DTeval

A Collective Intelligence Project

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Analysis: Gd4 C10 Dtef Global Dialogues Gd4 AiConcern%3Amore Concerned Than Excited C10 - Run f9abab0...

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

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

TAGS:

Global Dialogues Gd4

Instruction Following & Prompt Adherence

Factual Accuracy & Hallucination

Efficiency & Succinctness

Quantitative Analysis

Best Models (Coverage)

1.Claude 3.7 Sonnet
83.3%
2.GPT 5.1
82.9%
3.GPT 5
81.9%
4.GPT 4.1
81.5%
5.Claude Haiku 4.5
81.3%

🔀 Least Similar Models

Claude Sonnet 4.5vsGPT 4.1 Nano

72.2% similarity

👯 Most Similar Models

Claude Sonnet 4.5vsClaude Sonnet 4

89.5% similarity

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Macro Coverage Overview

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

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Not Met

	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 83.3%	5th 81.3%	9th 79.7%	6th 80.2%	14th 77.4%	19th 73.6%	10th 79.3%	11th 79.1%	7th 80.1%	4th 81.5%	12th 77.9%	21st 52.0%	8th 80.0%	20th 70.3%	3rd 81.9%	2nd 82.9%	15th 76.7%	17th 75.2%	18th 74.3%	16th 76.4%	13th 77.9%
67.4%		80%	70%	75%	73%	70%	55%	70%	82%	74%	75%	52%	24%	75%	51%	76%	74%	58%	57%	79%		78%
72.1%		83%	61%	67%	72%	87%	44%	57%	57%	69%	81%	73%	68%	83%	88%	91%	90%	76%	61%	58%	75%	74%
77.7%		90%	82%	86%	85%	53%	86%	82%	87%	78%	85%	74%	79%	88%	62%	84%	80%	61%	76%	78%	83%	53%
76.4%		87%	83%	79%	84%	80%	73%	74%	77%	75%	80%	78%	41%	78%	75%	75%	82%	74%	81%	75%	74%	80%
77.2%		91%	78%	81%	81%	71%	71%	79%	87%	72%	82%	82%	70%	77%	79%	77%	73%	78%	63%	71%	74%	85%
74.8%		78%	77%	77%	75%	80%	80%	75%	75%	73%	79%	75%	74%	80%	59%	75%	80%	65%	75%	79%	64%	76%
68.5%		77%	88%	77%	82%	75%	64%	75%	66%	67%	63%	60%	65%	61%	62%	66%	71%	65%	63%	68%	60%	64%
81.9%		92%	88%	72%	71%	85%	66%	81%	88%	91%	85%	88%	79%	84%	67%	89%	92%	76%	84%	89%	88%	65%
61.2%		82%	81%	40%	57%	67%	85%	91%	68%	76%	58%	84%	26%	53%	36%	48%	72%	45%	50%	40%	82%	44%
71.2%		62%	73%	85%	89%	28%	86%	86%	83%	90%	85%	93%	81%	80%	41%	56%	68%	63%	56%	51%	86%	53%
75.9%		79%	79%	76%	71%	74%	71%	73%	77%	74%	78%	75%	64%	79%	78%	79%	84%	73%	79%	81%	75%	75%
86.4%		93%	94%	91%	91%	97%	91%	77%	92%	87%	84%	96%	19%	88%	94%	92%	88%	88%	84%	93%	88%	88%
83.0%		92%	91%	83%	84%	94%	89%	91%	96%	92%	86%	76%	27%	92%	70%	86%	92%	79%	80%	69%	91%	83%
50.2%		68%	71%	63%	50%	68%	45%	51%	45%	44%	47%	38%	36%	37%	36%	62%	73%	41%	39%	40%	50%	51%
63.4%		71%	51%	63%	67%	39%	60%	60%	80%	72%	77%	57%	67%	65%	42%	73%	64%	68%	67%	44%	68%	76%
83.7%		88%	90%	85%	92%	77%	68%	80%	77%	91%	92%	88%	64%	94%	95%	93%	92%	78%	71%	76%	86%	80%
82.0%		80%	83%	80%	87%	94%	76%	88%	85%	83%	80%	76%	86%	82%	85%	81%	79%	84%	85%	72%	76%	80%
87.6%		88%	91%	89%	88%	76%	91%	87%	86%	92%	93%	91%	63%	95%	84%	92%	92%	91%	84%	88%	90%	89%
82.5%		84%	88%	84%	84%	88%	84%	85%	78%	86%	89%	84%	7%	92%	77%	82%	87%	91%	90%	90%	91%	91%
65.1%		100%	46%	57%	57%	74%	53%	66%	63%	53%	65%	59%	52%	58%	67%	100%	73%	55%	59%	51%	60%	100%
83.6%		83%	84%	84%	85%	86%	83%	83%	86%	86%	86%	84%	60%	85%	84%	87%	84%	88%	86%	85%	80%	86%
85.4%		91%	91%	93%	81%	95%	94%	97%	85%	89%	75%	80%	76%	81%	63%	95%	93%	86%	65%	88%	98%	78%
77.2%		88%	85%	70%	80%	80%	80%	73%	81%	81%	82%	79%	68%	79%	61%	88%	83%	76%	80%	61%	71%	76%
59.9%		70%	63%	63%	63%	78%	42%	57%	60%	69%	62%	65%	52%	59%	59%	58%	60%	73%	44%	46%	57%	57%
75.0%		76%	81%	75%	80%	71%	86%	91%	72%	76%	77%	71%	90%	75%	39%	82%	78%	64%	56%	73%	86%	76%
84.5%		86%	86%	89%	88%	86%	86%	85%	84%	82%	81%	88%	88%	89%	86%	89%	83%	80%	71%	86%	83%	79%
80.3%		93%	89%	78%	92%	90%	75%	86%	72%	77%	86%	88%	69%	78%	79%	89%	81%	72%	72%	73%	72%	75%
79.6%		81%	88%	78%	82%	88%	61%	93%	93%	94%	89%	77%	11%	90%	74%	88%	90%	88%	83%	71%	71%	81%
75.8%		83%	85%	72%	78%	86%	72%	91%	88%	78%	80%	66%	17%	80%	58%	90%	85%	79%	77%	67%	80%	80%
80.5%		87%	87%	82%	86%	81%	73%	80%	88%	88%	88%	74%	35%	88%	68%	87%	84%	84%	75%	89%	84%	83%
76.4%		75%	89%	79%	71%	79%	80%	78%	84%	86%	87%	83%	24%	89%	73%	77%	89%	65%	67%	82%	73%	75%
85.9%		93%	90%	86%	83%	87%	74%	90%	84%	88%	90%	86%	65%	90%	74%	95%	93%	85%	90%	90%	87%	84%
67.9%		86%	76%	62%	76%	86%	78%	76%	82%	66%	76%	62%	82%	58%	34%	65%	76%	48%	70%	38%	69%	59%
71.0%		71%	65%	68%	79%	79%	60%	66%	69%	81%	80%	83%	45%	80%	84%	78%	80%	63%	64%	61%	70%	65%
75.2%		86%	79%	79%	80%	77%	65%	79%	72%	77%	77%	67%	58%	81%	77%	50%	81%	84%	67%	84%	78%	82%
75.5%		78%	87%	75%	65%	54%	76%	78%	77%	80%	86%	89%	69%	79%	76%	83%	74%	68%	62%	75%	77%	78%
82.7%		88%	92%	82%	80%	78%	72%	86%	77%	88%	91%	83%	60%	91%	76%	82%	90%	85%	85%	86%	79%	85%
79.8%		83%	86%	87%	85%	87%	69%	73%	82%	76%	85%	84%	75%	72%	77%	85%	88%	74%	72%	77%	71%	87%
75.6%		72%	82%	69%	72%	70%	71%	68%	77%	77%	85%	82%	64%	85%	76%	58%	73%	80%	88%	71%	85%	83%
66.3%		66%	63%	66%	61%	68%	69%	64%	78%	62%	73%	88%	45%	66%	85%	66%	74%	53%	66%	70%	54%	56%
84.8%		84%	87%	91%	91%	63%	76%	90%	89%	95%	91%	77%	64%	96%	78%	92%	94%	81%	90%	80%	92%	79%
77.1%		89%	98%	90%	87%	78%	74%	82%	78%	85%	70%	65%	84%	71%	58%	89%	78%	63%	57%	70%	67%	86%
70.6%		82%	86%	60%	61%	72%	77%	78%	78%	73%	74%	69%	84%	69%	51%	82%	72%	69%	59%	66%	59%	62%
78.0%		79%	74%	74%	73%	97%	59%	72%	72%	74%	76%	75%	65%	88%	89%	74%	68%	91%	93%	66%	90%	89%
84.8%		94%	87%	89%	89%	82%	83%	90%	90%	86%	94%	90%	21%	89%	86%	89%	91%	85%	79%	87%	89%	90%
83.6%		85%	80%	89%	87%	83%	80%	96%	87%	84%	87%	84%	76%	91%	65%	86%	87%	89%	79%	79%	81%	80%
82.6%		80%	86%	80%	79%	85%	84%	82%	89%	84%	83%	84%	67%	88%	82%	85%	84%	93%	76%	81%	80%	82%
90.9%		94%	93%	97%	92%	88%	84%	99%	97%	98%	99%	90%	88%	93%	88%	87%	90%	92%	79%	90%	87%	84%
84.0%		86%	92%	90%	81%	64%	80%	96%	88%	92%	78%	81%	78%	76%	64%	88%	97%	89%	93%	82%	93%	77%
79.5%		86%	87%	85%	91%	79%	76%	88%	87%	88%	90%	76%	5%	83%	69%	89%	91%	80%	78%	78%	78%	85%
74.1%		75%	84%	80%	79%	76%	69%	85%	86%	80%	77%	73%	6%	76%	65%	74%	84%	78%	79%	73%	76%	81%
67.7%		88%	62%	76%	70%	57%	44%	74%	70%	66%	84%	47%	51%	77%	50%	91%	86%	69%	74%	41%	58%	87%
70.5%		82%	83%	82%	82%	66%	88%	76%	88%	67%	75%	48%	26%	74%	43%	80%	69%	54%	72%	75%	63%	87%
69.5%		72%	59%	69%	69%	77%	57%	65%	58%	68%	70%	78%	57%	73%	73%	77%	76%	76%	73%	63%	76%	73%
84.8%		94%	89%	89%	86%	81%	74%	92%	90%	95%	98%	89%	16%	93%	82%	93%	94%	88%	80%	82%	84%	91%
76.0%		88%	69%	80%	88%	90%	64%	70%	78%	76%	78%	71%	69%	71%	64%	90%	86%	77%	79%	67%	65%	76%
65.5%		67%	52%	73%	60%	84%	38%	57%	58%	62%	73%	54%	30%	69%	72%	84%	88%	76%	79%	52%	84%	64%
79.2%		82%	83%	85%	80%	84%	77%	81%	82%	78%	79%	84%	27%	85%	77%	83%	84%	90%	89%	78%	73%	82%
76.2%		82%	80%	73%	89%	83%	58%	69%	75%	81%	82%	83%	61%	78%	88%	79%	87%	73%	71%	62%	77%	70%
81.5%		86%	79%	93%	92%	85%	80%	72%	76%	74%	89%	94%	20%	89%	85%	88%	87%	91%	88%	82%	72%	89%
74.5%		81%	83%	77%	79%	55%	87%	87%	83%	85%	94%	83%	32%	75%	27%	87%	89%	88%	40%	63%	79%	91%
79.5%		89%	88%	85%	85%	76%	73%	76%	92%	91%	89%	63%	7%	90%	69%	87%	91%	77%	86%	84%	83%	88%
86.0%		98%	80%	98%	98%	83%	91%	94%	90%	93%	99%	95%	67%	92%	85%	91%	86%	82%	77%	69%	43%	96%
73.8%		78%	76%	73%	71%	64%	68%	70%	72%	64%	76%	71%	62%	83%	76%	69%	78%	81%	85%	80%	79%	74%
77.2%		84%	86%	79%	81%	80%	84%	80%	85%	84%	83%	77%	55%	86%	54%	80%	86%	68%	58%	85%	63%	84%
73.8%		87%	87%	86%	86%	59%	82%	88%	88%	89%	71%	61%	37%	73%	43%	87%	88%	79%	77%	56%	38%	88%
64.5%		85%	83%	77%	76%	71%	66%	84%	83%	73%	59%	53%	18%	57%	35%	82%	83%	49%	51%	58%	62%	49%
69.4%		72%	79%	79%	72%	68%	62%	67%	57%	66%	80%	83%	52%	80%	59%	78%	75%	69%	66%	67%	58%	68%
71.2%		69%	73%	70%	67%	72%	75%	73%	76%	75%	72%	77%	79%	70%	72%	74%	75%	59%	63%	73%	69%	63%
78.9%		88%	95%	76%	81%	76%	89%	83%	77%	79%	77%	92%	23%	65%	85%	77%	96%	81%	82%	90%	73%	72%
83.9%		84%	83%	92%	92%	97%	70%	86%	82%	90%	89%	91%	9%	93%	79%	92%	92%	92%	85%	80%	94%	90%
74.8%		87%	70%	73%	86%	62%	94%	83%	79%	85%	84%	76%	85%	68%	37%	84%	84%	47%	90%	47%	86%	64%
80.1%		92%	94%	86%	90%	88%	82%	76%	80%	86%	86%	66%	94%	82%	59%	80%	87%	61%	77%	86%	52%	79%
67.1%		73%	62%	75%	78%	62%	39%	61%	63%	75%	76%	73%	38%	72%	62%	86%	77%	82%	72%	37%	76%	70%
80.6%		86%	81%	84%	85%	86%	68%	75%	67%	66%	85%	85%	46%	81%	87%	87%	85%	85%	93%	90%	85%	86%
85.1%		89%	84%	92%	94%	72%	95%	92%	86%	87%	89%	88%	79%	89%	87%	85%	90%	82%	68%	86%	88%	66%
81.0%		88%	89%	85%	83%	83%	84%	85%	80%	85%	86%	83%	28%	85%	80%	86%	87%	83%	85%	76%	85%	76%
81.1%		85%	93%	86%	86%	79%	69%	84%	93%	92%	93%	74%	35%	90%	64%	88%	86%	78%	80%	79%	75%	95%
74.6%		78%	77%	77%	75%	43%	77%	76%	72%	78%	85%	77%	71%	79%	71%	79%	79%	79%	73%	75%	72%	74%
83.0%		90%	89%	87%	86%	85%	80%	83%	87%	86%	88%	88%	6%	87%	89%	89%	91%	86%	91%	88%	81%	87%
89.3%		91%	99%	99%	96%	86%	97%	89%	90%	93%	90%	87%	65%	91%	76%	96%	91%	82%	87%	99%	79%	93%
87.0%		87%	86%	88%	82%	82%	85%	82%	87%	85%	87%	93%	90%	87%	93%	85%	92%	85%	89%	92%	85%	85%
76.6%		89%	87%	91%	89%	64%	86%	86%	86%	83%	86%	68%	31%	77%	57%	85%	82%	71%	67%	63%	78%	83%
86.0%		88%	85%	88%	88%	92%	80%	91%	87%	88%	82%	92%	80%	84%	75%	88%	91%	89%	85%	90%	76%	87%
81.2%		88%	85%	82%	84%	82%	75%	83%	80%	87%	87%	83%	56%	88%	58%	88%	86%	81%	87%	77%	84%	85%
79.3%		85%	82%	80%	77%	88%	71%	76%	68%	77%	84%	86%	64%	83%	75%	84%	82%	84%	82%	78%	79%	81%
77.0%		75%	80%	82%	78%	74%	82%	74%	70%	73%	67%	81%	76%	74%	86%	73%	72%	82%	77%	87%	79%	75%
79.6%		79%	79%	82%	79%	85%	92%	82%	67%	77%	79%	85%	7%	80%	89%	84%	79%	90%	94%	93%	85%	84%
80.1%		81%	85%	89%	78%	92%	92%	71%	84%	73%	79%	72%	71%	75%	86%	82%	82%	74%	71%	84%	84%	78%
81.6%		84%	85%	90%	78%	96%	81%	96%	87%	84%	96%	91%	46%	89%	64%	67%	69%	86%	82%	80%	83%	79%
72.0%		92%	73%	59%	70%	79%	79%	83%	78%	79%	67%	56%	87%	66%	53%	58%	70%	56%	86%	85%	76%	59%
73.4%		79%	92%	82%	79%	76%	39%	63%	79%	71%	65%	75%	43%	70%	76%	81%	82%	79%	70%	67%	87%	86%
76.4%		83%	87%	79%	87%	58%	71%	77%	72%	71%	70%	64%	74%	80%	85%	81%	81%	79%	78%	83%	78%	67%
81.7%		79%	83%	82%	77%	87%	80%	78%	81%	81%	86%	92%	15%	92%	84%	82%	85%	85%	92%	91%	94%	90%
68.9%		58%	74%	77%	73%	79%	65%	69%	58%	70%	74%	80%	18%	76%	80%	66%	70%	81%	59%	62%	78%	79%
79.7%		89%	91%	76%	81%	83%	85%	90%	86%	87%	87%	87%	11%	90%	73%	85%	90%	81%	72%	83%	72%	74%
79.8%		85%	81%	87%	86%	83%	83%	80%	87%	87%	89%	73%	29%	92%	79%	86%	77%	75%	74%	86%	89%	67%
82.8%		89%	83%	91%	91%	52%	88%	89%	86%	93%	90%	90%	33%	94%	76%	87%	91%	67%	83%	93%	85%	88%
79.7%		88%	78%	76%	83%	80%	58%	73%	69%	74%	87%	89%	64%	86%	87%	91%	92%	85%	79%	75%	81%	78%
73.8%		78%	83%	80%	76%	85%	58%	66%	67%	68%	79%	82%	20%	81%	82%	73%	72%	76%	81%	82%	78%	82%
71.0%		93%	71%	85%	90%	53%	58%	69%	83%	78%	88%	52%	38%	81%	42%	86%	83%	65%	77%	45%	83%	70%
82.9%		84%	82%	80%	79%	70%	81%	91%	90%	89%	87%	79%	73%	85%	84%	86%	82%	85%	85%	81%	84%	84%
85.0%		82%	91%	87%	93%	93%	81%	90%	92%	88%	79%	88%	89%	76%	80%	81%	84%	86%	75%	88%	77%	86%
75.2%		75%	76%	70%	76%	77%	72%	81%	71%	84%	79%	74%	76%	77%	66%	74%	74%	83%	62%	75%	88%	70%
75.4%		73%	74%	74%	69%	76%	80%	72%	74%	81%	82%	77%	10%	81%	76%	79%	82%	81%	92%	82%	87%	82%
83.2%		84%	90%	84%	87%	66%	67%	86%	89%	91%	85%	84%	82%	84%	59%	88%	91%	81%	82%	80%	93%	95%
67.0%		92%	82%	64%	57%	85%	59%	66%	65%	82%	68%	67%	48%	63%	22%	94%	90%	84%	66%	65%	60%	27%
67.3%		78%	55%	63%	66%	81%	40%	58%	52%	67%	69%	76%	64%	69%	84%	80%	79%	73%	64%	52%	73%	71%
71.0%		70%	75%	73%	74%	78%	64%	77%	77%	69%	71%	78%	60%	65%	82%	69%	69%	75%	65%	78%	63%	60%
73.2%		92%	89%	72%	85%	79%	88%	81%	81%	83%	86%	68%	19%	73%	46%	82%	89%	60%	59%	68%	68%	70%
84.8%		85%	86%	81%	81%	77%	81%	89%	84%	91%	87%	83%	74%	88%	76%	92%	87%	91%	92%	87%	83%	85%
86.2%		93%	93%	88%	90%	95%	73%	85%	94%	87%	95%	91%	81%	85%	66%	93%	91%	87%	82%	61%	87%	94%
82.0%		88%	68%	82%	85%	88%	64%	78%	80%	85%	87%	80%	86%	87%	80%	86%	90%	77%	84%	78%	82%	86%
76.4%		83%	83%	81%	78%	68%	71%	77%	72%	79%	81%	84%	22%	82%	78%	84%	82%	82%	72%	79%	84%	82%
83.2%		83%	83%	83%	90%	89%	84%	86%	83%	82%	97%	86%	60%	81%	89%	93%	79%	94%	71%	73%	67%	94%
75.6%		78%	81%	85%	82%	85%	70%	77%	70%	78%	79%	92%	8%	82%	92%	77%	86%	87%	88%	89%	10%	91%

Model Similarity Dendrogram

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