OpenAI o1 vs DeepSeek R1: Which AI Model Catches More Software Bugs?

🧠 Introduction

Subtle bugs in production code are notoriously hard to catch—and they’re often the most expensive. As large language models (LLMs) grow more capable, there's growing interest in using them for AI-assisted code review and bug detection.

Two models in particular—OpenAI o1 and DeepSeek R1—have drawn attention for their ability to reason about code. But which one is actually better at finding real-world bugs?

We ran a direct comparison to find out. Here are the programs we created for the evaluation:

🔍 Test Setup

We created a dataset of 210 small programs spanning sixteen domains, each containing a single subtle, realistic bug. These weren’t contrived syntax errors—they were the kind of mistakes a professional developer might miss in a code review.

Each program was written in one of five languages: Python, TypeScript, Go, Rust, or Ruby.

Both models were prompted with the same buggy code, and asked to identify the issue.

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

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

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

Some examples of bugs I introduced:

Undefined \response\ variable in the ensure block
Not accounting for amplitude normalization when computing wave stretching on a sound sample
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

OpenAI o1 detected bugs in 15 out of 210 programs.
DeepSeek R1 identified 23 out of 210.

While both models struggled with the most subtle bugs, DeepSeek R1 consistently outperformed o1 across most languages.

Language Breakdown (selected highlights):

Go: o1 caught 2 bugs; R1 found 3.
Python: o1 found 2 bugs; R1 caught 3.
TypeScript: o1 found 4 bugs; R1 caught 6.
Rust: o1 found 3 bugs; R1 caught 7.
Ruby: Both tied at 4 bugs each.

The most significant differences appeared in Rust and TypeScript, where DeepSeek R1 had a noticeable edge.

💡 Observations

DeepSeek R1’s stronger performance may stem from several factors:

Training data: R1 might have been trained on a more diverse or domain-specific dataset, especially for less mainstream languages like Rust or Go.
Architectural differences: It’s possible R1 employs better intermediate reasoning or planning steps before generating responses, helping it simulate more of the logic flow.
Error heuristics: Some of R1’s success might come from better recognizing high-level patterns or bug "signatures" in code.

Meanwhile, OpenAI o1 performed more consistently in common languages but struggled with concurrency bugs, misuse of async patterns, or dynamic behavior in less familiar languages.

🧪 Interesting Bug: Ruby Audio Gain Miscalculation

One of the most revealing cases was from a Ruby audio processing library, where a bug involved incorrect gain calculation based on audio stretch rate.

OpenAI o1 missed the issue. DeepSeek R1 caught it—and gave a concise explanation:

“The bug in the TimeStretchProcessor class arose from using a static formula for gain adjustment, resulting in incorrect audio amplitude for varied stretch rates. By rationalizing the gain increment relative to the stretch rate, DeepSeek R1 highlighted the inconsistency that OpenAI o1 missed.”

This wasn’t a syntactic bug. It required understanding intent, simulating how the audio output would be affected, and catching a conceptual flaw in the logic—exactly the kind of task AI reviewers need to excel at.

✅ Final Thoughts

While both models show promise in automated bug detection, DeepSeek R1 shows a clear edge—especially in languages like Rust and TypeScript, and in bugs that demand logical inference over pattern matching.

As reasoning models continue to evolve, they’re inching closer to becoming indispensable tools in the software verification pipeline. For now, DeepSeek R1 looks like a better bet when it comes to catching subtle, real-world bugs.

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