strategically.