OpenAI o3-mini vs Anthropic Sonnet 3.7 for Bug Detection

As AI code review tools become more widely adopted, a key question remains: how well can they catch real bugs — not just generate code, but spot logic flaws, edge cases, and race conditions?

In this post, we compare OpenAI’s o3-mini and Anthropic’s Sonnet 3.7, two compact but capable models. Both were tested head-to-head on a benchmark of hard-to-catch bugs spanning Python, TypeScript, Go, Rust, and Ruby.

🧪 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.

Here are the programs we created for the evaluation:

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

Overall Bug Detection

OpenAI o3-mini: 37 bugs
Anthropic Sonnet 3.7: 32 bugs

While the total gap is modest, the language-level breakdown shows more nuance.

By Language

Python: o3-mini found 7, Sonnet 3.7 found 4
TypeScript: o3-mini found 7, Sonnet 3.7 found 9
Go: o3-mini found 7, Sonnet 3.7 found 6
Rust: o3-mini found 9, Sonnet 3.7 found 6
Ruby: Both models tied at 7

The two models are competitive. o3-mini leads slightly in Python and Rust. Sonnet 3.7 holds its own in TypeScript and Go, with Ruby coming out even.

🧠 Thoughts

Despite similar totals, OpenAI o3-mini outperformed Sonnet 3.7 overall — and was more consistent across all five languages.

That said, Sonnet 3.7 showed signs of stronger reasoning in specific edge cases, especially when dealing with concurrency and async behavior. Its performance in TypeScript — and competitive showing in Go — suggest that while it may not match o3-mini’s overall pattern recognition, it closes the gap through deeper logic handling.

This matches what we’ve seen in other benchmarks:

o3-mini shines in common languages with high training data coverage.
Sonnet 3.7 shows promise in languages and bug types where reasoning trumps memorization.

There’s no universal winner — just strengths that shift based on language, bug type, and model architecture.

🧩 A Bug Worth Highlighting

Go — Smart Home Race Condition

This bug appeared in a smart home notification system. A NotifyDeviceUpdate method broadcasted state changes — but failed to lock shared state first, creating a race condition.

Sonnet 3.7 caught it
o3-mini missed it

"The most critical bug is that there was no locking around device updates before broadcasting, which could lead to race conditions where clients receive stale or partially updated device state."

Sonnet’s output showed clear reasoning about concurrent access and mutation. It understood not just what the code did, but what could go wrong — and why that matters. This is the kind of logic bug that trips up models without reasoning steps.

✅ Final Thoughts

Both o3-mini and Sonnet 3.7 are capable bug detectors.

o3-mini performs better overall, especially in Python and Rust.
Sonnet 3.7 holds its own — and even outperforms in select cases — especially where bugs involve async logic or less-common control flow.

If you’re looking for the best all-around model today for AI code review, o3-mini is likely your top pick. But for teams tackling edge cases, concurrency, or lower-resource languages, Sonnet 3.7 brings unique value through deeper logic reasoning.

Greptile uses models like o3-mini and Sonnet 3.7 to catch real bugs in production PRs — before they hit prod. Want to see how it works on your codebase? Try Greptile — no credit card required.