Sonar GPT-5.5 Java Evaluation — June 2026

Sonar ran GPT-5.5 through its LLM evaluation framework, measuring generated Java code against the same static-analysis rules used for human-written codebases. The benchmark covered 4,444 tasks, 10 independent runs at temperature=1.0, and produced over 700,000 lines of code.

This is one of the few independent, language-specific quality evaluations of a frontier model published recently.

Metric	GPT-5.5 value	Interpretation
Functional skill (pass rate)	78.7%	Generated solutions that pass functional tests.
Vulnerability density	75 per mLOC	Security issues per million lines. Sonar calls this a strong profile.
Bug density	520 per mLOC	Total bugs per million lines, including concurrency and resource leaks.
Concurrency/threading bugs	170 per mLOC	Dominant bug category by a wide margin.
Cognitive complexity	151.8 per kLOC	Code is harder for humans to follow than average.
Comment density	2.0% of LOC	Very sparse comments given the output volume.

• Language: Java.
• Runs: 10 independent runs at temperature=1.0, reasoning_effort=medium.
• Analyzer: SonarQube systematic code analysis.
• Metrics reported per 1,000 lines (kLOC) or per 1,000,000 lines (mLOC).

• Security is the headline strength. Vulnerability density is low and the severity distribution is flat, meaning GPT-5.5 is not just avoiding trivial issues.
• Concurrency is the weak spot. Threading bugs occur at 170 per mLOC — substantially higher than any other bug category.
• Volume creates verification debt. 700K lines with 2% comments and elevated cognitive complexity means review and maintenance cost shift onto the team.
• One in five tasks still fails functional tests despite a high completion rate. Testing and review remain mandatory.

• Single-language evaluation (Java). Results may not transfer to Python, TypeScript, C++, or other languages.
• Code-quality metrics are not correctness metrics. A clean scan does not mean the code does what the user intended.
• The evaluation used synthetic tasks, not production legacy codebases.

• Use GPT-5.5 for security-sensitive Java code — its security profile is credible.
• Add concurrency-focused testing — threading bugs are under-detected by review alone.
• Budget for verification — the model generates faster than an unaided team can validate. Tooling and automation are what close the gap.

• Sonar source blog: https://www.sonarsource.com/blog/openai-gpt-5-5-evaluation/
• Sonar LLM Leaderboard: https://www.sonarsource.com/the-coding-personalities-of-leading-llms/leaderboard/