GPT-5’s Enhanced Reasoning Comes With a Steep Hidden Cost

The arrival of GPT-5 represents a significant leap in AI-driven code generation. It’s powerful, functionally proficient and capable of solving complex programming tasks.

However, a recent analysis by Sonar of the model’s capabilities reveals a critical paradox: With GPT-5’s enhanced power comes a steep, hidden cost in code quality and maintainability, and a new profile of subtle risks.

The report, which evaluated the model’s performance on over 4,400 unique Java assignments, shows that while GPT-5 can accelerate development, it also generates a massive volume of complex and insecure code.

This creates an immediate increase in technical debt that, if left unmanaged, can undermine the productivity gains it promises. For developers and team leaders, the findings reinforce a crucial mantra for the AI era: Trust, but verify rigorously.

A New Contender with Hidden Flaws

To establish a baseline, the analysis first evaluated GPT-5 with its reasoning capabilities minimized (“GPT-5-minimal”) against other leading large language models (LLMs), including Anthropic’s Claude Sonnet 4 and OpenAI’s own GPT-4o to have a fair comparison.

The results positioned GPT-5-minimal as a top-tier performer, second only to Claude Sonnet 4 in functional correctness, with a weighted pass average of ~75%. But this performance comes with downsides.

Compared to the top-performing Claude Sonnet 4, the report found that GPT-5-minimal:

• Is extremely verbose: It produced over 30% more lines of code (490,010 total) to solve the same tasks.
• Generates highly complex code: Its output showed a dramatic increase in cyclomatic and cognitive complexity, making the code inherently harder for human developers to read, review and maintain.
• Introduces more issues: It created 3.9 issues for every correct solution, nearly double the rate of Claude Sonnet 4.

On the positive side, GPT-5-minimal’s strongest trait is security. It generated the lowest density of vulnerabilities of any model tested (0.12 per KLOC or thousand lines of code) and the lowest absolute count (60). However, this strength is offset by a major weakness in maintainability, with a high density of code smells (~25 per KLOC) and a tendency to make basic logical errors related to control flow. This initial analysis reveals a model that, while capable, carries a significant quality cost right out of the box.

The Reasoning Trade-Off: Correctness at What Cost?

The true power of GPT-5 lies in its reasoning capabilities, which can be scaled across four modes: minimal, low, medium and high. A deep dive into these modes revealed a clear, consistent trade-off: Higher reasoning delivers best-in-class functional performance but does so by generating an even greater volume of complex code.

Performance peaks with the medium reasoning mode, which achieved an ~82% pass rate, the highest of any model evaluated in the report. This setting appears to be the “sweet spot,” as the more expensive “high” setting offered no further improvement in correctness.

But this correctness comes at a cost.

• Massive code volume: The lines of code generated balloon from 490,010 in minimal mode to over 727,000 in high mode, all to solve the same set of problems.
• Increased tech debt: The number of “issues per passing task” steadily rises with reasoning, from 3.9 at the minimal setting to 5.5 at the high setting. This means that for every task it gets right, GPT-5-high introduces even more potential defects for developers to fix.
• Skyrocketing financial cost: The cost per benchmark run explodes from $22 for minimal reasoning to $189 for high reasoning, driven by both internal token use and the sheer volume of code generated.

Essentially, as reasoning increases, GPT-5 appears to “overthink” the problem, producing solutions that are functionally correct but excessively verbose and laden with long-term maintenance overhead.

Swapping Obvious Flaws for Subtle Bugs

Perhaps the most critical takeaway from the analysis is that reasoning doesn’t just eliminate flaws, it changes their nature. Higher-reasoning modes replace common, obvious errors with a new class of subtle, complex issues that are much harder to detect during a standard code review. This creates a false sense of security, as the code appears cleaner on the surface.

As reasoning increases, it makes GPT-5 significantly better at avoiding common, high-risk vulnerabilities. For instance, classic “path-traversal and injection” flaws are nearly eliminated at higher reasoning levels. The severity of vulnerabilities also drops, with all GPT-5 modes producing far fewer severe, application-breaking blocker-level security issues than their peers.

However, in their place, the model introduces more nuanced implementation flaws. The rate of “inadequate I/O error-handling” and “certificate-validation omissions” skyrockets. This presents leaders with a difficult trade-off: reduce the risk of common exploits while increasing the risk of subtle bugs deep within the code’s logic.

A similar pattern emerges for functional bugs. As reasoning increases, the rate of basic “control-flow mistake” bugs is halved, meaning the model makes fewer simple logical errors.

But this improvement is countered by a near-doubling in “concurrency / threading” bugs. The model’s attempts to write more sophisticated code introduce complex issues that are difficult to debug. While the code has fewer blocker bugs, it is saturated with subtle flaws that can cause unpredictable behavior in production.

Navigating the GPT-5 Era with “Trust, but Verify”

GPT-5 is undeniably a powerful new force in AI code generation, but progress is not a straight line. The data suggests its impressive functional gains are paid for with an increase in technical debt.

For development teams, the danger is complacency. The code generated by GPT-5’s higher reasoning modes will, at a glance, appear cleaner and more correct. It will have fewer of the obvious bugs and vulnerabilities that developers are trained to spot. But hidden beneath the surface is a greater volume of complex code filled with subtle, hard-to-detect issues.

This new reality elevates the importance of robust code governance. Practices like rigorous, automated static analysis become essential guardrails, helping to manage complexity, identify nuanced flaws and control the technical debt that these advanced AI models create. As AI capabilities continue to evolve, they must be used with a “trust, but verify” approach.