OpenAI o1-mini vs o4-mini: Comparing AI Bug Detection Capabilities

At Greptile, we focus on leveraging AI to improve code reliability through advanced bug detection capabilities. Detecting subtle and intricate software bugs is significantly more challenging than generating new code, as it requires not only pattern recognition but also deeper reasoning about code logic.

Recently, I evaluated two of OpenAI’s language models—o1-mini and o4-mini—to determine which performs better at identifying hard-to-find bugs within complex software systems.

Evaluation Setup

For a fair and comprehensive assessment, I introduced 210 realistic, challenging bugs across five widely-used programming languages:

Go
Python
TypeScript
Rust
Ruby

Each bug was intentionally subtle, representative of real-world errors developers might overlook during typical code reviews, automated tests, and linting processes.

ID
1	distributed microservices platform
2	event-driven simulation engine
3	containerized development environment manager
4	natural language processing toolkit
5	predictive anomaly detection system
6	decentralized voting platform
7	smart contract development framework
8	custom peer-to-peer network protocol
9	real-time collaboration platform
10	progressive web app framework
11	webassembly compiler and runtime
12	serverless orchestration platform
13	procedural world generation engine
14	ai-powered game testing framework
15	multiplayer game networking engine
16	big data processing framework
17	real-time data visualization platform
18	machine learning model monitoring system
19	advanced encryption toolkit
20	penetration testing automation framework
21	iot device management platform
22	edge computing framework
23	smart home automation system
24	quantum computing simulation environment
25	bioinformatics analysis toolkit
26	climate modeling and simulation platform
27	advanced code generation ai
28	automated code refactoring tool
29	comprehensive developer productivity suite
30	algorithmic trading platform
31	blockchain-based supply chain tracker
32	personal finance management ai
33	advanced audio processing library
34	immersive virtual reality development framework
35	serverless computing optimizer
36	distributed machine learning training framework
37	robotic process automation rpa platform
38	adaptive learning management system
39	interactive coding education platform
40	language learning ai tutor
41	comprehensive personal assistant framework
42	multiplayer collaboration platform

Results

Overall Performance

Overall, OpenAI o4-mini slightly outperformed o1-mini:

OpenAI o4-mini: Identified 15 out of 210 bugs.
OpenAI o1-mini: Identified 11 out of 210 bugs.

Though the numbers appear modest, the complexity of these deliberately subtle bugs underscores the significant challenge faced by current AI models in software verification.

Language-Specific Breakdown

Let's examine how each model performed by programming language:

Go:
- OpenAI o1-mini: 2/42 bugs detected
- OpenAI o4-mini: 1/42 bugs detected (o1-mini demonstrated stronger capability here)
Python:
- OpenAI o4-mini: 5/42 bugs detected
- OpenAI o1-mini: 2/42 bugs detected (o4-mini performed substantially better)
TypeScript:
- OpenAI o4-mini: 2/42 bugs detected
- OpenAI o1-mini: 1/42 bugs detected (Marginal difference, slight advantage o4-mini)
Rust:
- OpenAI o4-mini: 3/41 bugs detected
- OpenAI o1-mini: 2/41 bugs detected (Close performance, slight o4-mini advantage)
Ruby:
- Both models: 4/42 bugs detected (Equal performance)

Insights and Analysis

These results illustrate the differing strengths of the two models. OpenAI’s o4-mini, which incorporates explicit reasoning steps, appears particularly adept at handling languages like Python, where logic errors and nuanced syntax problems frequently occur. This reasoning component enables the model to logically deduce and simulate code execution, making it effective in detecting bugs beyond surface-level pattern recognition.

In contrast, o1-mini, a model primarily reliant on pattern matching, performed slightly better in Go, a language widely represented in training data and characterized by distinct idiomatic patterns. This indicates that traditional pattern-based models may excel in well-documented, structured environments, whereas reasoning-enhanced models excel in scenarios involving subtler, logic-driven errors.

The even performance in Ruby could reflect inherent complexities or specific coding patterns that neither model currently fully addresses, indicating areas for future model improvement.

Highlighted Bug Example: Async Keyword Misuse in Python

One particularly illustrative bug highlights the reasoning capabilities of o4-mini. In Python test #29, involving a bioinformatics toolkit, OpenAI o4-mini identified an asynchronous syntax error that o1-mini overlooked:

OpenAI o4-mini’s Analysis:
"The code mistakenly uses await self._calculate_distance_matrix(sequences) in a non-async method. Since _calculate_distance_matrix returns a list synchronously, awaiting it results in a TypeError: 'list' object is not awaitable."

This subtle yet critical error demonstrates o4-mini’s reasoning ability—recognizing improper asynchronous usage by logically simulating the method's execution. OpenAI o1-mini’s inability to detect this bug underscores the advantage of reasoning-enhanced models in nuanced error detection scenarios.

Final Thoughts

Although both OpenAI models demonstrate meaningful bug detection capabilities, o4-mini’s embedded reasoning step clearly provides a promising advantage in detecting complex, logic-driven software errors. As AI continues evolving, models capable of sophisticated logical analysis, like OpenAI o4-mini, will likely become invaluable tools for developers, substantially improving software reliability and efficiency in the development process.