OpenAI o3-mini vs OpenAI o3: Which is Superior at Detecting Complex Bugs?

Detecting subtle, intricate bugs remains one of the toughest challenges in software development, despite major advancements in AI-driven code generation. Recently, OpenAI introduced two promising models—o3-mini and o3—both designed to enhance software verification capabilities. In this blog post, I directly compare these two models, evaluating their effectiveness at catching complex software 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

Overall Performance

Both models exhibited strong overall performance, with only a slight advantage for OpenAI o3:

• OpenAI o3: Detected 38 out of 210 bugs.
• OpenAI o3-mini: Detected 37 out of 210 bugs.

This minimal performance difference suggests both models possess robust capabilities, with slight contextual advantages for the larger o3 model in certain situations.

Performance Breakdown by Language

Here's a detailed language-specific performance analysis:

• Python:

  • OpenAI o3: 7/42 bugs detected
  • OpenAI o3-mini: 7/42 bugs detected (Equal performance)

• TypeScript:

  • OpenAI o3: 7/42 bugs detected
  • OpenAI o3-mini: 7/42 bugs detected (Equal performance)

• Go:

  • OpenAI o3: 7/42 bugs detected
  • OpenAI o3-mini: 7/42 bugs detected (Equal performance)

• Rust:

  • OpenAI o3: 9/41 bugs detected
  • OpenAI o3-mini: 9/41 bugs detected (Strong and equal performance)

• Ruby:

  • OpenAI o3: 8/42 bugs detected
  • OpenAI o3-mini: 7/42 bugs detected (Slight advantage for OpenAI o3)

Analysis: What Drives the Differences?

The very close overall performance of these models is intriguing, highlighting the nuanced differences between them. Both models clearly leverage sophisticated reasoning capabilities, successfully identifying a similar number of bugs across languages like Python, TypeScript, Go, and Rust. This performance consistency indicates that even the smaller-scale o3-mini incorporates effective reasoning capabilities comparable to the larger o3 model.

The slight edge OpenAI o3 demonstrated in Ruby, however, provides insight into where architectural differences or more extensive contextual analysis could offer advantages. Ruby—known for its nuanced idiomatic patterns and dynamic constructs—seems particularly suited to the enhanced analytical depth provided by the larger o3 model. Thus, when tackling particularly complex logic-driven scenarios, o3’s deeper context-awareness might offer critical benefits.

Highlighted Bug Example: Ruby Audio Processing (Test #33)

An insightful example showcasing o3’s advantage emerged within a Ruby-based audio processing library, specifically involving the calculation of normalize_gain:

• OpenAI o3’s Analysis:
  "The bug exists in the TimeStretchProcessor class, specifically how it calculates normalize_gain. Instead of adjusting gain dynamically based on the stretch_factor, a fixed formula was incorrectly used. This oversight causes the output audio to have incorrect amplitude—too loud or too quiet—depending on the stretching factor applied."

Interestingly, o3-mini missed this subtle logical issue, while OpenAI o3 successfully identified it. This specific case underscores how additional contextual awareness or deeper reasoning analysis within o3 can detect semantic and logical inconsistencies that simpler pattern-based analysis might miss.

Final Thoughts

Overall, the comparison indicates that both OpenAI models exhibit strong capabilities in detecting complex software bugs. OpenAI o3-mini's nearly equivalent overall performance is especially impressive, suggesting its reasoning mechanisms are robust and efficient. However, the subtle advantage of OpenAI o3, particularly in handling nuanced logical and semantic issues