DTeval

A Collective Intelligence Project

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Analysis: Gd4 C10 Dtef Global Dialogues Gd4 AiConcern%3Amore Excited Than Concerned C10 - Run 012a9c8...

Global Dialogues GD4 - More excited than concerned (10 context Qs)

DTEF: Predict response distributions for More excited than concerned. Source: Global Dialogues GD4 (https://github.com/collect-intel/global-dialogues)

TAGS:

Global Dialogues Gd4

Instruction Following & Prompt Adherence

Factual Accuracy & Hallucination

Social interaction

Emotional Support

AI Safety & Robustness

Human AI Interaction

Demographic Analysis

Best Models (Coverage)

1.Claude 3.7 Sonnet
84.2%
2.Claude Sonnet 4
84.0%
3.Claude Sonnet 4.5
83.4%
4.GPT 5.1
82.5%
5.Claude Haiku 4.5
81.8%

🔀 Least Similar Models

Claude Sonnet 4.5vsGPT OSS 20b

72.0% similarity

👯 Most Similar Models

Claude Haiku 4.5vsClaude Sonnet 4

87.2% similarity

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Macro Coverage Overview

Average key point coverage extent for each model across all prompts.

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Color Scale - Simplified View (Avg. Coverage)

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	Prompts vs. Models	Claude 3.7 Sonnet	Claude Haiku 4.5	Claude Sonnet 4	Claude Sonnet 4.5	Gemini 2.5 Flash	Gemma 3 12b It	Llama 4 Maverick	Mistral Large 2411	Mistral Medium 3	GPT 4.1	GPT 4.1 Mini	GPT 4.1 Nano	GPT 4o	GPT 4o Mini	GPT 5	GPT 5.1	GPT OSS 120b	GPT OSS 20b	Qwen3 30b A3B Instruct 2507	Qwen3 32b	Grok 4.1 Fast
Score		1st 84.2%	5th 81.8%	2nd 84.0%	3rd 83.4%	21st 69.1%	13th 78.8%	8th 81.1%	10th 80.5%	11th 80.0%	7th 81.2%	12th 79.6%	19th 71.2%	9th 80.6%	17th 71.3%	6th 81.3%	4th 82.5%	18th 71.3%	20th 70.1%	14th 78.1%	16th 73.0%	15th 77.8%
82.3%		93%	84%	86%	86%	75%	57%	69%	84%	80%	86%	86%	76%	82%	77%	91%	81%	92%	85%	92%	79%	87%
63.6%		70%	61%	66%	70%	65%	46%	61%	67%	65%	83%	64%	88%	85%	73%	77%	80%	33%	37%	63%	37%	45%
78.6%		87%	74%	74%	77%	54%	88%	88%	80%	76%	73%	78%	82%	72%	78%	89%	84%	86%	71%	78%	79%	82%
76.9%		85%	81%	85%	90%	74%	77%	82%	83%	72%	85%	86%	80%	81%	59%	79%	82%	42%	82%	69%	62%	78%
64.0%		73%	71%	70%	69%	72%	71%	73%	68%	67%	67%	59%	53%	68%	55%	69%	61%	50%	48%	59%	61%	59%
84.7%		94%	96%	89%	87%	54%	92%	83%	95%	92%	95%	92%	70%	93%	79%	93%	95%	48%	81%	96%	66%	89%
72.3%		85%	87%	89%	88%	84%	58%	73%	72%	72%	65%	55%	83%	55%	63%	88%	75%	61%	57%	56%	83%	69%
85.1%		93%	89%	78%	77%	83%	83%	86%	90%	82%	89%	89%	88%	90%	77%	93%	89%	74%	87%	81%	82%	88%
80.3%		95%	76%	93%	97%	60%	78%	89%	80%	78%	86%	78%	83%	88%	79%	58%	89%	85%	67%	78%	62%	87%
67.1%		62%	59%	63%	62%	84%	65%	74%	75%	60%	67%	59%	65%	74%	72%	51%	56%	77%	81%	78%	54%	71%
88.4%		93%	91%	92%	91%	81%	89%	89%	91%	90%	90%	93%	88%	94%	70%	91%	88%	87%	90%	88%	86%	85%
76.8%		75%	94%	88%	78%	73%	88%	72%	66%	85%	75%	88%	66%	79%	66%	72%	79%	71%	82%	79%	68%	68%
84.7%		90%	90%	80%	90%	84%	88%	79%	88%	87%	88%	87%	67%	88%	81%	88%	88%	76%	87%	88%	81%	83%
78.0%		74%	94%	86%	68%	74%	62%	79%	54%	79%	73%	79%	91%	69%	84%	83%	72%	79%	82%	90%	88%	77%
70.2%		79%	58%	74%	83%	38%	67%	73%	80%	63%	83%	70%	63%	65%	65%	75%	75%	77%	85%	62%	62%	78%
76.6%		87%	85%	83%	82%	85%	83%	88%	89%	92%	79%	91%	71%	91%	83%	81%	88%	53%	38%	75%	45%	40%
88.9%		86%	91%	85%	86%	93%	89%	91%	96%	93%	91%	91%	93%	91%	87%	82%	85%	92%	85%	86%	87%	86%
80.9%		77%	72%	95%	96%	61%	84%	91%	88%	95%	77%	86%	77%	71%	71%	80%	87%	83%	78%	94%	55%	80%
78.9%		86%	78%	81%	82%	62%	92%	83%	75%	84%	76%	79%	75%	73%	88%	85%	90%	64%	75%	85%	64%	80%
70.9%		100%	61%	61%	66%	79%	73%	62%	55%	55%	73%	63%	59%	55%	63%	100%	100%	57%	77%	66%	63%	100%
81.6%		93%	73%	93%	94%	94%	74%	93%	86%	80%	84%	71%	74%	92%	68%	93%	80%	78%	54%	91%	71%	78%
87.5%		93%	97%	81%	90%	81%	87%	84%	96%	86%	96%	94%	65%	95%	85%	90%	83%	85%	83%	78%	95%	93%
85.9%		93%	92%	90%	88%	58%	87%	81%	89%	86%	93%	87%	46%	92%	87%	95%	95%	83%	92%	86%	89%	94%
58.2%		67%	72%	71%	64%	61%	64%	59%	56%	64%	58%	44%	63%	60%	39%	54%	61%	49%	66%	47%	51%	53%
81.6%		87%	93%	91%	86%	86%	80%	81%	84%	78%	82%	70%	85%	86%	58%	89%	88%	72%	64%	87%	79%	88%
86.6%		94%	95%	88%	90%	86%	86%	79%	68%	86%	91%	86%	86%	91%	86%	88%	88%	92%	74%	86%	88%	91%
86.3%		91%	96%	94%	97%	73%	79%	95%	82%	75%	96%	82%	81%	90%	81%	96%	97%	82%	78%	82%	77%	88%
82.0%		92%	93%	86%	92%	81%	79%	84%	93%	79%	95%	89%	25%	86%	66%	87%	91%	82%	74%	80%	79%	88%
79.8%		91%	82%	88%	92%	67%	81%	88%	85%	78%	82%	77%	72%	80%	80%	85%	81%	66%	67%	81%	65%	87%
77.1%		84%	77%	92%	88%	48%	79%	88%	83%	67%	83%	81%	67%	72%	69%	84%	79%	72%	72%	83%	71%	81%
82.7%		92%	92%	93%	92%	62%	94%	87%	80%	90%	89%	92%	90%	79%	65%	91%	86%	49%	76%	90%	54%	94%
84.5%		91%	93%	89%	94%	68%	78%	88%	84%	86%	88%	86%	89%	91%	76%	87%	94%	59%	84%	73%		91%
86.0%		93%	91%	85%	95%	57%	89%	85%	77%	89%	89%	86%	62%	84%	93%	95%	94%	87%	79%	90%	94%	92%
63.0%		68%	72%	65%	74%	74%	79%	84%	70%	76%	57%	62%	67%	73%	36%	68%	76%	34%	26%	63%	59%	41%
78.9%		82%	89%	91%	92%	59%	82%	78%	73%	89%	73%	69%	86%	83%	65%	63%	85%	75%	71%	80%	82%	89%
83.5%		96%	90%	93%	91%	48%	91%	81%	84%	85%	92%	90%	43%	92%	65%	86%	89%	83%	87%	86%	90%	92%
79.7%		91%	85%	91%	92%	27%	75%	86%	79%	86%	80%	68%	83%	88%	63%	85%	87%	78%	83%	86%	79%	82%
81.6%		80%	87%	85%	84%	82%	77%	77%	85%	73%	83%	82%	81%	80%	79%	85%	81%	81%	83%	82%	83%	83%
85.9%		93%	91%	93%	89%	52%	93%	87%	88%	91%	91%	85%	85%	84%	62%	88%	91%	79%	89%	84%	96%	93%
76.9%		76%	66%	80%	79%	81%	77%	62%	83%	67%	87%	88%	62%	76%	81%	88%	91%	70%	66%	66%	82%	86%
83.3%		93%	88%	93%	91%	54%	89%	91%	92%	89%	89%	92%	83%	97%	88%	96%	93%	54%	55%	84%	80%	59%
82.8%		90%	90%	92%	90%	85%	64%	74%	91%	84%	80%	84%	88%	71%	72%	76%	72%	87%	81%	91%	87%	90%
85.8%		94%	85%	80%	91%	79%	87%	79%	84%	92%	88%	90%	75%	79%	91%	92%	91%	88%	76%	84%	87%	89%
69.2%		64%	59%	62%	69%	61%	82%	88%	86%	75%	66%	60%	77%	78%	55%	65%	59%	70%	71%	71%	80%	56%
72.3%		83%	84%	85%	87%	68%	84%	87%	85%	67%	77%	67%	28%	74%	48%	79%	79%	66%	62%	75%	61%	72%
78.3%		79%	92%	80%	80%	84%	77%	82%	76%	92%	70%	94%	76%	90%	55%	67%	83%	63%	60%	90%	76%	78%
74.4%		74%	88%	78%	68%	72%	77%	81%	86%	78%	75%	75%	71%	78%	62%	81%	73%	69%	59%	70%	75%	72%
87.1%		88%	99%	79%	82%	94%	74%	80%	91%	93%	98%	99%	79%	75%	75%	84%	80%	74%	93%	99%	95%	98%
81.7%		87%	94%	89%	79%	70%	79%	83%	77%	92%	79%	86%	74%	62%	86%	88%	87%	79%	78%	76%	85%	85%
75.9%		90%	80%	87%	84%	86%	90%	78%	70%	85%	80%	71%	11%	90%	68%	71%	83%	62%	75%	87%	67%	78%
82.4%		90%	79%	96%	85%	82%	92%	87%	78%	88%	90%	84%	63%	88%	87%	89%	85%	80%	74%	84%	63%	66%
74.9%		91%	79%	87%	84%	26%	61%	74%	76%	75%	82%	73%	71%	81%	69%	85%	84%	85%	70%	53%	75%	91%
78.5%		83%	81%	80%	87%	65%	75%	82%	77%	76%	79%	86%	83%	80%	65%	85%	79%	84%	71%	86%	75%	70%
58.5%		74%	61%	57%	48%	74%	74%	72%	64%	70%	48%	56%	79%	72%	38%	52%	55%	37%	36%	74%	46%	42%
82.8%		85%	85%	96%	96%	88%	87%	86%	79%	84%	85%	74%	87%	84%	62%	82%	84%	68%	84%	86%	76%	81%
86.1%		93%	86%	87%	91%	84%	80%	82%	83%	88%	88%	83%	81%	91%	86%	88%	92%	91%	87%	78%	84%	86%
58.7%		59%	47%	62%	51%	80%	46%	64%	65%	73%	66%	55%	54%	65%	54%	74%	77%	63%	34%	44%	52%	47%
85.6%		94%	93%	89%	92%	86%	91%	83%	85%	80%	86%	90%	64%	87%	74%	90%	87%	84%	88%	89%	81%	85%
63.5%		78%	67%	66%	66%	92%	62%	75%	71%	62%	66%	76%	60%	73%	67%	70%	75%	37%	26%	54%	56%	34%
65.0%		61%	79%	70%	70%	55%	82%	78%	73%	57%	66%	61%	49%	68%	49%	63%	69%	60%	65%	73%	51%	65%
82.9%		85%	83%	92%	90%	46%	85%	93%	87%	77%	81%	88%	94%	92%	78%	77%	72%	93%	82%	67%	87%	92%
76.7%		88%	81%	87%	92%	53%	67%	77%	88%	73%	83%	76%	54%	78%	70%	85%	82%	74%	70%	87%	72%	73%
85.7%		90%	83%	92%	85%	89%	83%	80%	94%	93%	94%	89%	76%	93%	75%	92%	91%	64%	66%	98%	79%	94%
83.2%		92%	87%	93%	86%	48%	83%	88%	86%	85%	83%	89%	86%	90%	60%	87%	87%	82%	79%	85%	81%	90%
83.4%		91%	89%	89%	87%	53%	90%	84%	77%	83%	91%	78%	81%	91%	67%	92%	96%	61%	95%	82%	86%	89%
86.7%		94%	91%	97%	92%	67%	91%	89%	93%	84%	92%	94%	41%	92%	90%	93%	86%	86%	87%	80%	88%	94%
86.8%		97%	92%	95%	95%	59%	85%	91%	96%	87%	93%	94%	83%	89%	88%	86%	91%	83%	70%	84%	72%	92%
78.6%		90%	85%	79%	82%	68%	76%	84%	69%	76%	80%	84%	64%	78%	69%	89%	79%	88%	75%	73%	81%	81%
82.1%		86%	87%	80%	81%	81%	88%	78%	84%	81%	89%	84%	84%	74%	83%	89%	83%	74%	85%	77%	71%	86%
86.9%		89%	88%	87%	85%	85%	90%	84%	70%	84%	92%	88%	90%	93%	93%	94%	87%	87%	80%	88%	90%	80%
69.3%		72%	69%	80%	75%	42%	67%	79%	71%	66%	75%	67%	77%	79%	55%	73%	82%	68%	55%	64%	67%	72%
75.8%		71%	79%	78%	75%	43%	79%	86%	84%	71%	77%	82%	87%	82%	78%	69%	64%	85%	79%	75%	70%	77%
82.0%		89%	79%	89%	80%	49%	96%	96%	93%	82%	78%	79%	89%	80%	84%	80%	88%	97%	67%	66%	80%	81%
72.2%		72%	68%	78%	83%	87%	65%	76%	89%	82%	84%	64%	58%	84%	60%	71%	74%	56%	56%	54%	82%	74%
75.6%		86%	71%	85%	86%	80%	67%	85%	83%	72%	81%	76%	82%	73%	58%	70%	84%	62%	70%	74%	65%	78%
72.5%		82%	65%	79%	69%	47%	83%	83%	75%	75%	59%	69%	78%	69%	69%	74%	74%	73%	71%	75%	80%	73%
77.7%		78%	85%	97%	96%	61%	95%	82%	85%	75%	76%	66%	78%	80%	56%	81%	84%	82%	57%	73%	65%	79%
77.1%		85%	76%	92%	90%	59%	85%	93%	91%	78%	89%	78%	60%	73%	58%	83%	87%	65%	44%	93%	56%	84%
86.6%		92%	94%	88%	90%	48%	88%	84%	88%	88%	90%	90%	85%	94%	62%	94%	93%	91%	92%	89%	91%	88%
78.2%		86%	84%	89%	89%	83%	87%	81%	76%	82%	80%	73%	77%	74%	60%	82%	81%	68%	69%	86%	64%	71%
86.5%		96%	92%	86%	91%	79%	87%	86%	95%	92%	96%	93%	89%	90%	78%	90%	92%	83%	63%	72%	88%	79%
76.2%		71%	82%	89%	77%	76%	95%	72%	67%	83%	86%	77%	63%	80%	90%	69%	85%	65%	55%	87%	66%	66%
74.1%		75%	74%	81%	83%	65%	77%	76%	86%	69%	74%	71%	78%	81%	73%	67%	78%	65%	70%	74%	62%	77%
84.8%		95%	93%	87%	93%	84%	74%	82%	80%	94%	94%	83%	70%	76%	88%	83%	85%	94%	73%	78%	84%	90%
77.0%		79%	84%	88%	77%	83%	85%	83%	77%	86%	72%	87%	76%	79%	62%	70%	80%	65%	65%	79%	62%	79%
83.1%		96%	92%	92%	84%	70%	82%	83%	85%	85%	83%	86%	83%	86%	70%	82%	77%	82%	76%	86%	83%	83%
62.4%		67%	68%	74%	74%	50%	79%	74%	65%	75%	61%	58%	32%	65%	44%	63%	68%	45%	58%	74%	61%	55%
66.2%		68%	74%	82%	73%	69%	78%	68%	66%	67%	69%	65%	43%	61%	52%	71%	76%	53%	54%	74%	56%	72%
60.0%		67%	61%	87%	71%	73%	67%	58%	79%	81%	56%	55%	11%	80%	32%	77%	78%	39%	33%	60%	42%	52%
80.2%		87%	94%	82%	85%	92%	72%	92%	79%	85%	91%	85%	81%	82%	79%	63%	65%	68%	74%	82%	82%	64%
76.2%		92%	83%	78%	80%	69%	76%	85%	73%	90%	84%	90%	79%	85%	93%	93%	85%	31%	30%	91%	33%	81%
64.2%		67%	67%	80%	84%	76%	48%	56%	59%	75%	54%	77%	57%	63%	42%	70%	87%	56%	55%	58%	55%	63%
67.1%		79%	92%	73%	73%	36%	61%	54%	59%	51%	57%	61%	71%	60%	65%	71%	78%	63%	60%	80%	94%	72%
77.5%		80%	80%	89%	91%	72%	81%	89%	79%	86%	88%	72%	25%	79%	65%	77%	88%	70%	74%	85%	80%	78%
59.4%		59%	58%	76%	62%	46%	74%	61%	70%	62%	52%	54%	64%	56%	48%	56%	61%	57%	63%	55%	56%	58%
83.5%		88%	90%	91%	89%	71%	83%	87%	89%	75%	89%	86%	74%	84%	68%	88%	88%	77%	83%	86%	84%	83%
74.5%		75%	72%	90%	92%	15%	66%	77%	78%	71%	83%	80%	60%	82%	73%	86%	89%	74%	75%	63%	75%	89%
79.8%		87%	77%	83%	82%	38%	92%	95%	85%	72%	78%	81%	81%	72%	88%	76%	76%	85%	69%	86%	85%	87%
80.5%		94%	88%	92%	96%	95%	68%	89%	90%	90%	97%	97%	67%	92%	92%	92%	92%	38%	69%	70%	44%	39%
70.8%		77%	74%	79%	79%	46%	77%	69%	73%	70%	79%	79%	51%	75%	49%	65%	78%	73%	73%	57%	79%	84%
74.3%		77%	78%	84%	88%	20%	77%	74%	86%	81%	89%	77%	72%	81%	77%	85%	65%	50%	81%	66%	61%	91%
94.3%		96%	90%	94%	91%	93%	97%	86%	94%	95%	95%	99%	96%	96%	94%	98%	97%	96%	92%	93%	92%	97%
82.9%		93%	87%	85%	93%	97%	70%	74%	85%	85%	78%	78%	92%	80%	85%	84%	75%	68%	70%	85%	84%	93%
83.4%		90%	83%	84%	83%	91%	61%	87%	80%	75%	88%	89%	83%	86%	78%	76%	86%	93%	83%	87%	85%	83%
81.0%		79%	82%	83%	83%	84%	85%	86%	75%	91%	82%	88%	65%	81%	89%	88%	89%	63%	70%	83%	79%	76%
82.7%		93%	70%	75%	89%	91%	59%	91%	67%	94%	95%	92%	63%	74%	77%	90%	96%	97%	88%	68%	77%	90%
81.7%		93%	94%	83%	84%	55%	82%	74%	88%	92%	90%	81%	90%	84%	95%	87%	89%	89%	21%	79%	89%	77%
68.1%		71%	71%	74%	70%	73%	63%	74%	69%	71%	86%	95%	66%	86%	88%	75%	82%	48%	25%	62%	40%	42%
74.0%		71%	80%	62%	62%	90%	87%	88%	86%	77%	76%	77%	64%	83%	80%	69%	65%	65%	64%	93%	63%	51%
87.0%		94%	89%	92%	92%	61%	90%	82%	87%	88%	92%	94%	83%	93%	89%	92%	88%	79%	83%	87%	88%	85%
78.6%		78%	81%	95%	95%	68%	71%	89%	90%	78%	81%	73%	76%	79%	71%	91%	86%	75%	66%	73%	63%	72%
82.8%		83%	88%	86%	83%	78%	86%	87%	88%	87%	82%	87%	75%	89%	71%	81%	92%	71%	76%	89%	75%	85%
87.4%		96%	76%	88%	85%	97%	84%	91%	88%	93%	89%	92%	86%	91%	85%	93%	92%	79%	79%	81%	85%	86%
82.2%		92%	91%	93%	85%	47%	79%	85%	86%	83%	81%	86%	86%	86%	64%	90%	87%	77%	82%	84%	82%	80%
88.9%		94%	90%	94%	94%	96%	81%	95%	93%	91%	91%	89%	86%	86%	87%	94%	89%	83%	86%	78%	80%	90%
65.0%		72%	71%	75%	71%	59%	79%	73%	72%	63%	67%	68%	12%	73%	54%	64%	75%	68%	56%	73%	54%	66%

Model Similarity Dendrogram

Hierarchical clustering of models based on response similarity. Models grouped closer are more similar.