DTeval

A Collective Intelligence Project

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Analysis: Gd4 C10 Dtef Global Dialogues Gd4 AgeGroup%3A46 55 C10 - Run 0144459...

Global Dialogues GD4 - 46-55 (10 context Qs)

DTEF: Predict response distributions for 46-55. 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
85.2%
2.GPT 5
83.3%
3.Claude Sonnet 4.5
83.1%
4.GPT 5.1
82.5%
5.GPT 4.1
82.2%

🔀 Least Similar Models

Claude Sonnet 4.5vsGPT OSS 20b

74.2% similarity

👯 Most Similar Models

Claude Sonnet 4.5vsClaude Sonnet 4

87.4% 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 85.2%	8th 81.4%	6th 81.9%	3rd 83.1%	20th 75.8%	18th 76.2%	11th 80.7%	9th 81.3%	12th 80.3%	5th 82.2%	10th 81.0%	19th 76.0%	7th 81.8%	14th 78.3%	2nd 83.3%	4th 82.5%	16th 76.9%	21st 73.5%	17th 76.8%	15th 77.5%	13th 79.6%
75.6%		81%	79%	77%	88%	78%	52%	68%	78%	75%	68%	65%	71%	72%	65%	81%	82%	86%	83%	71%	83%	84%
64.1%			61%	67%	75%	81%	38%	56%	56%	70%	88%	67%	60%	78%	64%	77%	76%	53%	45%	52%	67%	51%
84.4%		89%	82%	91%	94%	87%	83%	88%	78%	84%	85%	89%	81%	88%	81%	87%	89%	83%	78%	77%	82%	76%
82.5%			89%	77%	87%	72%	87%	86%	90%	86%	86%	75%	79%	79%	82%	87%	88%	81%	77%	80%	82%	80%
63.2%			72%	70%	71%	52%	55%	66%	61%	71%	71%	59%	65%	71%	57%	63%	64%	55%	59%	57%	56%	69%
73.6%			69%	72%	85%	86%	71%	68%	74%	73%	84%	72%	67%	84%	79%	92%	81%	54%	55%	68%	72%	66%
71.3%		76%	82%	86%	87%	75%	67%	70%	67%	64%	69%	69%	75%	66%	61%	76%	77%	67%	65%	64%	66%	69%
74.8%			71%	64%	61%	73%	87%	83%	75%	74%	78%	84%	80%	82%	76%	78%	76%	80%	68%	78%	64%	63%
76.1%			82%	79%	77%	73%	82%	81%	81%	77%	81%	87%	80%	81%	82%	41%	77%	84%	70%	74%	68%	64%
73.0%			61%	65%	63%	59%	90%	74%	74%	61%	66%	69%	91%	87%	88%	68%	64%	91%	40%	87%	80%	83%
83.9%			91%	84%	92%	88%	85%	83%	81%	79%	91%	79%	80%	85%	86%	90%	86%	84%	77%	81%	80%	77%
83.7%		90%	88%	89%	89%	90%	92%	80%	83%	86%	90%	89%	75%	86%	86%	90%	82%	73%	69%	84%	73%	73%
77.8%			77%	74%	78%	79%	76%	70%	86%	84%	80%	78%	89%	83%	76%	73%	79%	82%	81%	67%	76%	68%
61.5%			85%	69%	69%	61%	63%	64%	45%	62%	56%	66%	47%	49%	58%	70%	71%	45%	45%	65%	73%	68%
72.8%		80%	62%	77%	89%	22%	71%	71%	79%	74%	81%	78%	71%	84%	68%	78%	78%	78%	79%	63%	71%	75%
81.5%			89%	90%	91%	69%	74%	88%	83%	85%	81%	92%	61%	88%	77%	94%	85%	72%	80%	73%	76%	83%
80.1%		78%	83%	78%	83%	86%	83%	81%	91%	80%	76%	80%	82%	73%	84%	73%	72%	80%	85%	77%	68%	90%
84.1%		86%	85%	77%	83%	87%	83%	83%	88%	84%	87%	80%	80%	84%	74%	84%	90%	83%	88%	89%		86%
85.7%		85%	89%	87%	83%	85%	80%	90%	89%	95%	86%	91%	93%	87%	86%	86%	91%	75%	72%	82%	84%	83%
79.4%		79%	83%	69%	74%	77%	64%	75%	81%	71%	70%	88%	84%	75%	86%	84%	82%	79%	85%	80%	93%	88%
84.5%		83%	88%	79%	88%	78%	77%	88%	86%	87%	90%	89%	77%	88%	75%	88%	88%	87%	85%	89%	77%	88%
90.9%		97%	92%	90%	90%	86%	90%	95%	96%	96%	92%	97%	84%	80%	86%	94%	89%	90%	92%	89%	88%	95%
73.2%			66%	68%	75%	92%	67%	72%	65%	82%	81%	59%	69%	80%	85%	81%	80%	65%	69%	51%	69%	87%
69.5%			77%	83%	86%	89%	54%	74%	71%	67%	79%	65%	52%	78%	58%	55%	71%	68%	54%	51%	80%	79%
78.1%			83%	81%	83%	43%	77%	83%	78%	79%	82%	79%	83%	82%	75%	88%	80%	75%	71%	85%	78%	77%
92.1%		94%	85%	93%	95%	95%	87%	94%	93%	92%	95%	93%	95%	93%	95%	90%	92%	87%	87%	95%	95%	89%
84.2%		92%	85%	95%	85%	85%	78%	95%	82%	77%	89%	82%	82%	89%	77%	91%	88%	75%	84%	77%	77%	83%
77.3%		83%	78%	76%	74%	73%	54%	77%	90%	81%	81%	77%	77%	78%	71%	84%	84%	66%	81%	71%		89%
83.2%		97%	89%	88%	86%	85%	71%	87%	90%	82%	87%	79%	81%	85%	70%	94%	87%	82%	75%	72%	75%	85%
85.3%		94%	85%	96%	95%	92%	67%	86%	90%	80%	85%	87%	79%	84%	78%	95%	91%	79%	82%	82%	82%	83%
79.3%			79%	81%	94%	86%	74%	77%	75%	82%	86%	82%	78%	91%	89%	90%	83%	62%	55%	80%	58%	83%
78.2%			75%	68%	72%	82%	83%	79%	79%	78%	77%	81%	77%	81%	82%	84%	76%	82%	75%	82%	79%	72%
71.4%			77%	78%	90%	85%	68%	74%	70%	72%	83%	75%	67%	67%	63%	85%	77%	60%	76%	59%	30%	72%
84.0%			84%	93%	95%	81%	78%	85%	84%	92%	94%	90%	71%	93%	78%	89%	92%	58%	82%	68%	88%	84%
82.8%			76%	84%	86%	86%	86%	85%	80%	85%	83%	81%	88%	76%	89%	86%	81%	76%	76%	90%	74%	87%
73.3%			79%	80%	80%	89%	71%	76%	68%	71%	88%	71%	55%	88%	81%	85%	83%	61%	63%	65%	53%	59%
86.8%			91%	88%	74%	80%	88%	80%	83%	88%	92%	85%	91%	95%	90%	94%	85%	90%	87%	90%	84%	82%
77.1%		86%	81%	75%	78%	85%	81%	66%	80%	73%	76%	73%	83%	75%	79%	82%	77%	68%	67%	81%	76%	77%
81.8%			81%	84%	88%	84%	80%	84%	83%	86%	84%	88%	60%	90%	92%	94%	89%	71%	70%	74%	73%	82%
77.0%		81%	73%	71%	70%	79%	80%	66%	79%	76%	69%	82%	84%	72%	82%	82%	78%	78%	77%	81%	78%	80%
83.0%			94%	87%	80%	56%	85%	92%	97%	77%	74%	88%	78%	85%	88%	84%	78%	87%	83%	77%	89%	80%
73.6%		79%	83%	82%	85%	68%	82%	83%	79%	77%	68%	75%	73%	73%	68%	71%	76%	58%	48%	79%	65%	73%
74.6%			71%	71%	71%	64%	64%	70%	77%	79%	73%	84%	83%	75%	71%	89%	78%	75%	75%	67%	79%	77%
85.6%			84%	92%	92%	87%	77%	91%	91%	90%	92%	81%	90%	88%	81%	81%	76%	80%	86%	84%	77%	91%
83.6%			85%	90%	88%	89%	81%	82%	84%	84%	84%	86%	83%	85%	81%	85%	84%	78%	68%	84%	83%	89%
87.1%			77%	87%	87%	86%	84%	94%	92%	96%	91%	86%	76%	86%	78%	93%	93%	82%	86%	96%	93%	79%
80.9%			88%	85%	77%	76%	76%	82%	86%	81%	82%	76%	69%	84%	79%	85%	84%	74%	84%	75%	84%	91%
89.5%			93%	96%	98%	81%	75%	86%	85%	88%	88%	93%	90%	92%	94%	89%	98%	98%	82%	93%	83%	87%
82.7%		83%	94%	86%	86%	73%	86%	94%	80%	85%	85%	85%	73%	73%	82%	87%	89%	72%	77%	84%	74%	88%
87.2%		83%	92%	86%	88%	86%	86%	93%	83%	93%	85%	91%	91%	86%	85%	89%	90%	88%	80%	79%	84%	93%
83.2%		72%	89%	92%	85%	87%	78%	90%	90%	89%	85%	85%	81%	87%	80%	85%	78%	87%	67%	76%	84%	81%
75.7%		92%	64%	80%	93%	40%	51%	83%	70%	94%	87%	62%	62%	76%	57%	87%	89%	90%	81%	54%	85%	93%
77.5%		82%	82%	79%	95%	86%	71%	65%	84%	74%	74%	81%	61%	75%	76%	86%	86%	74%	67%	75%	76%	78%
76.6%			77%	73%	74%	84%	62%	76%	76%	78%	81%	78%	76%	76%	73%	82%	81%	63%	85%	66%	82%	88%
82.0%			79%	83%	82%	76%	77%	79%	88%	87%	83%	86%	83%	88%	86%	89%	86%	79%	80%	77%	73%	79%
79.5%		84%	80%	82%	84%	67%	77%	81%	80%	82%	82%	76%	77%	77%	78%	81%	84%	83%	80%	76%	77%	82%
70.3%		79%	62%	87%	75%	58%	53%	73%	74%	84%	88%	68%	58%	73%	64%	89%	88%	63%	57%	57%	62%	64%
87.5%			90%	79%	91%	90%	90%	82%	95%	88%	91%	89%	88%	91%	87%	96%	93%	80%	87%	78%	87%	78%
68.9%			74%	80%	84%	67%	54%	75%	73%	73%	83%	74%	55%	74%	61%	76%	73%	56%	52%	56%	71%	67%
81.4%			83%	89%	89%	85%	87%	93%	92%	75%	84%	80%	66%	83%	78%	78%	84%	78%	70%	86%	71%	78%
77.3%			80%	86%	86%	49%	75%	80%	84%	85%	86%	92%	76%	77%	73%	73%	74%	67%	60%	78%	76%	88%
80.0%		90%	85%	80%	84%	80%	58%	78%	87%	82%	82%	65%	78%	81%	68%	92%	89%	79%	81%	73%	84%	85%
83.9%		76%	91%	85%	85%	86%	88%	81%	97%	79%	81%	91%	81%	90%	94%	84%	87%	80%	86%	83%	61%	76%
81.4%			83%	72%	88%	47%	90%	84%	82%	80%	89%	80%	78%	81%	87%	87%	90%	84%	81%	80%	82%	83%
68.7%			66%	69%	75%	87%	65%	73%	60%	65%	79%	63%	56%	87%	76%	84%	74%	56%	38%	70%	66%	64%
80.0%		87%	85%	81%	88%	61%	73%	85%	87%	82%	84%	84%	74%	81%	74%	85%	79%	65%	74%	77%	88%	85%
76.3%		86%	81%	76%	84%	88%	62%	80%	91%	77%	80%	74%	67%	76%	65%	84%	81%	79%	63%	69%	65%	75%
73.1%			80%	78%	77%	56%	78%	74%	71%	71%	69%	81%	68%	68%	80%	78%	76%	75%	71%	61%	78%	73%
75.8%		84%	79%	81%	83%	71%	68%	80%	77%	75%	88%	67%	61%	76%	72%	79%	82%	78%	73%	71%	72%	74%
84.4%			97%	82%	73%	70%	93%	91%	64%	77%	78%	95%	89%	85%	90%	94%	89%	75%	92%	93%	68%	94%
84.1%			87%	88%	88%	82%	94%	90%	83%	85%	93%	83%	80%	86%	84%	83%	80%	85%	74%	81%	76%	81%
80.9%			85%	85%	71%	62%	90%	86%	87%	69%	77%	83%	82%	88%	82%	77%	72%	77%	86%	87%	86%	86%
80.2%		89%	84%	92%	69%	39%	92%	86%	77%	82%	73%	93%	83%	77%	84%	74%	88%	77%	77%	83%	85%	81%
71.9%		80%	73%	83%	86%	69%	65%	72%	77%	85%	73%	66%	44%	83%	68%	73%	68%	70%	60%	54%	78%	82%
82.1%			86%	80%	86%	84%	86%	83%	81%	76%	86%	81%	79%	77%	83%	88%	84%	79%	77%	80%	84%	82%
78.5%		82%	79%	84%	85%	50%	87%	88%	81%	75%	68%	84%	80%	76%	81%	80%	77%	85%	69%	81%	79%	77%
82.5%			82%	76%	73%	78%	81%	80%	88%	84%	83%	84%	78%	84%	86%	88%	91%	89%	73%	84%	85%	83%
81.0%		88%	83%	73%	83%	78%	62%	78%	87%	82%	78%	82%	75%	83%	82%	85%	93%	89%	78%	76%	81%	85%
76.7%			82%	74%	84%	81%	75%	73%	73%	70%	82%	70%	79%	81%	70%	85%	82%	61%	78%	77%	84%	74%
79.3%			80%	80%	77%	72%	78%	79%	86%	78%	79%	84%	79%	79%	76%	87%	81%	80%	71%	82%	79%	78%
89.3%		70%	90%	86%	86%	82%	93%	88%	97%	96%	90%	96%	90%	93%	96%	91%	96%	97%	85%	81%	86%	87%
86.6%		90%	92%	84%	80%	91%	89%	89%	89%	89%	90%	93%	80%	95%	87%	88%	92%	80%	80%	80%	80%	80%
77.1%		85%	81%	88%	86%	55%	81%	81%	87%	75%	80%	77%	73%	78%	73%	70%	70%	79%	71%	73%	77%	79%
84.9%		91%	89%	85%	86%	85%	89%	79%	92%	89%	90%	86%	84%	79%	85%	84%	84%	86%	68%	86%	86%	80%
81.7%			85%	89%	89%	82%	77%	81%	85%	81%	83%	86%	65%	85%	78%	90%	77%	80%	82%	76%	80%	83%
77.1%			84%	81%	84%	47%	85%	76%	80%	76%	85%	76%	71%	85%	83%		84%	79%	72%	79%	63%	74%
81.0%			85%	88%	84%	78%	81%	82%	81%	79%	79%	82%	71%	77%	85%	81%	85%	64%	81%	89%	83%	85%
79.2%		85%	80%	84%	85%	81%	72%	80%	75%	78%	87%	84%	81%	85%	72%	75%	81%	78%	64%	78%	82%	77%
82.5%			87%	86%	83%	79%	84%	84%	83%	88%	88%	83%	63%	88%	84%	81%	89%	72%	66%	89%	85%	87%
83.2%		92%	93%	96%	99%	95%	75%	84%	84%	84%	84%	92%	75%	84%	74%	67%	68%	67%	80%	93%	82%	79%
65.0%			65%	59%	63%	87%	56%	67%	59%	64%	61%	71%	72%	64%	64%	61%	67%	78%	42%	59%	76%	66%
77.6%			65%	86%	83%	89%	55%	82%	83%	77%	85%	82%	65%	73%	68%	89%	91%	88%	73%	72%	60%	85%
72.1%		77%	81%	80%	84%	53%	75%	67%	66%	61%	59%	74%	85%	77%	73%	72%	60%	68%	61%	92%	85%	65%
82.0%		87%	81%	81%	79%	87%	63%	87%	91%	84%	78%	85%	84%	84%	79%	89%	89%	82%	74%	69%	81%	87%
81.0%			82%	88%	88%	78%	86%	83%	84%	82%	83%	80%	77%	84%	78%	76%	74%	69%	83%	82%	78%	84%
78.6%			74%	75%	71%	66%	81%	83%	87%	76%	75%	83%	84%	83%	85%	83%	81%	85%	80%	76%	81%	62%
84.2%		83%	79%	88%	88%	67%	93%	82%	91%	77%	88%	90%	83%	86%	87%	88%	85%	90%	72%	90%	76%	85%
86.6%		91%	79%	95%	88%	48%	87%	93%	87%	88%	80%	93%	92%	87%	92%	86%	84%	94%	84%	88%	90%	93%
72.4%			73%	78%	92%	86%	49%	73%	68%	71%	89%	77%	57%	79%	64%	84%	81%	66%	69%	62%	69%	60%
85.8%			90%	95%	91%	76%	89%	87%	88%	89%	92%	90%	77%	87%	81%	91%	94%	90%	67%	85%	76%	80%
74.8%		90%	68%	77%	83%	39%	69%	68%	82%	69%	89%	68%	55%	78%	70%	82%	85%	89%	73%	64%	84%	88%
89.6%		87%	91%	90%	88%	81%	90%	80%	92%	91%	90%	90%	85%	87%	95%	97%	98%	94%	94%	80%	92%	89%
75.9%		87%	77%	83%	83%	78%	76%	75%	82%	79%	66%	74%	82%	70%	69%	78%	74%	69%	62%	83%	69%	78%
78.6%		82%	75%	70%	70%	77%	65%	75%	74%	75%	81%	82%	74%	80%	75%	86%	91%	88%	82%	79%	87%	83%
81.8%		91%	87%	91%	83%	86%	78%	89%	87%	85%	86%	81%	77%	87%	82%	87%	89%	82%	53%	68%	79%	69%
83.7%		92%	91%	70%	72%	76%	73%	91%	88%	91%	92%	80%	80%	87%	78%	78%	88%	79%	84%	90%	91%	87%
79.9%		81%	79%	89%	88%	63%	70%	81%	89%	84%	86%	88%	72%	88%	70%	88%	84%	79%	78%	75%	89%	57%
71.1%			68%	73%	77%	86%	44%	70%	61%	77%	87%	78%	65%	84%	72%	86%	84%	54%	66%	59%	62%	69%
75.9%		76%	76%	78%	79%	81%	80%	81%	77%	73%	74%	80%	77%	78%	73%	70%	69%	71%	78%	78%	70%	75%
78.1%			84%	74%	85%	78%	81%	77%	81%	79%	88%	79%	80%	79%	69%	82%	70%	77%	75%	66%	81%	78%
85.4%			87%	87%	83%	72%	94%	91%	89%	87%	93%	74%	81%	90%	86%	95%	89%	85%	75%	87%	74%	90%
85.5%		86%	87%	86%	87%	93%	82%	84%	84%	88%	78%	87%	87%	89%	94%	88%	87%	83%	71%	78%	86%	91%
90.5%		93%	83%	94%	95%	91%	86%	92%	94%	91%	93%	90%	95%	93%	90%	93%	93%	93%	80%	91%	80%	91%
81.0%			86%	83%	78%	79%	87%	77%	78%	78%	89%	87%	74%	80%	83%	90%	81%	84%	72%	76%	78%	79%
85.3%		91%	94%	93%	85%	80%	75%	84%	87%	91%	78%	87%	84%	88%	77%	86%	89%	82%	79%	96%	87%	78%
83.9%			85%	90%	86%	86%	84%	87%	76%	83%	81%	91%	80%	85%	84%	81%	81%	83%	81%	84%	83%	87%

Model Similarity Dendrogram

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