The Personality of Open Source: How Llama, Mistral, and Qwen Compare to Frontier (www.lindr.io)

A recent analysis comparing the personalities of various large language models (LLMs) revealed significant differences between frontier models like Claude and GPT-5.2, while open-weight models such as Llama and Mistral exhibited striking uniformity. The study found that Claude scores notably higher in traits like Openness and Curiosity, making it ideal for creative tasks, whereas GPT-5.2 excels in Conscientiousness, positioning it as a prime choice for structured task execution. Notably, personality differences among open-weight models were negligible, with pairwise comparisons yielding effect sizes below 0.2, indicating they likely converge on a generic personality type influenced by shared training data. The findings emphasize the critical role of prompt selection over model identity, as it accounted for 35% of the variance in personality scores, compared to just 7.7% attributed to the model itself. These insights suggest that developers should focus on how they deploy and evaluate models to achieve desired outcomes rather than solely on the choice of model, as optimal performance can often hinge on the context and design of prompts. Overall, this research underscores the evolving nature of LLM personalities, inviting further inquiry into the implications of prompt design and training methodologies within the AI/ML community.