Examining the Coding Styles of Prominent Legal Language Models - Findings from the Sonar State of Code Study

Examining the Coding Styles of Prominent Legal Language Models - Findings from the Sonar State of Code Study

In August 2025, Sonar, a leading software development company, released The Coding Personalities of Leading LLMs - A State of Code Report. This groundbreaking study analysed the coding personalities of top Large Language Models (LLMs), including Claude Sonnet 4, Claude 3.7 Sonnet, GPT-4o, Llama 3.2 90B, and OpenCoder-8B.

Understanding the Coding Personalities

Each LLM has a distinct "coding personality," which refers to its unique strengths, weaknesses, and coding habits. These personalities are crucial for developers to understand so they can effectively integrate AI into their development processes.

Model Strengths and Weaknesses

1. Claude Sonnet 4
2. Strengths: Tops the list with a 95.57% HumanEval score and a weighted Pass@1 rate of 77.04%, indicating high reliability in its first attempts.
3. Weaknesses: Despite its high scores, the report does not specify any notable weaknesses.
4. Claude 3.7 Sonnet
5. Strengths: Still performs well with a HumanEval score of 72.46%, showing strong syntactic reliability.
6. Weaknesses: Lower performance compared to Claude Sonnet 4.
7. GPT-4o
8. Strengths: Scored 69.67% on HumanEval, demonstrating good problem-solving capabilities.
9. Weaknesses: Lower success rate compared to the top Claude models.
10. Llama 3.2 90B
11. Strengths: Reasoning through problems rather than memorizing syntax, showing versatility.
12. Weaknesses: Lower HumanEval score of 61.47%, indicating more room for improvement.
13. OpenCoder-8B
14. Strengths: Demonstrates an ability to handle varied tasks, though specifics are not highlighted.
15. Weaknesses: Lowest HumanEval score of 60.43%, suggesting more errors in its code.

Shared Strengths Across All Models

• Syntactic Reliability: All models demonstrated strong syntactic reliability, meaning their generated code compiled and ran successfully in most cases.
• Reasoning Ability: They are capable of reasoning through problems rather than just memorizing syntax, which allows them to perform well across different programming languages.

Shared Challenges

Common challenges include ensuring security and reliability while maintaining performance across diverse coding tasks. The report highlights the importance of understanding these models' blind spots and leveraging their strengths to integrate AI safely and effectively into software development.

Comparing Claude Sonnet 4 to Claude 3.7, although Sonnet 4 improved its pass rate, the percentage of its bugs rated as blocker nearly doubled, and blocker-level vulnerabilities rose. GPT-4o was called "The Efficient Generalist," balancing functionality and complexity but often tripping over control-flow errors.

Llama 3.2 90B was referred to as "The Unfulfilled Promise," delivering moderate results but having the worst security posture. OpenCoder-8B, labeled "The Rapid Prototyper," produced short, focused code with the highest issue density. Claude Sonnet 4, named "The Senior Architect," wrote the most verbose code with high cognitive complexity, prone to sophisticated bugs like resource leaks and concurrency errors.

The report emphasizes that benchmark accuracy is not the only factor; understanding security risks, maintainability, and coding style is equally important. The assessment was conducted on more than 4,400 Java assignments.

In conclusion, the report underscores the value of understanding these distinct coding personalities to optimize AI integration in development processes. It provides invaluable insights for developers, organizations, and AI enthusiasts alike, paving the way for safer and more effective integration of AI in software development.

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