Mike Vizard explores Sonar’s findings on ChatGPT-5, describing how improvements in AI-powered code generation affect cost, vulnerability profiles, and developer maintenance in modern software teams.

Report: ChatGPT-5 Coding Gains Come at a Higher Cost

Author: Mike Vizard

A recent Sonar report investigates the improvements and trade-offs introduced by OpenAI’s ChatGPT-5 coding models. The report evaluates more than 4,400 Java tasks and compares four reasoning levels available in GPT-5, highlighting changes in code quality, security, and productivity from earlier models like GPT-4o.

Key Findings

Improved Code Quality at a Cost:
- Higher reasoning levels in ChatGPT-5 yield better code quality, particularly in reducing certain vulnerabilities and bugs.
- However, higher reasoning settings drive up subscription cost (up to $189 per developer/month versus $22 for minimal capability).
Code Volume and Maintenance:
- Advanced reasoning models more than double the lines of code generated per task compared to previous versions.
- Increased code volume introduces additional maintenance challenges, especially for developers unfamiliar with autogenerated code patterns.
Security Trade-Offs:
- While common vulnerabilities like path-traversal and injection flaws decrease, subtle issues (e.g., inadequate I/O error-handling) become more prevalent (44% in high reasoning, 30% in minimal reasoning).
- Advanced modes shift bug profiles from basic control-flow mistakes to more complex issues like concurrency bugs (20% in minimal mode, up to 38% in high mode).
Cost-Benefit Analysis:
- Teams should balance productivity benefits against higher subscription costs and the new challenges posed by increased code complexity.
- Quality assurance remains essential, as even sophisticated AI tools produce both obvious and subtle security and logic flaws.

Industry Context and Recommendations

DevOps Implications:
- The report encourages DevOps teams to actively verify AI-generated output, especially in production environments, and budget for increased operational costs tied to advanced features.
- Adoption rates of advanced AI coding tools remain unclear, as does the percentage of autogenerated code making it to production.
Comparative Model Insights:
- Previous Sonar research found coding “personalities” and security flaw tendencies varied across LLMs from OpenAI, Anthropic, and Meta.
- AI models demonstrate strong cross-language code translation and algorithmic problem-solving, but hard-coded credentials and severe vulnerabilities are still common.

Overall, while ChatGPT-5 marks a step forward in AI-assisted development, teams must stay vigilant about costs, maintenance, and code quality when integrating these tools into their workflows.

Further Reading:

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