Recommendation for Debug / review
Debugging & Code Review
The best LLM for debugging and code review is GPT-5, which holds the top spot on the Aider polyglot coding benchmark at 88%, followed closely by o3 Pro and Gemini 2.5 Pro Preview 06-05. For complex codebase analysis and finding non-obvious bugs, community evidence points to Claude Sonnet 4.5 and Gemini 2.5 Pro as strong performers, while GPT-5.4 receives more mixed feedback regarding its speed and bug-finding thoroughness. The Aider benchmark is particularly relevant here because it tests editing real code across many languages, which mirrors the actual work of debugging. Security-focused code review may benefit from specialized tools, as one user reported that Kimi k2.6 outperformed frontier models from OpenAI and Anthropic in that domain. For day-to-day debugging and code review where editing accuracy matters most, the benchmark leaders are your best bets.
About this recommendation
- Updated
- Jul 17, 2026
- Evidence through
- Jul 17, 2026
- Sources
- 12
- Revision
- v5
GPT-5 sits at the top of the Aider polyglot coding benchmark with an 88% score, making it the strongest option for code editing tasks that mirror real debugging work. While community discussion notes it can be outperformed by specialized tools for security review, its benchmark dominance speaks for itself.
Best when: You need the highest accuracy for cross-language code edits and bug fixes.
Tips
- Scores 88% on the Aider polyglot coding benchmark, the top result of 27 models tested.
Watch out for
- In multi-model security reviews, it finds fewer issues than specialized tools like Kimi k2.6 and tends to declare mission accomplished early.
o3 Pro takes second place on the Aider benchmark at 84.9%, offering strong code editing capabilities just behind GPT-5. It's a solid choice when you want OpenAI's reasoning capabilities without the top-tier pricing.
Best when: You want near-top-tier accuracy for code editing but may not need the absolute highest score.
Tips
- Scores 84.9% on the Aider polyglot coding benchmark, ranking #2 of 27 models.
Gemini 2.5 Pro Preview 06-05 ranks third on the Aider benchmark at 83.1%, making it Google's strongest offering for code editing tasks. It edges out the older 05-06 preview by a notable margin.
Best when: You prefer Google's ecosystem or need a strong non-OpenAI/Anthropic option for code editing.
Tips
- Scores 83.1% on the Aider polyglot coding benchmark, ranking #3 of 27 models.
o3 holds the fourth position on the Aider benchmark at 81.3%, a respectable showing that places it above older frontier models like Claude Opus 4 and DeepSeek R1.
Best when: You want strong coding performance at potentially lower cost than o3 Pro.
Tips
- Scores 81.3% on the Aider polyglot coding benchmark, ranking #4 of 27 models.
Claude Sonnet 4.5 has compelling community evidence for real-world debugging, with users reporting it found longstanding bugs in a months-old compiler codebase. However, its Aider benchmark score of 61.3% lags behind the top contenders, suggesting a gap between benchmark performance and practical codebase analysis.
Best when: You need deep analysis of large, unfamiliar codebases where understanding context matters more than pure editing accuracy.
Tips
- Found several longstanding bugs in a compiler codebase that the developer hadn't found after months of development.
- Identified a critical design flaw requiring redesign and provided well-reasoned rationale for the change.
GPT-5.4 receives polarizing community feedback. One user praises it as being "on another level altogether" for code review compared to Gemini 3.1 Pro. Others note it can miss bugs that smaller models find and suffers from long thinking times that mimic hung jobs.
Best when: You need deep reasoning for security audits and can tolerate slower response times.
Tips
- Described as "on another level altogether" for code review, significantly outperforming Gemini 3.1 Pro.
- Found 2 serious issues in a Rust codebase that had passed multiple audits and third-party security review.
Watch out for
- Can spend up to 10 minutes thinking through a problem, appearing to users as a hung job.
- Missed bugs that Qwen 3.6 found in a deliberate test, including a malloc issue and a missing release callback.
- Scores only 44.9% on the Aider polyglot coding benchmark, ranking #17 of 27.
Claude Opus 4.5 appears in community testing for bug-finding harnesses, though the evidence is more anecdotal. Combined with Gemini 2.5 Flash, it was used via GitHub Copilot for simple bug-finding targets.
Best when: You already use GitHub Copilot and want a frontier model integrated into your existing workflow.
Tips
- Used in combination with other models for bug-finding harnesses with simple targets.
Frequently asked
- Which LLM scores best on coding benchmarks for bug fixes?
- GPT-5 scores 88% on the Aider polyglot coding benchmark, the top result among 27 models tested for editing real code across many languages.
- Has anyone successfully used LLMs to find bugs in large codebases?
- Yes, one user reported that Claude Sonnet 4.5 found several longstanding bugs in a compiler codebase they had developed for months, including identifying a critical design issue requiring redesign.
- Are slower reasoning models worth the wait for debugging?
- It depends on your use case. GPT-5.4 can take up to 10 minutes thinking through a problem, which may appear as a hung job, but users report it operates "on another level" for deep code review compared to faster alternatives.
- Which model is best for security code review?
- Community evidence suggests Kimi k2.6 running inside Kimi code finds more security issues than Claude Opus or GPT-5, though the frontier models can validate findings to reduce false positives.
Sources
- 1
“Scores 88% on the Aider polyglot coding benchmark (the top result), which tests editing real code across many languages.”
Aider polyglot benchmark · Benchmark · Jul 21, 2026 - 2
“Kimi k2.6 7 running inside Kimi code already kicks the pants of the latest Claude and OpenAI models when it comes to cyber security. I regularly run multi model security reviews and while opus 4.6 7 8 and gpt 5.3 4 5 find a couple of things and declare mission accomplished (running inside pi) kimi k2.6 7 inside pi finds more issues and inside kimi code finds the most. There are sometimes false positives but when I give Kimi’s report to the frontier models they more often than not confirm they a…”
dools · Hacker News · Jun 25, 2026 - 3
“Scores 84.9% on the Aider polyglot coding benchmark (#2 of 27), which tests editing real code across many languages.”
Aider polyglot benchmark · Benchmark · Jul 21, 2026 - 4
“Scores 83.1% on the Aider polyglot coding benchmark (#3 of 27), which tests editing real code across many languages.”
Aider polyglot benchmark · Benchmark · Jul 21, 2026 - 5
“Scores 81.3% on the Aider polyglot coding benchmark (#4 of 27), which tests editing real code across many languages.”
Aider polyglot benchmark · Benchmark · Jul 21, 2026 - 6
“I unleashed it on a compiler codebase that I've been developing for several months now using Claude Sonnet 4.5 6, Gemini 3.1 Pro, DeepSeek V4 Pro(recent), and a bit of Qwen3.6-27B. Right away Fable found several longstanding bugs in our compiler that we hadn't found before. It found that there was a critical part of our design that needed to be mostly redesigned rewritten and gave a very well-reasoned rationale for doing so.”
UncleOxidant · Hacker News · Jun 12, 2026 - 7
“I wish it were otherwise but antigravity is also a distant third behind codex cli app, and claude code. 3.1 pro is just fundamentally not on the same level. In any context I've tried it in, for code review it acts like a model from 1yr ago in that it's all hallucinated superficial bullshit. Claude code is significantly less likely to produce the same (yet still does a decent amount). Gpt 5.4 high xhigh is on another level altogether - truly not comparable to Gemini.”
virgildotcodes · Hacker News · Apr 22, 2026 - 8
“I was a bit rough, agreed, but the overall point is still correct, I kinda want to emphasize that I've also ran hundred of loops recently (combination of opus-4.6 gpt-5.4 gemini-3.1-pro-preview) toward a Rust codebase that we manage and that we deemed secure after many audits and found 2 serious issues as well in it, this was also audited externally by a third party that we've paid, which makes me genuinely scared of releasing anything without deep AI verification nowadays. Anybody has the same…”
pixel_popping · Hacker News · Apr 29, 2026 - 9
“> One of the operational headaches we didn’t predict was that large, advanced models like Claude Opus 4.7 or GPT-5.4 can sometimes spend quite a while thinking through a problem, and to our users this can make it look exactly like a hung job. I had the same problem in my recursive agent harness. It would always come back, but it could sometimes take up to 10 minutes depending. I fixed this by adding a required "purpose" argument to every tool and call return event. As the recursive evaluation p…”
bob1029 · Hacker News · May 29, 2026 - 10
“Had a very similar experience recently. Built a basic authentication handler for this test just so it wouldn't be in the training data of either model. It had deliberately planted bugs. One was a hardcoded secret, another was a wrap-on-0xFFFFFFFF bug as a result of a malloc(length+1). Qwen 3.6 found both, alongside two other issues I hadn't even considered, and the location of the magic value. GPT-5.4, though, missed the malloc issue (flagging memory exhaustion as the only risk), it missed a se…”
AdamConwayIE · Hacker News · May 11, 2026 - 11
“Scores 44.9% on the Aider polyglot coding benchmark (#17 of 27), which tests editing real code across many languages.”
Aider polyglot benchmark · Benchmark · Jul 21, 2026 - 12
“I hadn't taken a look and indeed submitting bugs into the Firefox bug program looks much more accessible than upstreaming a patch into an open source project. It's true just asking the robots "find bugs" isn't enough but it doesn't take a particularly sophisticate harness to make them work for simple targets. I my tests were primarily using a combination of Opus 4.5 and Gemini 3 Flash running in GitHub Copilot, and the harness was constructed by asking the agent something like: "Assume the pers…”
hedgehog · Hacker News · May 14, 2026
Rankings synthesized from community evidence and open benchmarks. See methodology. Not driven by vendor marketing.