DTeval

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Analysis: Gd4 C10 Dtef Global Dialogues Gd4 AiConcern%3Aequally Concerned And Excited C10 - Run 6da7c71...

Global Dialogues GD4 - Equally concerned and excited (10 context Qs)

DTEF: Predict response distributions for Equally concerned and 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

AI Safety & Robustness

Social interaction

Best Models (Coverage)

1.Claude 3.7 Sonnet
85.3%
2.Claude Sonnet 4.5
84.9%
3.GPT 5
84.3%
4.Claude Sonnet 4
83.8%
5.GPT 4.1
83.7%

🔀 Least Similar Models

Claude Sonnet 4.5vsGPT 4o Mini

73.3% similarity

👯 Most Similar Models

Claude Sonnet 4.5vsClaude Sonnet 4

91.1% 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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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 85.3%	7th 82.4%	4th 83.8%	2nd 84.9%	17th 77.7%	16th 77.7%	13th 79.8%	10th 80.3%	9th 81.0%	5th 83.7%	11th 80.2%	20th 70.8%	8th 82.1%	21st 69.9%	3rd 84.3%	6th 83.6%	15th 78.3%	18th 76.5%	14th 78.6%	19th 75.5%	12th 80.2%
74.3%		85%	83%	74%	79%	87%	60%	65%	78%	73%	71%	69%	63%	72%	69%	78%	78%	72%	77%	77%	73%	78%
66.6%		75%	62%	60%	70%	69%	53%	54%	57%	70%	86%	69%	67%	79%	69%	79%	82%	62%	63%	62%	67%	43%
80.2%		88%	80%	87%	91%	48%	83%	87%	80%	78%	84%	85%	77%	84%	74%	85%	82%	77%	78%	75%	82%	80%
82.9%		92%	86%	86%	92%	84%	74%	83%	87%	77%	89%	84%	84%	83%	54%	88%	88%	78%	87%	70%	84%	90%
62.7%		75%	65%	73%	76%	60%	57%	67%	70%	64%	64%	56%	59%	71%	44%	67%	63%	54%	57%	52%	57%	66%
84.6%		91%	87%	89%	88%	92%	89%	82%	87%	82%	91%	90%	64%	89%	62%	91%	90%	78%	89%	81%	78%	87%
71.0%		83%	90%	82%	82%	72%	62%	72%	67%	66%	62%	61%	75%	66%	61%	79%	77%	66%	68%	63%	68%	69%
81.6%		90%	84%	85%	67%	69%	83%	82%	89%	87%	72%	80%	85%	87%	76%	86%	81%	86%	81%	87%	77%	80%
80.3%		86%	90%	76%	74%	77%	92%	87%	87%	77%	88%	89%	87%	76%	81%	57%	72%	89%	78%	72%	81%	70%
77.6%		65%	78%	72%	68%	58%	80%	84%	73%	64%	77%	75%	90%	89%	84%	62%	73%	94%	71%	95%	87%	90%
84.5%		89%	90%	87%	89%	82%	87%	85%	84%	84%	88%	83%	84%	90%	65%	84%	91%	87%	89%	84%	73%	80%
83.4%		84%	94%	88%	92%	81%	89%	74%	82%	83%	92%	90%	70%	81%	71%	94%	90%	81%	76%	79%	79%	81%
83.3%		83%	91%	85%	87%	85%	88%	77%	87%	89%	86%	85%	48%	84%	85%	83%	90%	84%	87%	85%	77%	84%
63.7%		72%	89%	71%	64%	59%	64%	69%	46%	64%	59%	69%	39%	51%	73%	64%	66%	59%	66%	71%	54%	69%
71.4%		75%	61%	71%	79%	64%	65%	69%	66%	72%	84%	71%	18%	78%	79%	79%	80%	76%	83%	70%	79%	80%
79.7%		79%	78%	72%	89%	89%	72%	76%	76%	82%	89%	91%	66%	86%	75%	92%	86%	75%	88%	68%		65%
82.6%		81%	86%	81%	86%	84%	89%	83%	91%	83%	77%	87%	71%	80%	96%	81%	77%	80%	89%	79%	67%	87%
85.1%		88%	81%	94%	95%	83%	73%	90%	83%	93%	95%	92%	71%	86%	71%	91%	90%	85%	81%	90%	73%	83%
81.3%		82%	84%	86%	83%	82%	90%	81%	78%	85%	86%	82%	75%	86%	77%	86%	89%	80%	63%	81%	76%	75%
53.3%		100%	41%	44%	39%	51%	52%	54%	39%	37%	53%	37%	41%	46%	44%	100%	74%	41%	44%	39%	44%	100%
84.9%		95%	94%	95%	96%	90%	70%	93%	83%	87%	93%	85%	66%	94%	66%	94%	91%	75%	77%	87%	73%	78%
86.8%		88%	93%	92%	82%	88%	88%	88%	81%	92%	86%	91%	79%	80%	85%	86%	88%	85%	73%	88%	94%	95%
83.5%		90%	82%	92%	89%	78%	85%	82%	84%	86%	90%	81%	61%	93%	59%	89%	92%	79%	86%	85%	82%	89%
56.9%		66%	65%	56%	67%	64%	52%	49%	57%	55%	67%	40%	61%	70%	42%	57%	54%	52%	57%	45%	62%	57%
84.7%		94%	88%	86%	93%	86%	92%	91%	88%	81%	91%	86%	79%	94%	64%	87%	89%	85%	64%	82%	80%	79%
91.3%		98%	93%	95%	92%	93%	96%	85%	84%	91%	93%	93%	93%	93%	93%	86%	90%	84%	82%	93%	93%	97%
84.3%		93%	90%	86%	91%	94%	76%	89%	79%	79%	92%	80%	80%	80%	77%	92%	89%	79%	74%	92%	77%	81%
83.4%		95%	89%	82%	84%	86%	86%	85%	94%	91%	82%	89%	40%	87%	62%	92%	89%	82%	88%	81%	79%	89%
85.7%		96%	87%	89%	92%	87%	86%	90%	86%	88%	90%	86%	82%	88%	83%	92%	92%	75%	73%	68%	85%	85%
82.8%		92%	85%	90%	94%	83%	82%	83%	84%	82%	82%	86%	79%	85%	68%	87%	83%	82%	84%	65%	81%	82%
84.9%		92%	91%	88%	89%	89%	94%	85%	86%	84%	88%	81%	63%	83%	75%	94%	88%	75%	91%	86%	76%	84%
82.9%		86%	84%	89%	89%	83%	69%	83%	80%	86%	85%	75%	79%	86%	70%	89%	85%	87%	82%	88%	77%	88%
75.0%		88%	77%	77%	88%	69%	75%	74%	71%	73%	77%	81%	78%	68%	79%	74%	69%	77%	68%	69%		69%
66.9%		70%	63%	63%	72%	94%	64%	60%	63%	74%	67%	70%	60%	69%	46%	72%	77%	75%	68%	59%	66%	53%
81.3%		89%	88%	82%	87%	85%	74%	87%	82%	86%	82%	72%	82%	83%	71%	81%	79%	75%	70%	87%	83%	82%
85.7%		89%	89%	88%	88%	87%	91%	86%	87%	85%	87%	88%	87%	87%	72%	89%	90%	85%	86%	86%	67%	85%
85.3%		92%	90%	85%	90%	82%	78%	84%	84%	87%	86%	69%	87%	87%	79%	92%	91%	84%	80%	94%	80%	90%
84.1%		82%	92%	89%	87%	80%	81%	80%	91%	77%	86%	82%	84%	80%	82%	88%	92%	79%	76%	89%	84%	86%
81.2%		94%	91%	88%	86%	56%	94%	89%	86%	88%	87%	82%	70%	93%	65%	89%	93%	89%	82%	88%	10%	86%
75.0%		75%	68%	82%	79%	73%	74%	70%	80%	61%	79%	89%	57%	72%	88%	84%	89%	68%	69%	67%	64%	87%
83.7%		93%	89%	90%	93%	60%	76%	77%	83%	96%	93%	94%	72%	95%	75%	97%	90%	79%	85%	68%	88%	65%
80.5%		89%	92%	94%	93%	86%	73%	91%	83%	89%	80%	74%	82%	74%	93%	75%	77%	68%	48%	85%	59%	86%
75.5%		90%	73%	68%	80%	67%	75%	73%	79%	78%	77%	90%	69%	68%	73%	84%	80%	73%	85%	66%	66%	71%
73.4%		71%	61%	72%	82%	71%	68%	81%	82%	81%	84%	65%	86%	79%	64%	72%	65%	70%	71%	76%	74%	66%
83.0%		92%	85%	92%	92%	89%	90%	92%	89%	86%	92%	74%	30%	88%	63%	92%	90%	77%	75%	84%	82%	88%
82.3%		80%	87%	86%	87%	84%	76%	90%	79%	86%	88%	94%	68%	93%	58%	85%	84%	81%	77%	86%	75%	84%
78.9%		79%	85%	81%	72%	76%	82%	79%	86%	81%	78%	77%	70%	79%	70%	90%	78%	71%	80%	75%	81%	86%
89.3%		93%	93%	93%	91%	89%	82%	87%	92%	95%	97%	96%	86%	89%	55%	93%	90%	96%	86%	91%	91%	90%
84.7%		89%	95%	89%	80%	82%	89%	88%	79%	88%	81%	81%	79%	78%	90%	79%	87%	93%	76%	80%	86%	90%
82.7%		92%	83%	89%	90%	91%	91%	90%	82%	88%	93%	89%	27%	89%	67%	89%	84%	80%	70%	85%	83%	85%
86.6%		94%	90%	94%	93%	92%	91%	88%	83%	89%	91%	88%	75%	92%	81%	92%	94%	83%	69%	81%	66%	92%
74.1%		89%	70%	75%	82%	61%	70%	10%	78%	89%	79%	75%	72%	78%	68%	82%	82%	88%	76%	67%	76%	89%
77.9%		84%	81%	79%	95%	60%	84%	75%	82%	83%	76%	80%	77%	76%	60%	76%	79%	77%	79%	81%		73%
65.8%		76%	66%	62%	63%	73%	71%	58%	68%	61%	65%	54%	67%	70%	50%	76%	72%	56%	80%	61%	61%	72%
86.8%		95%	88%	93%	92%	84%	86%	90%	89%	85%	92%	79%	85%	90%	54%	91%	89%	84%	87%	89%	85%	96%
83.8%		90%	82%	89%	89%	86%	78%	79%	86%	85%	88%	80%	77%	81%	80%	85%	88%	90%	88%	76%	81%	82%
67.5%		69%	57%	68%	67%	87%	48%	63%	77%	71%	83%	63%	48%	75%	57%	80%	83%	68%	82%	52%	74%	46%
85.7%		91%	90%	93%	95%	92%	85%	90%	87%	87%	92%	77%	38%	89%	77%	92%	91%	90%	82%	91%	85%	86%
69.0%		70%	59%	66%	78%	96%	60%	63%	71%	73%	74%	83%	54%	76%	61%	77%	71%	66%	79%	56%	63%	53%
74.5%		78%	81%	84%	84%	72%	83%	80%	82%	74%	76%	67%	57%	78%	48%	77%	82%	70%	49%	83%	82%	78%
85.7%		94%	86%	91%	92%	52%	87%	89%	92%	88%	92%	92%	69%	88%	93%	76%	93%	81%	86%	90%	71%	97%
76.4%		85%	80%	79%	82%	78%	68%	74%	82%	76%	77%	69%	74%	76%	67%	80%	77%	78%	67%	79%	76%	81%
86.6%		94%	87%	92%	93%	83%	92%	93%	87%	87%	99%	89%	74%	90%	85%	98%	90%	73%	72%	87%	66%	88%
82.9%		91%	94%	88%	91%	78%	85%	88%	90%	87%	92%	87%	75%	87%	66%	93%	92%	85%	85%	92%	10%	84%
86.4%		94%	86%	90%	90%	94%	95%	84%	79%	86%	91%	83%	62%	95%	58%	89%	91%	90%	97%	87%	87%	87%
87.0%		90%	92%	88%	88%	93%	80%	91%	92%	89%	80%	94%	71%	84%	88%	91%	90%	81%	81%	93%	83%	87%
80.1%		87%	89%	77%	83%	90%	76%	78%	90%	85%	71%	84%	76%	74%	78%	90%	82%	81%	81%	72%	63%	75%
75.0%		86%	87%	78%	74%	64%	81%	78%	67%	71%	75%	86%	59%	76%	61%	83%	82%	70%	75%	71%	75%	75%
78.6%		80%	78%	81%	80%	81%	89%	74%	80%	80%	82%	74%	80%	74%	76%	82%	78%	80%	72%	72%	74%	84%
82.5%		82%	93%	85%	84%	71%	87%	86%	62%	75%	75%	94%	90%	71%	94%	82%	83%	87%	94%	84%	77%	76%
80.6%		88%	72%	94%	87%	87%	75%	86%	80%	81%	92%	64%	81%	87%	45%	89%	80%	84%	72%	83%	77%	88%
83.0%		86%	88%	86%	79%	64%	86%	84%	86%	72%	89%	82%	81%	88%	82%	88%	92%	93%	55%	92%	85%	85%
85.8%		94%	94%	91%	94%	34%	96%	87%	91%	87%	84%	87%	89%	97%	86%	92%	92%	78%	87%	87%	63%	91%
74.6%		77%	64%	89%	94%	82%	63%	70%	84%	85%	81%	67%	52%	85%	63%	79%	77%	80%	64%	57%		79%
78.5%		86%	76%	88%	82%	82%	70%	84%	78%	66%	80%	69%	68%	84%	62%	85%	80%	78%	83%	81%		87%
79.5%		79%	77%	89%	88%	45%	88%	88%	81%	77%	71%	86%	81%	79%	71%	79%	83%	90%	68%	86%	84%	80%
80.8%		83%	85%	89%	89%	85%	85%	77%	86%	80%	85%	66%	74%	85%	59%	85%	89%	80%	84%	82%	72%	77%
84.1%		95%	75%	90%	95%	81%	82%	84%	86%	88%	87%	86%	58%	94%	51%	93%	89%	85%	92%	88%	80%	88%
81.7%		81%	85%	88%	84%	65%	85%	77%	83%	81%	85%	85%	84%	86%	69%	89%	85%	84%	82%	84%	69%	85%
82.8%		89%	84%	94%	95%	86%	80%	86%	87%	83%	89%	72%	81%	84%	51%	91%	89%	79%	73%	86%	85%	74%
90.3%		97%	95%	91%	91%	88%	84%	91%	92%	95%	95%	97%	92%	91%	83%	93%	95%	90%	78%	87%	87%	85%
83.3%		80%	88%	89%	93%	88%	81%	77%	79%	90%	91%	87%	67%	91%	77%	83%	93%	77%	75%	89%	77%	77%
78.1%		86%	84%	89%	85%	39%	82%	85%	80%	83%	88%	76%	76%	87%	71%	82%	75%	71%	69%	78%	81%	74%
86.6%		91%	94%	96%	94%	75%	77%	89%	86%	95%	84%	84%	71%	78%	83%	94%	90%	94%	87%	83%	84%	89%
84.0%		83%	88%	92%	75%	83%	84%	93%	87%	88%	91%	83%	85%	78%	80%	87%	85%	83%	72%	81%	87%	79%
81.1%		88%	85%	84%	85%	57%	78%	81%	83%	82%	83%	83%	85%	87%	63%	86%	85%	81%	84%	81%	80%	83%
69.9%		72%	65%	78%	74%	72%	72%	78%	62%	71%	65%	77%	68%	67%	41%	76%	79%	64%	64%	74%	71%	78%
71.8%		70%	74%	75%	79%	78%	70%	64%	66%	72%	82%	81%	34%	78%	58%	74%	80%	81%	69%	77%	67%	79%
64.7%		72%	70%	78%	80%	80%	54%	64%	75%	75%	71%	55%	45%	78%	25%	71%	74%	57%	41%	67%	68%	58%
82.2%		90%	91%	96%	84%	90%	63%	89%	81%	85%	96%	95%	73%	85%	71%	67%	62%	82%	83%	82%	80%	81%
76.0%		86%	71%	65%	74%	89%	73%	81%	71%	80%	70%	91%	78%	71%	91%	71%	76%	69%	81%	66%	78%	64%
68.0%		61%	58%	90%	94%	97%	43%	62%	67%	64%	65%	64%	59%	70%	36%	84%	91%	68%	69%	64%	60%	61%
75.9%		87%	91%	74%	81%	84%	70%	68%	67%	63%	69%	66%	82%	75%	78%	83%	71%	77%	57%	95%	70%	85%
81.7%		84%	87%	91%	90%	83%	93%	87%	82%	88%	94%	87%	18%	92%	61%	86%	88%	75%	77%	80%	83%	90%
62.3%		60%	64%	68%	67%	65%	65%	59%	65%	63%	63%	49%	66%	64%	42%	64%	62%	55%	73%	70%	67%	58%
83.8%		88%	87%	88%	88%	86%	81%	82%	85%	88%	83%	81%	87%	85%	63%	90%	85%	83%	83%	91%	79%	77%
84.2%		85%	86%	92%	91%	57%	76%	83%	81%	77%	88%	92%	89%	91%	73%	92%	89%	85%	78%	92%	80%	92%
87.0%		95%	78%	92%	94%	48%	90%	95%	86%	88%	87%	90%	92%	90%	83%	85%	91%	94%	83%	88%	90%	87%
75.7%		86%	71%	89%	90%	80%	53%	71%	72%	70%	92%	90%	63%	83%	81%	93%	88%	57%	71%	58%	69%	62%
78.4%		81%	85%	83%	75%	86%	67%	73%	79%	79%	90%	80%	69%	87%	45%	78%	91%	71%	69%	82%	86%	90%
77.2%		84%	75%	78%	84%	71%	78%	79%	80%	77%	89%	67%	64%	84%	81%	75%	72%	88%	79%	60%	72%	85%
91.6%		95%	93%	90%	93%	91%	93%	88%	96%	94%	88%	93%	93%	85%	93%	95%	91%	88%	96%	84%	93%	92%
84.7%		93%	91%	90%	94%	93%	76%	84%	90%	84%	78%	88%	93%	81%	85%	81%	82%	65%	82%	95%	66%	88%
84.0%		82%	86%	86%	83%	90%	71%	80%	90%	86%	92%	84%	83%	83%	73%	83%	81%	88%	98%	74%	93%	78%
86.3%		80%	89%	83%	83%	88%	91%	89%	89%	89%	92%	92%	80%	93%	83%	94%	98%	78%	73%	85%	85%	79%
81.9%		93%	91%	72%	89%	37%	67%	95%	75%	89%	90%	96%	68%	73%	81%	92%	94%	95%	85%	70%	77%	91%
78.5%		94%	91%	88%	87%	49%	71%	74%	81%	91%	96%	86%	49%	77%	92%	95%	90%	74%	65%	67%	69%	62%
65.2%		71%	57%	63%	70%	83%	48%	52%	61%	68%	86%	73%	58%	74%	62%	75%	70%	62%	56%	54%	70%	57%
76.7%		77%	77%	77%	78%	83%	82%	84%	84%	75%	87%	85%	48%	85%	76%	72%	73%	69%	63%	87%	71%	78%
85.0%		91%	90%	88%	94%	90%	90%	79%	85%	84%	91%	92%	86%	85%	85%	90%	82%	80%	78%	70%	75%	80%
85.3%		84%	92%	92%	94%	91%	75%	90%	83%	90%	90%	72%	85%	86%	60%	90%	89%	93%	72%	93%	82%	89%
88.6%		88%	92%	93%	93%	89%	85%	95%	88%	91%	93%	83%	87%	87%	78%	91%	92%	91%	86%	89%	79%	91%
91.6%		97%	85%	96%	91%	90%	88%	93%	96%	93%	94%	94%	93%	95%	90%	94%	93%	85%	83%	89%	96%	89%
87.0%		92%	93%	94%	91%	61%	85%	89%	88%	92%	91%	82%	83%	95%	62%	92%	85%	89%	85%	95%	92%	91%
90.5%		93%	90%	96%	96%	93%	76%	95%	89%	97%	96%	99%	76%	90%	86%	98%	97%	88%	90%	78%	86%	92%
68.7%		69%	74%	76%	74%	75%	68%	64%	57%	73%	72%	63%	69%	70%	35%	68%	63%	67%	84%	73%	83%	65%

Model Similarity Dendrogram

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