OpenAI o3-mini vs OpenAI o4-mini: Comparing AI Models for Software Bug Detection

As software complexity continues to grow, developers face increasing challenges in detecting subtle and complex bugs. AI-driven code review tools promise to help solve this problem, particularly through models capable of deep reasoning and logical analysis.

Recently, I evaluated two notable AI language models—OpenAI o3-mini and OpenAI o4-mini—to determine their effectiveness in detecting hard-to-find bugs across several programming languages. Unlike conventional language models, these advanced models incorporate a reasoning ("thinking") phase, theoretically enhancing their ability to analyze code logic and context.

The Evaluation Dataset

I wanted to have the dataset of bugs to cover multiple domains and languages. I picked sixteen domains, picked 2-3 self-contained programs for each domain, and used Cursor to generate each program in TypeScript, Ruby, Python, Go, and Rust.

Next I cycled through and introduced a tiny bug in each one. The type of bug I chose to introduce had to be:

1. A bug that a professional developer could reasonably introduce
2. A bug that could easily slip through linters, tests, and manual code review

Some examples of bugs I introduced:

1. Undefined `response` variable in the ensure block
2. Not accounting for amplitude normalization when computing wave stretching on a sound sample
3. Hard coded date which would be accurate in most, but not all situations

At the end of this, I had 210 programs, each with a small, difficult-to-catch, and realistic bug.

A disclaimer: these bugs are the hardest-to-catch bugs I could think of, and are not representative of the median bugs usually found in everyday software.

Results

Overall Performance

OpenAI’s o3-mini significantly outperformed the newer o4-mini model overall:

• OpenAI o3-mini: Detected 37 out of 210 bugs.
• OpenAI o4-mini: Detected 15 out of 210 bugs.

This unexpected outcome indicates that despite advancements in the o4-mini model, certain practical limitations impacted its real-world effectiveness.

Language-Specific Breakdown

Detailed analysis by language provided further clarity:

• Python:

  • OpenAI o3-mini: 7/42 bugs detected
  • OpenAI o4-mini: 5/42 bugs detected (Slight advantage for o3-mini)

• Go:

  • OpenAI o3-mini: 7/42 bugs detected
  • OpenAI o4-mini: 1/42 bugs detected (Clear advantage for o3-mini)

• TypeScript:

  • OpenAI o3-mini: 7/42 bugs detected
  • OpenAI o4-mini: 2/42 bugs detected (Notable advantage for o3-mini)

• Rust:

  • OpenAI o3-mini: 9/41 bugs detected
  • OpenAI o4-mini: 3/41 bugs detected (Significant advantage for o3-mini)

• Ruby:

  • OpenAI o3-mini: 7/42 bugs detected
  • OpenAI o4-mini: 4/42 bugs detected (Closer, though o3-mini still leads)

Interestingly, the gap narrowed in Ruby, suggesting that o4-mini’s reasoning approach might hold specific advantages in languages with less widely available training data.

Analysis and Insights

The unexpectedly strong performance of OpenAI o3-mini relative to o4-mini warrants deeper consideration. Theoretically, o4-mini’s more advanced architecture and enhanced reasoning capabilities should provide an edge. However, practical testing showed that o3-mini consistently performed better, especially in more widely-used languages like Python, Go, TypeScript, and Rust.

This difference might be attributed to training methodologies, dataset coverage, or efficiency in pattern recognition in o3-mini. Particularly in Go, TypeScript, and Rust, o3-mini's comprehensive training on established code patterns seems to have outpaced the more reasoning-heavy approach of o4-mini, indicating potential areas of optimization for future reasoning-based models.

Conversely, in Ruby, the smaller performance gap hints that the advanced reasoning capabilities of o4-mini could indeed provide added value in less common languages, where deeper logical deduction might compensate for limited training examples.

Highlighted Bug Example: Ruby Audio Processing (TimeStretchProcessor Class)

An illustrative example emphasizing the potential advantage of o4-mini’s reasoning approach is the Ruby-based audio processing bug in the TimeStretchProcessor class:

• Bug description (OpenAI o4-mini’s Analysis):
  "The critical issue resides in how normalize_gain is calculated within the TimeStretchProcessor class. Instead of dynamically adjusting gain based on the stretch_factor, a fixed formula is used. Consequently, audio outputs have incorrect amplitudes, being either excessively loud or quiet depending on the stretch factor."

Interestingly, o4-mini successfully identified this subtle yet significant logical error, whereas o3-mini did not. This case highlights how enhanced reasoning can occasionally uncover nuanced, logic-intensive bugs that pattern-oriented models might miss.

Final Thoughts

Although OpenAI o3-mini demonstrated superior overall performance, particularly in mainstream programming languages, the Ruby case study reveals important potential for enhanced reasoning models like o4-mini in specific scenarios. These findings suggest that future AI-driven software verification tools could benefit from strategically balancing extensive pattern recognition with deeper logical reasoning.

As AI models continue to evolve, such nuanced capabilities will undoubtedly become essential in empowering developers to deliver increasingly reliable, high-quality software.