DTeval

A Collective Intelligence Project

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Analysis: Gd4 C10 Dtef Global Dialogues Gd4 AgeGroup%3A56 65 C10 - Run 043cb14...

Global Dialogues GD4 - 56-65 (10 context Qs)

DTEF: Predict response distributions for 56-65. 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

Best Models (Coverage)

1.Claude 3.7 Sonnet
83.7%
2.GPT 5.1
81.0%
3.Claude Sonnet 4.5
80.9%
4.GPT 5
80.4%
5.Claude Sonnet 4
80.2%

🔀 Least Similar Models

Claude Sonnet 4.5vsGPT OSS 20b

71.0% similarity

👯 Most Similar Models

Claude Haiku 4.5vsClaude Sonnet 4

88.4% 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 83.7%	6th 79.8%	5th 80.2%	3rd 80.9%	14th 76.1%	21st 73.2%	12th 77.1%	10th 79.4%	9th 79.6%	7th 79.7%	11th 78.6%	19th 73.9%	8th 79.6%	13th 76.2%	4th 80.4%	2nd 81.0%	15th 76.0%	20th 73.2%	18th 75.0%	16th 75.7%	17th 75.6%
61.6%		68%	63%	60%	68%	61%	44%	53%	76%	63%	58%	53%	56%	64%	44%	65%	63%	66%	71%	71%	69%	58%
73.0%			72%	78%	83%	79%	48%	64%	66%	72%	86%	72%	62%	86%	65%	85%	85%	76%	68%	63%	75%	74%
79.8%		82%	80%	83%	84%	76%	78%	82%	78%	75%	85%	82%	73%	85%	76%	82%	81%	81%	86%	71%	82%	73%
78.1%			78%	82%	83%	82%	80%	78%	83%	78%	81%	79%	80%	82%	81%	78%	80%	76%	74%	80%	47%	79%
64.5%			70%	71%	70%	67%	53%	65%	62%	71%	72%	65%	56%	71%	62%	70%	68%	42%	56%	67%	62%	69%
78.5%			84%	75%	83%	80%	85%	74%	84%	76%	85%	79%	75%	86%	90%	90%	90%	67%	57%	59%	91%	61%
75.5%		83%	81%	89%	89%	77%	73%	79%	73%	75%	74%	73%	72%	72%	67%	72%	79%	72%	70%	72%	72%	72%
75.2%			66%	64%	56%	75%	79%	83%	77%	81%	68%	83%	81%	83%	81%	81%	69%	82%	84%	82%	67%	62%
76.5%			76%	81%	84%	71%	83%	85%	81%	76%	78%	84%	76%	82%	76%	43%	82%	78%	71%	77%	82%	65%
74.3%			66%	64%	66%	86%	75%	74%	72%	77%	69%	70%	84%	81%	86%	59%	72%	85%	76%	94%	56%	75%
83.1%			87%	81%	86%	82%	85%	86%	86%	82%	83%	82%	88%	81%	78%	84%	89%	84%	79%	81%		75%
75.8%		81%	83%	82%	85%	73%	79%	72%	68%	75%	84%	86%	73%	82%	73%	83%	77%	59%	59%	75%	59%	84%
66.8%			70%	57%	73%	67%	69%	60%	70%	72%	66%	59%	70%	73%	69%	72%	64%	68%	63%	59%	77%	57%
59.2%			85%	74%	71%	55%	54%	66%	50%	64%	60%	55%	51%	47%	55%	77%	65%	38%	42%	55%	63%	56%
63.6%			49%	66%	62%	32%	55%	57%	73%	67%	73%	73%	76%	73%	78%	62%	55%	76%	62%	61%	60%	63%
74.6%			78%	82%	87%	72%	61%	77%	76%	79%	83%	78%	59%	81%	76%	79%	82%	62%	76%	59%	65%	79%
82.4%		76%	84%	80%	84%	95%	86%	85%	95%	84%	69%	82%	76%	80%	78%	78%	75%	82%	88%	85%	77%	91%
81.1%		86%	80%	78%	84%	88%	79%	84%	83%	90%	83%	85%	70%	79%	76%	83%	82%	77%	74%	86%	77%	79%
83.1%		88%	81%	87%	89%	88%	84%	83%	77%	83%	88%	86%	86%	86%	86%	90%	83%	82%	56%	75%	86%	81%
73.1%		74%	78%	69%	72%	72%	63%	72%	74%	71%	78%	73%	81%	73%	83%	78%	75%	71%	67%	72%	66%
84.0%		77%	81%	78%	79%	80%	84%	81%	90%	91%	90%	88%	74%	91%	73%	83%	89%	85%	91%	92%	84%	83%
85.4%		84%	87%	89%	86%	86%	81%	91%	85%	89%	89%	89%	86%	81%	88%	90%	87%	92%	74%	78%	75%	86%
76.2%			75%	72%	71%	82%	76%	72%	81%	79%	78%	63%	79%	82%	79%	82%	86%	64%	87%	86%	65%	65%
71.8%			83%	83%	77%	79%	54%	85%	70%	86%	71%	69%	53%	74%	59%	79%	73%	65%	66%	60%	80%	69%
77.0%			79%	71%	82%	72%	79%	86%	76%	71%	83%	74%	68%	82%	68%	80%	85%	83%	76%	67%	79%	80%
92.2%		95%	95%	90%	92%	95%	94%	89%	89%	89%	93%	95%	95%	95%	95%	93%	90%	88%	82%	95%	95%	93%
79.4%		78%	75%	80%	74%	77%	77%	79%	84%	82%	78%	83%	79%	84%	79%	70%	81%	79%	83%	81%	82%	82%
70.9%		82%	73%	68%	69%	71%	50%	68%	77%	75%	75%	63%	66%	79%	59%	79%	77%	76%	76%	59%	73%	73%
82.6%		92%	85%	94%	94%	78%	76%	90%	77%	83%	87%	85%	86%	84%	72%	80%	87%	80%	63%	76%	82%	83%
82.0%		91%	91%	92%	92%	84%	63%	79%	87%	84%	82%	84%	76%	89%	78%	85%	87%	65%	78%	78%	87%	70%
82.2%			85%	84%	89%	84%	84%	80%	86%	79%	84%	80%	82%	89%	85%	96%	90%	83%	72%	73%	71%	67%
77.8%			80%	83%	80%	41%	77%	83%	89%	80%	88%	81%	80%	79%	81%	80%	84%	80%	60%	80%	75%	75%
60.8%			74%	60%	75%	85%	52%	55%	67%	58%	61%	56%	46%	53%	56%	67%	63%	67%	60%	48%		52%
69.5%			62%	70%	78%	77%	49%	69%	82%	76%	85%	68%	53%	69%	61%	78%	80%	62%	84%	49%	75%	63%
81.8%			76%	88%	62%	86%	83%	77%	91%	87%	84%	80%	88%	78%	83%	84%	88%	76%	81%	89%	76%	80%
83.4%			89%	86%	85%	84%	88%	85%	91%	85%	84%	79%	76%	83%	78%	91%	88%	83%	81%	77%	74%	80%
76.0%			72%	79%	67%	78%	81%	62%	86%	78%	77%	84%	80%	78%	82%	72%	68%	70%	76%	84%	77%	70%
80.3%		83%	88%	79%	83%	85%	65%	70%	86%	78%	76%	82%	70%	84%	81%	88%	87%	84%	81%	86%	66%	85%
81.2%			85%	86%	86%	69%	83%	83%	88%	88%	87%	84%	85%	80%	72%	84%	89%	80%	74%	64%	76%	81%
75.6%		83%	71%	75%	75%	78%	71%	71%	79%	74%	80%	73%	76%	76%	69%	85%	77%	72%	66%	79%	78%	80%
77.8%			84%	83%	79%	59%	72%	81%	83%	76%	77%	84%	67%	79%	82%	77%	83%	80%	76%	73%	79%	83%
81.4%		87%	89%	85%	92%	84%	86%	85%	83%	82%	78%	86%	88%	75%	79%	78%	74%	65%	64%	84%	78%	88%
61.1%			69%	61%	62%	62%	56%	58%	65%	60%	58%	60%	57%	60%	65%	65%	70%	63%	59%	57%	63%	52%
72.0%			72%	71%	82%	68%	59%	74%	82%	69%	81%	59%	51%	76%	72%	73%	65%	81%	83%	64%	78%	79%
86.0%			93%	82%	84%	79%	84%	82%	88%	92%	88%	85%	93%	90%	91%	83%	82%	80%	91%	84%	84%	84%
83.5%			81%	90%	94%	88%	68%	89%	82%	83%	88%	88%	71%	88%	83%	81%	91%	85%	80%	93%	70%	78%
84.5%			90%	89%	81%	91%	75%	85%	90%	85%	82%	83%	77%	83%	85%	90%	87%	75%	85%	79%	90%	88%
84.7%			82%	89%	87%	82%	75%	87%	82%	82%	77%	84%	84%	91%	89%	95%	91%	92%	82%	82%	82%	79%
83.5%		80%	88%	86%	82%	72%	78%	89%	84%	83%	90%	90%	88%	74%	85%	92%	78%	81%	88%	76%	82%	87%
81.7%		88%	85%	96%	90%	80%	81%	82%	72%	82%	89%	84%	84%	83%	73%	86%	84%	80%	62%	75%	77%	82%
88.3%		94%	94%	93%	93%	88%	88%	90%	84%	89%	82%	93%	91%	89%	88%	87%	95%	90%	70%	82%	90%	84%
66.5%			73%	76%	82%	44%	45%	10%	77%	79%	75%	62%	49%	65%	53%	79%	82%	83%	74%	55%	80%	87%
70.8%		76%	79%	75%	85%	89%	57%	62%	83%	77%	67%	55%	59%	73%	60%	79%	73%	78%	61%	76%	61%	62%
73.4%			74%	70%	76%	81%	59%	78%	73%	77%	83%	72%	60%	76%	69%	81%	76%	73%	80%	60%	74%	77%
78.2%			80%	79%	80%	70%	76%	79%	87%	85%	77%	78%	92%	82%	77%	77%	79%	75%	71%	68%	74%	78%
82.4%		90%	82%	92%	94%	62%	75%	80%	83%	86%	83%	81%	79%	82%	80%	85%	92%	83%	82%	78%	79%	82%
63.4%		70%	56%	71%	60%	82%	41%	64%	67%	71%	77%	62%	40%	71%	53%	79%	83%	63%	54%	48%	68%	51%
83.3%			91%	81%	81%	87%	84%	82%	88%	88%	84%	78%	87%	83%	84%	81%	86%	81%	83%	88%	75%	73%
67.5%			67%	78%	79%	78%	47%	66%	72%	72%	82%	75%	45%	72%	58%	79%	80%	50%	75%	46%	66%	63%
81.2%			84%	84%	83%	81%	79%	85%	84%	81%	83%	81%	80%	80%	81%	83%	82%	72%	81%	81%	78%	81%
76.1%			83%	82%	82%	73%	67%	81%	83%	82%	81%	82%	65%	77%	75%	75%	82%	83%	44%	77%	79%	69%
74.2%		84%	78%	76%	74%	75%	55%	65%	82%	76%	79%	62%	72%	78%	63%	81%	82%	84%	74%	73%	80%	66%
85.6%		90%	91%	90%	92%	79%	93%	88%	92%	79%	85%	92%	80%	88%	90%	96%	90%	73%	90%	96%	49%	74%
84.5%			80%	76%	83%	81%	92%	82%	89%	86%	81%	79%	93%	88%	89%	96%	95%	89%	75%	85%	80%	72%
75.5%			71%	75%	77%	76%	82%	76%	76%	75%	79%	76%	78%	82%	78%	87%	84%	59%	60%	71%	72%	76%
83.4%		91%	83%	92%	93%	77%	76%	87%	86%	83%	85%	86%	78%	88%	83%	88%	82%	81%	72%	88%	71%	82%
77.9%		89%	87%	78%	84%	83%	65%	83%	89%	83%	73%	72%	72%	75%	63%	85%	82%	71%	79%	65%	79%	79%
70.8%			84%	76%	72%	58%	78%	71%	70%	67%	64%	79%	67%	70%	71%	74%	74%	70%	69%	67%		64%
75.1%		78%	69%	74%	78%	73%	75%	75%	77%	72%	83%	74%	59%	78%	71%	82%	77%	86%	81%	70%	74%	72%
82.5%			90%	77%	76%	82%	92%	82%	60%	83%	76%	94%	89%	74%	88%	85%	90%	85%	68%	89%	88%	81%
80.5%			79%	81%	87%	72%	85%	79%	69%	76%	88%	82%	77%	83%	83%	86%	81%	78%	81%	80%	79%	83%
80.7%			90%	85%	68%	41%	92%	87%	82%	78%	78%	82%	83%	90%	84%	83%	83%	85%	74%	83%	80%	85%
73.0%		78%	75%	78%	85%	89%	68%	61%	63%	76%	79%	68%	53%	83%	58%	85%	75%	73%	72%	69%	75%	69%
68.8%			62%	72%	78%	82%	49%	49%	44%	78%	70%	77%	50%	69%	64%	82%	82%	81%	80%	46%	83%	77%
78.2%			78%	88%	84%	62%	78%	82%	78%	76%	80%	80%	76%	75%	76%	81%	80%	82%	73%	75%		81%
82.3%		86%	82%	85%	84%	82%	84%	84%	79%	81%	78%	94%	80%	89%	78%	80%	84%	83%	74%	74%	87%	81%
77.1%			78%	84%	84%	79%	80%	80%	78%	78%	80%	82%	64%	77%	73%	78%	78%	74%	66%	77%	72%	80%
83.4%		92%	87%	86%	90%	86%	68%	81%	89%	84%	82%	87%	74%	83%	75%	89%	88%	81%	68%	87%	88%	87%
80.1%			87%	85%	86%	57%	82%	82%	87%	82%	86%	80%	84%	78%	84%	89%	80%	76%	67%	86%	69%	75%
82.1%			85%	86%	85%	83%	81%	81%	80%	87%	80%	81%	87%	87%	81%	88%	88%	74%	82%	63%	83%	79%
85.0%		74%	88%	81%	78%	86%	75%	79%	88%	85%	82%	80%	91%	83%	89%	89%	96%	94%	94%	84%	89%	79%
79.4%		85%	89%	91%	97%	89%	93%	81%	67%	79%	89%	88%	81%	86%	92%	68%	76%	59%	56%	83%	59%	59%
77.0%		70%	78%	74%	75%	69%	82%	80%	82%	82%	78%	84%	80%	84%	82%	67%	78%	83%	57%	70%	81%	80%
88.4%		96%	94%	85%	88%	88%	74%	88%	98%	87%	95%	86%	87%	82%	86%	93%	85%	96%	83%	80%	90%	96%
77.2%			83%	80%	72%	83%	73%	81%	77%	78%	77%	76%	76%	73%	79%	78%	78%	76%	67%	75%	80%	81%
80.4%			84%	85%	88%	80%	79%	83%	82%	80%	83%	82%	78%	81%	78%	78%	79%	75%	80%	79%	74%	79%
76.8%			83%	83%	84%	75%	76%	80%	70%	82%	75%	77%	70%	69%	84%	77%	68%	64%	76%	92%	81%	70%
68.3%		67%	73%	68%	67%	71%	72%	66%	49%	65%	75%	71%	65%	64%	74%	68%	69%	60%	72%	75%	71%	72%
78.9%			83%	93%	87%	84%	73%	79%	85%	81%	83%	74%	64%	79%	73%	83%	82%	65%	65%	82%	73%	89%
82.8%		90%	93%	94%	90%	90%	75%	88%	75%	81%	82%	91%	66%	78%	76%	68%	73%	72%	88%	98%	84%	86%
70.0%			67%	70%	69%	77%	63%	77%	74%	76%	51%	66%	84%	71%	75%	71%	72%	84%	55%	72%	75%	52%
81.0%			68%	89%	91%	92%	57%	80%	94%	90%	83%	94%	63%	82%	62%	82%	93%	94%	81%	75%	88%	61%
71.2%		77%	83%	73%	77%	49%	76%	69%	71%	69%	69%	75%	77%	76%	69%	64%	65%	71%	65%	85%	76%	60%
84.7%		90%	92%	97%	97%	92%	80%	86%	76%	85%	88%	89%	92%	89%	77%	85%	78%	75%	78%	78%	86%	69%
63.2%			61%	69%	70%	68%	69%	69%	63%	64%	69%	62%	59%	62%	58%	59%	62%	58%	56%	62%	60%	64%
76.8%			72%	71%	71%	78%	80%	67%	88%	82%	70%	71%	85%	87%	90%	79%	76%	70%	79%	82%	82%	56%
85.4%		87%	81%	95%	93%	55%	94%	84%	86%	78%	94%	92%	93%	94%	93%	84%	92%	73%	79%	90%	76%	80%
87.8%		93%	87%	90%	86%	52%	88%	93%	87%	90%	87%	94%	94%	91%	93%	83%	92%	94%	94%	88%	84%	84%
77.0%			76%	82%	88%	69%	55%	79%	77%	74%	88%	81%	69%	85%	70%	85%	87%	82%	80%	65%	73%	76%
73.4%			69%	79%	81%	77%	68%	71%	83%	72%	79%	78%	54%	77%	61%	63%	77%	73%	68%	78%	80%	81%
65.0%		75%	65%	78%	74%	34%	61%	59%	75%	67%	74%	62%	44%	70%	63%	69%	72%	57%	72%	48%	72%	73%
76.1%		77%	77%	74%	74%	69%	78%	74%	81%	75%	75%	74%	76%	83%	77%	75%	81%	78%	82%	74%	62%	83%
72.3%		83%	79%	76%	80%	86%	77%	66%	78%	78%	60%	61%	76%	60%	66%	71%	79%	71%	58%	74%	66%	74%
75.7%		83%	73%	71%	69%	70%	61%	67%	84%	76%	76%	80%	82%	79%	74%	78%	81%	93%	74%	73%	76%	69%
80.0%		84%	84%	88%	85%	81%	75%	84%	81%	81%	86%	84%	82%	84%	82%	84%	85%	64%	66%	73%	61%	85%
85.0%		94%	93%	68%	74%	82%	73%	90%	88%	95%	89%	82%	92%	85%	88%	82%	95%	88%	79%	83%	80%	84%
74.9%			82%	81%	81%	80%	46%	72%	79%	96%	81%	76%	55%	79%	65%	92%	90%	68%	70%	63%	61%	81%
77.3%			75%	81%	87%	83%	46%	73%	72%	81%	93%	90%	53%	94%	84%	91%	95%	78%	43%	70%	76%	81%
85.9%		85%	91%	92%	90%	88%	89%	94%	88%	89%	91%	93%	57%	86%	83%	84%	86%	87%	82%	94%	83%	72%
67.9%			72%	71%	76%	69%	67%	63%	72%	68%	74%	66%	59%	67%	66%	72%	71%	63%	71%	57%	70%	63%
75.2%			77%	69%	64%	62%	85%	76%	77%	79%	76%	85%	80%	80%	77%	83%	76%	83%	79%	69%	60%	68%
82.7%		85%	87%	79%	79%	88%	83%	80%	87%	86%	76%	79%	84%	88%	89%	89%	88%	64%	83%	70%	84%	89%
90.5%		91%	81%	91%	95%	89%	85%	94%	95%	95%	97%	94%	95%	95%	88%	89%	83%	90%	82%	87%	90%	94%
88.2%			90%	83%	89%	86%	88%	87%	90%	94%	93%	86%	87%	90%	98%	91%	92%	84%	76%	95%	84%	80%
87.6%		89%	93%	94%	89%	84%	80%	95%	87%	98%	81%	95%	73%	81%	86%	86%	98%	94%	78%	74%	90%	95%
86.0%			86%	88%	91%	83%	85%	88%	80%	87%	82%	89%	84%	79%	83%	87%	87%	84%	87%	88%	87%	94%

Model Similarity Dendrogram

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