When it comes to choosing a large language model (LLM) for software development, performance benchmarks only tell half the story. But selecting the right LLM for your engineering organisation requires code quality, maintainability, and consistency all play critical roles in determining long-term productivity and risk.
In this exclusive session, Prasenjit Sarkar, Marketing Manager at Sonar presents the results of the latest qualitative analysis of six leading LLMs - including GPT-5, GPT-4, Claude Sonnet 4, Claude Sonnet 3.7, Meta Llama and OpenCoder 8B.
The detailed examination of generated code samples reveal how each model demonstrates distinct “coding personalities,” reflecting varying priorities in structure, security, and problem-solving.
You’ll walk away with insights that go beyond accuracy scores, helping you decide which model best fits your team’s coding culture, velocity, and quality standards.
Key Takeaways:
- Distinct coding personalities: See how each model balances creativity, structure, and speed under real-world coding conditions.
- Strengths and trade-offs: Learn which models produce the most secure and maintainable code, and which may introduce hidden technical debt.
- Insights on code quality: Discover why over 90 percent of identified issues across models were “code smells” that compromise long-term maintainability.
- Practical decision guidance: Identify the LLM that best aligns with your team’s goals, whether that’s reliability, speed, or innovation.
Can an AI write production-ready code or just convincing prototypes?
Find out in this exclusive Sonar webinar.