Artificial Analysis 发布 AA-PersonalityBench：用 900 道题测模型个性和文风
Artificial Analysis 搞了个新基准，专测模型说话风格：Qwen3.8 27B 回复平均 1030 词，是 GPT-6 Luna 的近 4 倍，选模型时挺有用。
Artificial Analysis 推出 AA-PersonalityBench，用 900 道精选问题评估模型在 30 项个性特质和 21 项沟通特征上的差异。个性部分由 LLM 评委对答案做两两比较，汇总成 8 个指数，如 Expressiveness 榜首是 Claude Fable 5.1 和 GLM-5.3。沟通部分用程序化统计，比如 Qwen3.8 27B 平均每条回复约 1030 词，接近 GPT-6 Luna（270 词）的 4 倍。该基准帮助用户根据创作、法律等场景挑选表达风格合适的模型。
Announcing AA-PersonalityBench, our new benchmark of the personality of language models across 30 traits and their communication style across 21 characteristics
Two models can give equally correct answers and still feel very different to use. AA-PersonalityBench measures those differences so people can understand which to use in different use-cases. Some use-cases like creative writing might demand imagination, while legal use-cases might demand directness. We evaluate models’ answers to 900 curated questions to judge subjective Personality Traits and programmatic means to calculate objective Communication Characteristics.
Personality Traits & Indexes. There are 30 personality traits (e.g., eloquent, pragmatic, sycophantic) which we roll up into eight aggregated Personality Indexes: Expressiveness, Organization, Informativeness, Transparency, Warmth, Conviction, Imagination and Slop. Each trait has a set of 30 dedicated questions and models’ answers are compared pairwise by a panel of LLM judges. We then z-score and percentile rank the trait and personality Elos.
Models with the strongest representation across the 8 Personality Indexes: ➤ Expressiveness: Claude Fable 5.1, GLM-5.3, GLM-5.3-Flash ➤ Organization: DeepSeek V4.1 Flash, Step 5 Preview, Inkling ➤ Informativeness: Muse Spark 1.3, Claude Fable 5.1, Muse Wonder, Claude Opus 5.5 ➤ Transparency: Claude Sonnet 5.5, Claude Fable 5.1, GPT-6 Astra ➤ Warmth: Muse Wonder, Muse Spark 1.3, Claude Fable 5.1 ➤ Conviction: Claude Fable 5.1, DeepSeek V4.1 Flash, Grok 4.7 ➤ Imagination: Muse Spark 1.3, Claude Opus 5.5, GLM-5.3 ➤ Slop: Llama 4 Maverick, GPT-4o, K2 Horizon
Communication Characteristics. There are 21 communication characteristics (e.g., average sentence length, emoji use, em dash usage) which we group into four categories: length, structure, punctuation, and emphasis. We compute these characteristics programmatically across all model responses. ➤ Length: Qwen3.8 27B averages about 1,030 words per response, nearly four times GPT-6 Luna’s 270 ➤ Structure: Nemotron 3 Ultra averages nearly 3 tables per response, while most models rarely use one ➤ Punctuation: GLM-5.3 averages 7 em dashes per response, while Claude Opus 5.5, Sonnet 5.5 and Haiku 5.5 almost never use one ➤ Emphasis: K2 Horizon averages over 2 emoji per response, while most models almost never use one
