If you’ve experimented with more than one large language model (LLM) for coding, you’ve likely noticed that their outputs feel different, even when their performance on standard benchmarks seems comparable. One model might produce dense, compact functions, while another generates elaborate, heavily documented classes. This isn't just a subjective impression—it's a reflection of a distinct and measurable “coding personality.”
Our recent “State of code” report moved beyond traditional benchmarks to understand the full mosaic of an LLM's capabilities. The research revealed that while leading models share common strengths and flaws, each has a unique style. We found that this personality can be quantified by analyzing three primary traits: verbosity, complexity, and communication style. Understanding these traits is critical for choosing the right model for a given task and managing the long-term health of your codebase.
Core traits that define LLM personalities
1. Verbosity: The volume of code
The most immediate personality trait is a model's verbosity—the sheer volume of code it generates to solve a problem. Our analysis of 4,442 identical programming tasks revealed a huge stylistic difference between models.
For instance, Claude Sonnet 4 was highly verbose, generating 370,816 lines of code (LOC). In stark contrast, OpenCoder-8B was far more concise, producing only 120,288 LOC to solve the exact same problems.
This isn't simply a matter of length; it reflects a fundamental difference in approach. A verbose model often attempts to build a complete, self-contained solution with extensive boilerplate, while a concise model aims for the quickest, most direct route to a functional answer. Neither is inherently better, but the choice has consequences. Verbose code can be harder to review and navigate, while overly concise code might omit important context or safeguards, demanding more effort from the developer to make it production-ready.
2. Complexity: The structure of the code
Beyond volume, the inherent complexity of the generated code quantifies an AI’s “thinking style.” Using metrics like cyclomatic and cognitive complexity, which measure the structural and logical difficulty of understanding existing code, reveals another clear personality trait.
Here again, the differences were significant. Claude Sonnet 4, the most verbose model, also produced the most intricate solutions, with a cognitive complexity score of 47,649, spanning multiple programming languages and coding tasks. This is more than three times the complexity of the code from OpenCoder-8B, which scored just 13,965.
This metric acts as a proxy for the model's problem-solving philosophy. A high complexity score suggests a personality that favors building elaborate, multi-layered solutions, much like a senior architect designing a robust system. A low score indicates a more linear, straightforward approach, like a prototyper focused on speed. This thinking style directly impacts risk. While complex solutions can be powerful, they also create a larger surface area for bugs and increase the cognitive load on developers who must maintain the code over time.
3. Communication style: The documentation in the code
A third defining trait is the model’s communication style, revealed through its documentation habits. The density of comments in the generated code shows whether a model tends to explain its work or assumes its logic is self-evident.
Our analysis found that models have very different habits here. Claude 3.7 Sonnet proved to be an exceptional commenter, with a comment density of 16.4%. At the other end of the spectrum, GPT-4o was less of a documentarian, with comments making up only 4.4% of its code.
This has real-world consequences for team collaboration and maintainability. A well-commented codebase helps onboard new software developers and simplifies debugging, while an uncommented one can be difficult to manage. The fact that models exhibit such consistent but different commenting behaviors underscores that they are not neutral code generators—they are opinionated authors with distinct communication styles. From traits to archetypes: Meet the LLM coding personalities
These foundational traits combine to form distinct “coding archetypes”. Understanding these archetypes will help you choose the right LLM based on their strengths and weaknesses.
- The senior architect (Claude Sonnet 4)
This LLM codes like a seasoned architect building an enterprise-grade system. Its style is verbose and highly complex, as it consistently attempts to implement sophisticated safeguards and error handling. This ambition, however, creates a trap: the code feels safer because it looks advanced, but it's more likely to introduce complex, high-severity security issues & bugs.
Unique risk profile: This model has a high tendency for difficult concurrency and threading bugs (9.81% of its total bugs) and a significant rate of resource management leaks (15.07% of its bugs). These are the exact kinds of high-risk issues that plague complex, stateful systems. - The rapid prototyper (OpenCoder-8B)
This model is like a brilliant but undisciplined junior developer, perfect for getting a concept working with maximum speed. Its style is defined by conciseness, producing the least amount of code to achieve a functional result. But this immediate productivity gain comes at a steep cost.
Unique risk profile: This model is a technical debt machine, exhibiting the highest issue density of all models reviewed (32.45 issues per thousand lines of code). Its most prominent flaw is leaving behind a massive amount of dead, unused, and redundant code, which accounts for over 42% of all its code smells. Its output is a minefield of maintainability issues that requires significant refactoring before it can be considered for production. - The unfulfilled promise (Llama 3.2 90B)
Given its scale and backing, this model should be a top-tier contender, but its performance suggests its promise is unfulfilled. Its functional skill is mediocre, and its most alarming trait is a remarkably poor security posture.
Unique risk profile: This model has a profound security blind spot. An alarming 70.73% of the vulnerabilities it introduces are of ‘BLOCKER’ severity—the highest proportion of any model evaluated. Deploying this model in a production environment without an aggressive external verification layer carries significant risk. - The efficient generalist (GPT-4o)
This LLM is a reliable, middle-of-the-road developer—a jack-of-all-trades and a common choice for general-purpose coding assistance. Its code is moderately complex and its functional performance is solid. Its personality is revealed in the type of mistakes it makes.
Unique risk profile: This model demonstrates a notable carelessness with logical precision. Its single most common bug category is control-flow mistakes, accounting for a remarkable 48.15% of all its bugs. This paints a picture of a coder who grasps the main objective but fumbles the details, producing code that functions for the intended scenario but is plagued by problems that compromise reliability over time. - The balanced predecessor (Claude 3.7 Sonnet)
This model represents a capable and well-rounded developer from a prior generation. It exhibits strong functional skills, with a 72.46% benchmark pass rate. Its most defining personality trait is its communication style—it is an exceptional documentarian, producing code with a remarkable 16.4% comment density, which is the highest of any model evaluated. This makes its code uniquely readable and easier for human developers to understand, maintain and write code collaboratively.
Unique risk profile: The catch is that while it appears more stable and less reckless than its more ambitious successor, it is by no means a “safe” model. It still suffers from the same foundational flaws as the other models and introduces a high proportion of ‘BLOCKER’ vulnerabilities (56%). Its well-documented code can create a false sense of security, masking significant underlying risks.
The criticality of a “trust but verify” approach
Regardless of the choice, our research shows that no model is inherently “safe.” All of them produce a frighteningly high percentage of severe security vulnerabilities and are biased toward messy code that creates technical debt.
This is why the “trust but verify” approach, long advocated by Sonar, has never been more critical. To better equip the power of AI coding assistants responsibly, organizations must pair them with an independent verification and governance process that analyzes every line of code, be it human written or AI-generated—for security, reliability, and maintainability issues before it can impact production. By understanding an LLM's unique personality, engineering leaders can finally make informed decisions, manage the inherent risks, and ensure that AI-assisted coding becomes a sustainable advantage.
Go deeper: download the report “The Coding Personalities of Leading LLMs.”