OpenAI 4o vs. 4o-mini: Which AI Model Is Better at Catching Hard Bugs?

Introduction

AI-driven bug detection has advanced significantly with recent improvements in Large Language Models (LLMs). Yet, catching subtle, complex bugs—those lurking deep within intricate logic—is still a tougher challenge than code generation itself.

In this post, I evaluate and compare two models from OpenAI: 4o and its reasoning-focused counterpart, 4o-mini, to understand their strengths at identifying these challenging bugs across multiple programming languages.

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

After testing the models on 210 intentionally difficult-to-catch bugs across Python, TypeScript, Go, Rust, and Ruby, the results were insightful:

• Overall Performance: OpenAI 4o identified 19 out of 210 bugs, whereas OpenAI 4o-mini found 20 out of 210. While modest, these results highlight both the difficulty of the task and the subtle advantage of reasoning capabilities.

Breaking it down by language:

• Python: OpenAI 4o slightly outperformed 4o-mini (6 vs. 4 bugs detected). Python’s widespread use and abundant training data likely helped both models detect common asynchronous patterns.

• TypeScript: OpenAI 4o again had a slight edge, catching 4 bugs versus 4o-mini’s 2. TypeScript’s strong typing likely supported the pattern-recognition strengths of the larger model.

• Go: Performance was closely matched, with OpenAI 4o detecting 4 bugs and 4o-mini finding 3.

• Rust: Interestingly, OpenAI 4o-mini marginally outperformed 4o (4 vs. 3 bugs), highlighting that reasoning may offer slight advantages in Rust’s safety-focused environment.

• Ruby: The most notable difference emerged in Ruby, where OpenAI 4o-mini detected twice as many bugs (6) compared to 4o’s (3). Ruby’s dynamic nature and complexity significantly favored 4o-mini’s reasoning approach.

Why Reasoning Matters for Bug Detection

The slight overall advantage of OpenAI 4o-mini, especially pronounced in Ruby, underscores a crucial insight: reasoning or "thinking" steps appear especially valuable for dynamically-typed languages and environments lacking clear, authoritative training data.

For widely-used languages like Python and TypeScript, both models showed competence due to extensive pattern-matching capabilities. However, less popular languages such as Ruby and Rust benefited notably from the reasoning approach of 4o-mini, suggesting underrepresentation in general LLM training data.

Example of an Interesting Bug

Here's a notable example from our dataset illustrating why reasoning can make a difference:

Bug: In an audio processing library written in Ruby, the TimeStretchProcessor class incorrectly calculated the normalize_gain. Instead of scaling based on the stretch_factor, it used a fixed formula, causing amplitude errors—either too loud or too quiet audio outputs.

• OpenAI 4o-mini: Successfully caught this subtle logical error by explicitly reasoning through the audio stretching logic.
• OpenAI 4o: Missed this bug, likely due to reliance on pattern detection rather than logical reasoning.

This instance highlights how critical logical reasoning is for identifying nuanced, dynamic bugs.

Final Thoughts

While the numerical differences between OpenAI 4o and 4o-mini might seem minimal, their implications for software verification are substantial. The growing importance of AI-powered reasoning models is clear, especially for dynamically-typed or less mainstream languages.

As AI continues to advance, comparative evaluations like these help refine our expectations and guide improvements in AI-driven bug detection tools. Ultimately, reasoning capabilities may prove essential for delivering safer, more reliable software.

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