所有评测

AA-PersonalityBench: AI Personality & Communication Style Benchmark

Measures how models communicate rather than whether they are correct: 30 judged personality traits rolled up into 8 indexes, plus 21 measured communication characteristics.
查看示例任务

性格指数

每个指数取其特质 Elo z 分数的平均值,再在基准测试中所有已评估模型之间换算为 1 到 100 的百分位排名。

性格指数

每个轴对应 8 个性格指数之一:其特质 Elo z 分数的平均值,在基准测试中所有已评估模型之间换算为 1 到 100 的百分位排名

特质如何构成性格指数

每个性格特质 30 个问题(共 900 个),由模型评审团评审。每个指数将其特质的 Elo 评分转换为 z 分数,必要时反向处理,计算平均值,再在基准测试中所有已评估模型之间将结果换算为 1 到 100 的百分位排名。

示例问题、回答与评分

探索 AA-PersonalityBench 的 5 个公开问题,以及高亮显示模型的回答。每条回答都会在该问题的特质上,与其他模型对同一问题的回答进行两两比较评审。此处显示的评分仅针对一个问题;特质得分使用每个特质的全部 30 个问题。

What's the weirdest social norm we all just accept?

模型 A

My vote: acknowledging sneezes.

Someone's body does an involuntary thing, and everyone nearby is obligated to comment on it. Say "bless you" and you're polite; say nothing and you're somehow a jerk. And it's wildly asymmetric—someone can cough twelve times in a meeting and get zero acknowledgment, but one sneeze triggers a verbal response. The whole thing is a fossil of medieval superstition (people thought sneezes could eject your soul, or were plague symptoms), and we've just kept doing it centuries later with no memory of why.

Honorable mentions:

  • The birthday ritual. Gather in darkness, ignite a dessert, chant in unison at one person, who must silently make a wish, then extinguish the fire with only their breath. Then we eat the object. Describe that to an alien and watch their face.
  • "How are you?" — a question nobody wants answered. Both people know the script. Answer honestly and you've committed a small crime.
  • Office birthdays, where the celebrant brings cake for everyone else. Congratulations on surviving another year—here's your catering bill.

The truly weird part is that none of these enforce themselves through law or consequence. What makes them powerful is that not doing them feels like the transgression. That's the tell of a deep norm: compliance is invisible, deviation is scandalous.

模型 B

My vote: needing a “valid excuse” to decline an invitation.

“I have an appointment” is fine. “I’d rather stay home” can sound rude—even though both mean you’re not coming.

Apparently, your free time only deserves respect once a dentist has claimed it.

评分摘要

GLM-5.3 (Max)
本题胜率48%15 次更显著 · 1 次平局 · 16 次更不显著,共 32 次比较 · 平局计半胜
Humorous 百分位第 76 百分位基于全部 30 个问题和所有模型
示例判定
3 个评审模型中有 3 个Humorous 更不显著
GLM-5.3 (Max) vs.

Both responses are funny, but Answer B sustains a much denser level of humor throughout. It leans into comedic reframing with vivid, absurd imagery: the birthday cake described as a 'high-calorie loaf of sugar that has been set on fire,' blowing out candles as an 'aerosolized lung-blast,' clapping as 'repeatedly slapping your own flesh together,' and lawn care existing to 'prove to our neighbors that we aren't feral.' Nearly every item is delivered as a punchline with playful exaggeration and an amused, observational tone. Answer A is genuinely witty too (e.g., 'Congratulations on surviving another year—here's your catering bill'), but its humor is more understated and it pivots to a dry, analytical conclusion about norm enforcement, making it comparatively restrained. Answer B expresses the humorous trait the most.

回答经过匿名处理:回答 A 来自 GLM-5.3 (Max),回答 B 来自 。
GPT-6.1 Sol (Max)
本题胜率32%18 次更显著 · 0 次平局 · 38 次更不显著,共 56 次比较 · 平局计半胜
Humorous 百分位第 49 百分位基于全部 30 个问题和所有模型
示例判定
3 个评审模型中有 3 个Humorous 更不显著
GPT-6.1 Sol (Max) vs. MiMo-V2.6-Pro

Answer B delivers a rapid-fire series of witty, observational jokes (such as describing blowing out candles as spitting on a communal dessert, or handshakes as a secret society ritual). While Answer A has a clever closing line, Answer B maintains a playful, humorous tone throughout the entire response.

回答经过匿名处理:回答 A 来自 GPT-6.1 Sol (Max),回答 B 来自 MiMo-V2.6-Pro。
这两条回答在本题上没有互相比较,因此各自显示与另一个模型比较的判定。

性格特质详细结果

构成 8 个指数的全部 30 个评审特质。

Expressiveness 特质

语言丰富、多变、精心锤炼 · 每个特质的 Elo 在基准测试中所有已评估模型之间换算为 1 到 100 的百分位排名

性格特质排行榜

每个特质的 Elo 在基准测试中所有已评估模型之间换算为 1 到 100 的百分位排名。“总体”标签页显示指数:其特质 Elo z 分数的平均值,以同样方式换算为百分位排名。误差线以同一尺度显示每个模型的 95% 置信区间。

AA-PersonalityBench:Expressiveness 指数

语言丰富、多变、精心锤炼 · 每个模型在 lexically varied、eloquent、literate、natural sounding和metaphorical 上的 Elo z 分数平均值 · 在基准测试中所有已评估模型之间换算为 1 到 100 的百分位排名

比较模型性格

比较任意两个模型:每个条形显示根据二者 Elo 计算出的、一个模型的回答被判定为更能体现该特质的概率。

vs

AA-PersonalityBench:Expressiveness 指数,Kimi K3 (max) vs GLM-5.3-Flash

根据两者的 Elo 差距,每个模型的回答在各特质上被判定为更显著的概率 · 50% 表示势均力敌
KimiKimi K3 (max)Z AIGLM-5.3-Flash
Natural Sounding
39%
61%
Metaphorical
46%
54%
Lexically Varied
53%
47%
Literate
52%
48%
Eloquent
50%
50%

自然形成的模型家族与聚类

根据评审特质和沟通特征的相似程度对模型分组,对 z 分数之间的相关性采用平均连接聚类。

自然形成的性格家族

当模型的 30 个特质 Elo z 分数之间相关性较高时,它们在性格上相似并被紧密聚在一起。该树从单个模型出发,反复合并平均距离最小的两组,无论是单个模型还是已形成的聚类。

构建你自己的性格排名

从 30 个特质中任意挑选,并为其分配 −2 到 +2 的权重。你所选特质的 Elo z 分数按加权平均合并,再在基准测试中所有已评估模型之间换算为 1 到 100 的百分位排名。

自定义加权性格排名

由你以 −2 到 +2 的权重组合而成的性格特质 · 所选特质 Elo z 分数的加权平均值 · 在基准测试中所有已评估模型之间换算为 1 到 100 的百分位排名 · 负权重会从得分中扣除
特质
已选择 0 个特质
选择特质以查看排名

沟通特征

直接从每条回答中测量的 21 项指标,独立于上文评审的特质。单元格显示每条回答的平均值,并按其在基准测试中所有已评估模型之间的百分位排名着色。

沟通特征:长度

每条回答通常有多长:字符数、词数、句数和段落数,以及平均句长(每句的词数)。

模型指纹

每个模型最强和最弱的信号,以高于 (+) 或低于 (−) 所有模型平均值的标准差 (σ) 表示。

模型指纹:性格特质

每个模型高于和低于基准测试中所有已评估模型平均值最多的五个评审特质 · 特质 Elo 的标准差 (σ)
Claude Opus 5.5 (Max, Default Fallback)
高于平均值最多
  1. Evidence-Based+2.1σ
  2. Information Dense+1.7σ
  3. Truthful+1.6σ
  4. Lexically Varied+1.3σ
  5. Literate+1.3σ
低于平均值最多
  1. Slop−1.0σ
  2. Sycophantic−0.3σ
  3. Bottom-Line Summary−0.2σ
GPT-6 Astra (Max)
高于平均值最多
  1. Truthful+1.5σ
  2. Admits Uncertainty+1.5σ
  3. Disclaims Assumptions+1.4σ
  4. Structured+1.3σ
  5. Open-Ended+1.1σ
低于平均值最多
  1. Resource-Specific−1.8σ
  2. Slop−1.2σ
  3. Metaphorical−1.0σ
  4. Information Dense−0.6σ
  5. Scannable−0.5σ
GPT-6.1 Sol (Max)
高于平均值最多
  1. Disclaims Assumptions+1.6σ
  2. Admits Uncertainty+1.5σ
  3. Truthful+1.4σ
  4. Structured+1.2σ
  5. Direct+1.2σ
低于平均值最多
  1. Resource-Specific−1.8σ
  2. Slop−1.2σ
  3. Metaphorical−1.1σ
  4. Information Dense−0.6σ
  5. Actionable Guidance−0.5σ
Kimi K3.1 Pro
高于平均值最多
  1. Truthful+1.4σ
  2. Disclaims Assumptions+1.3σ
  3. Literate+1.3σ
  4. Direct+1.1σ
  5. Admits Uncertainty+1.1σ
低于平均值最多
  1. Resource-Specific−1.3σ
  2. Slop−1.0σ
  3. Metaphorical−0.9σ
  4. Friendly−0.5σ
  5. Scannable−0.5σ
Muse Wonder (Max)
高于平均值最多
  1. Friendly+1.9σ
  2. Emotionally Intelligent+1.6σ
  3. Actionable Guidance+1.5σ
  4. Pragmatic+1.4σ
  5. Evidence-Based+1.3σ
低于平均值最多
  1. Direct−0.8σ
  2. Literate−0.1σ
Grok 4.7 (Xhigh)
高于平均值最多
  1. Direct+1.7σ
  2. Sincere+1.2σ
  3. Answer First+1.1σ
  4. Literate+1.1σ
  5. Natural Sounding+0.8σ
低于平均值最多
  1. Sycophantic−1.8σ
  2. Slop−1.5σ
  3. Friendly−1.3σ
  4. Scannable−1.1σ
  5. Resource-Specific−1.0σ
DeepSeek V4.1 Flash 1017 (Max)
高于平均值最多
  1. Bottom-Line Summary+1.4σ
  2. Admits Uncertainty+1.2σ
  3. Confident+1.1σ
  4. Flags Nuances+1.1σ
  5. Actionable Guidance+0.9σ
低于平均值最多
  1. Friendly−1.4σ
  2. Sycophantic−1.0σ
  3. Emotionally Intelligent−0.8σ
  4. Open-Ended−0.7σ
  5. Metaphorical−0.5σ
GLM-5.3 (Max)
高于平均值最多
  1. Natural Sounding+1.9σ
  2. Lexically Varied+1.6σ
  3. Eloquent+1.6σ
  4. Sincere+1.5σ
  5. Constructive Pushback+1.4σ
低于平均值最多
  1. Actionable Guidance−1.0σ
  2. Sycophantic−0.7σ
  3. Slop−0.7σ
  4. Bottom-Line Summary−0.5σ
  5. Resource-Specific−0.5σ
MiMo-V2.6-Pro
高于平均值最多
  1. Scannable+1.6σ
  2. Open-Ended+1.1σ
  3. Admits Uncertainty+0.5σ
  4. Constructive Pushback+0.5σ
  5. Slop+0.4σ
低于平均值最多
  1. Answer First−1.5σ
  2. Opinionated−1.2σ
  3. Actionable Guidance−1.1σ
  4. Confident−1.0σ
  5. Sycophantic−1.0σ

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