DTeval

A Collective Intelligence Project

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Analysis: Gd4 C10 Dtef Global Dialogues Gd4 AgeGroup%3A36 45 C10 - Run c842ffd...

Global Dialogues GD4 - 36-45 (10 context Qs)

DTEF: Predict response distributions for 36-45. 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
86.7%
2.GPT 5
84.6%
3.GPT 4.1
84.1%
4.GPT 5.1
84.0%
5.Claude Sonnet 4.5
83.6%

🔀 Least Similar Models

Claude Sonnet 4.5vsGPT OSS 20b

73.1% similarity

👯 Most Similar Models

Claude Haiku 4.5vsClaude Sonnet 4

88.5% 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 86.7%	9th 82.1%	7th 83.0%	5th 83.6%	16th 79.4%	18th 78.5%	12th 80.9%	11th 81.5%	10th 81.6%	3rd 84.1%	8th 82.8%	20th 77.4%	6th 83.2%	14th 79.6%	2nd 84.6%	4th 84.0%	17th 78.6%	21st 74.8%	15th 79.5%	19th 77.6%	13th 80.6%
71.6%		82%	72%	73%	79%	64%	51%	65%	80%	74%	71%	60%	74%	76%	57%	87%	80%	84%	51%	75%	71%	78%
68.2%			62%	69%	75%	79%	39%	56%	51%	70%	90%	72%	61%	88%	66%	90%	84%	61%	69%	58%	79%	44%
86.0%		94%	85%	92%	94%	67%	84%	91%	80%	83%	86%	94%	80%	89%	80%	93%	90%	91%	85%	77%	88%	84%
81.0%			85%	86%	86%	74%	85%	84%	87%	80%	84%	82%	81%	81%	82%	83%	82%	75%	73%	75%	76%	78%
62.9%			69%	70%	73%	66%	57%	69%	69%	68%	67%	61%	55%	65%	62%	62%	65%	47%	54%	60%	53%	66%
81.5%			84%	79%	84%	91%	78%	81%	79%	80%	85%	87%	78%	90%	92%	88%	86%	71%	69%	82%	63%	82%
78.0%		87%	97%	89%	93%	92%	71%	77%	72%	78%	70%	78%	81%	70%	69%	84%	76%	70%	69%	68%	73%	75%
84.0%			77%	70%	66%	76%	86%	84%	91%	87%	84%	88%	89%	89%	86%	92%	91%	85%	90%	90%	77%	83%
85.9%			84%	83%	81%	68%	95%	93%	89%	72%	96%	89%	91%	90%	92%	86%	90%	86%	82%	84%	85%	83%
75.0%		66%	66%	66%	60%	60%	89%	77%	77%	64%	70%	73%	84%	84%	85%	73%	66%	86%	82%	84%	74%	88%
83.1%			85%	80%	83%	89%	78%	80%	79%	83%	84%	83%	83%	88%	86%	86%	83%	83%	86%	81%	80%	83%
86.3%		95%	96%	92%	92%	84%	94%	78%	87%	90%	96%	86%	71%	81%	88%	92%	88%	76%	76%	85%	76%	89%
85.4%			88%	82%	83%	87%	85%	77%	88%	91%	87%	84%	89%	87%	77%	88%	89%	89%	81%	86%	83%	87%
63.8%			71%	70%	71%	60%	66%	67%	47%	64%	62%	71%	56%	54%	55%	66%	79%	57%	58%	67%	62%	73%
72.8%		75%	66%	76%	76%	30%	79%	69%	83%	66%	87%	76%	75%	75%	71%	77%	77%	75%	76%	64%	76%	79%
84.2%			78%	88%	96%	91%	76%	87%	80%	91%	95%	87%	68%	90%	77%	94%	95%	71%	80%	69%	90%	81%
87.2%		87%	92%	87%	86%	93%	96%	90%	93%	89%	87%	92%	77%	83%	79%	81%	83%	89%	86%	84%	84%	94%
87.6%		90%	86%	83%	89%	92%	89%	87%	91%	89%	93%	88%	86%	95%	75%	89%	89%	85%	95%	92%	75%	82%
86.5%		89%	86%	81%	90%	88%	92%	85%	80%	87%	92%	98%	83%	85%	94%	90%	91%	83%	69%	86%	74%	93%
77.2%		80%	79%	72%	72%	72%	66%	82%	77%	68%	77%	78%	83%	77%	82%	83%	79%	82%	75%	77%	82%	78%
87.9%		88%	86%	86%	89%	83%	92%	88%	91%	95%	92%	89%	82%	94%	72%	91%	94%	80%	85%	93%	85%	90%
89.2%		85%	95%	89%	89%	87%	92%	86%	92%	91%	92%	92%	82%	88%	83%	90%	95%	93%	79%	84%	94%	95%
73.5%			68%	70%	74%	62%	77%	73%	78%	80%	80%	76%	72%	88%	76%	80%	88%	54%	67%	53%	72%	81%
73.0%			83%	92%	92%	92%	57%	78%	70%	81%	76%	66%	56%	70%	62%	66%	73%	72%	72%	57%	70%	76%
81.6%			86%	83%	82%	72%	86%	86%	78%	85%	82%	87%	82%	85%	81%	84%	86%	79%	59%	89%	78%	82%
85.8%		88%	84%	91%	90%	88%	91%	83%	80%	87%	91%	88%	88%	89%	88%	82%	81%	70%	73%	88%	88%	94%
79.6%		85%	86%	85%	83%	83%	74%	85%	74%	75%	86%	74%	74%	82%	74%	83%	85%	74%	75%	79%	76%	80%
83.2%		90%	87%	78%	78%	83%	59%	81%	91%	91%	87%	84%	87%	83%	77%	90%	90%	84%	76%	82%	87%	82%
88.1%		89%	93%	94%	88%	95%	83%	95%	93%	90%	96%	89%	89%	95%	84%	90%	88%	87%	72%	74%	81%	85%
86.0%		95%	87%	96%	95%	93%	73%	84%	92%	82%	88%	85%	83%	88%	70%	93%	88%	80%	88%	85%	81%	79%
78.9%			80%	80%	83%	78%	77%	83%	74%	82%	91%	85%	83%	91%	82%		92%	67%	50%	86%	62%	74%
82.1%			77%	73%	73%	76%	88%	77%	84%	85%	83%	87%	78%	88%	83%	81%	89%	92%	90%	88%	79%	70%
72.8%			77%	72%	81%	90%	67%	68%	67%	86%	77%	74%	72%	65%	73%	82%	71%	68%	64%	66%	63%	73%
73.4%			65%	80%	87%	93%	68%	77%	72%	80%	85%	79%	60%	77%	62%	75%	80%	81%	48%	53%		73%
80.7%			81%	89%	88%	82%	86%	87%	81%	79%	70%	79%	86%	78%	85%	77%	68%	73%	75%	90%	81%	80%
78.2%			78%	76%	83%	83%	74%	73%	75%	78%	85%	78%	65%	84%	80%	92%	89%	68%	69%	78%		77%
87.2%			85%	82%	70%	82%	95%	92%	89%	85%	91%	90%	89%	91%	90%	85%	83%	85%	91%	91%	88%	90%
84.7%		92%	88%	89%	88%	87%	83%	78%	86%	81%	86%	78%	83%	78%	82%	92%	86%	85%	85%	80%	86%	86%
79.2%			76%	79%	78%	81%	78%	78%	76%	79%	86%	78%	65%	87%	85%	89%	84%	80%	81%	85%	67%	71%
82.1%		81%	75%	84%	83%	81%	84%	67%	83%	79%	75%	93%	86%	74%	83%	87%	87%	81%	75%	90%	87%	90%
81.0%			92%	95%	92%	70%	77%	90%	89%	89%	92%	97%	70%	91%	86%	89%	84%	87%	68%	70%	0%	92%
79.9%		82%	87%	89%	89%	80%	89%	87%	84%	76%	76%	91%	87%	67%	67%	80%	79%	60%	59%	87%	85%	77%
71.7%			67%	64%	66%	67%	67%	77%	72%	74%	79%	77%	80%	73%	75%	75%	81%	64%	68%	69%	69%	69%
80.4%			78%	88%	88%	78%	81%	87%	94%	84%	87%	71%	84%	84%	79%	68%	76%	84%	88%	71%	84%	54%
80.7%			82%	87%	90%	84%	84%	87%	87%	76%	87%	75%	67%	81%	82%	85%	84%	74%	74%	78%	65%	86%
87.0%			80%	90%	88%	89%	80%	95%	89%	91%	91%	93%	80%	94%	86%	92%	87%	77%	85%	90%	85%	78%
82.1%			95%	97%	83%	91%	76%	86%	84%	82%	82%	76%	46%	84%	82%	88%	83%	73%	86%	77%	84%	87%
87.6%			92%	92%	95%	79%	76%	85%	86%	78%	91%	92%	75%	87%	92%	95%	99%	86%	86%	92%	76%	98%
86.2%		83%	84%	87%	87%	88%	87%	89%	83%	87%	86%	87%	88%	66%	80%	96%	81%	96%	88%	85%	93%	90%
85.8%		95%	89%	89%	90%	85%	89%	89%	77%	88%	92%	87%	87%	88%	82%	85%	91%	78%	61%	85%	80%	94%
87.5%		93%	92%	86%	86%	92%	86%	89%	86%	85%	88%	93%	81%	92%	88%	92%	93%	88%	68%	85%	86%	89%
74.0%		91%	69%	82%	86%	33%	70%	33%	87%	88%	79%	66%	62%	80%	72%	88%	84%	84%	79%	60%	69%	93%
80.1%		84%	85%	85%	84%	93%	70%	71%	85%	87%	78%	80%	78%	77%	67%	82%	79%	80%	86%	85%	73%	73%
71.4%			71%	72%	72%	90%	61%	71%	65%	80%	68%	70%	63%	75%	64%	82%	75%	75%	82%	64%	64%	64%
83.0%			76%	81%	80%	73%	84%	77%	88%	85%	84%	85%	88%	86%	86%	83%	86%	80%	83%	89%	78%	87%
77.3%		84%	77%	79%	84%	69%	71%	72%	80%	78%	84%	75%	73%	81%	72%	83%	78%	82%	78%	73%	75%	76%
67.6%		75%	55%	79%	65%	71%	49%	74%	68%	80%	87%	65%	57%	76%	62%	88%	85%	58%	59%	48%	62%	57%
88.4%			95%	87%	88%	89%	92%	83%	91%	87%	94%	91%	90%	95%	90%	93%	95%	92%	67%	92%	85%	72%
65.2%			65%	77%	79%	88%	50%	69%	73%	72%	77%	69%	50%	71%	57%	74%	79%	54%	32%	52%	59%	57%
80.5%			85%	87%	89%	92%	91%	83%	90%	65%	83%	80%	68%	81%	81%	77%	79%	81%	56%	87%	71%	83%
82.0%		91%	87%	90%	94%	37%	83%	90%	86%	82%	87%	90%	70%	80%	83%	93%	80%	80%	64%	81%	81%	94%
78.1%		87%	80%	75%	80%	81%	66%	68%	82%	79%	78%	66%	79%	81%	66%	84%	82%	81%	83%	80%	80%	82%
86.0%		91%	89%	93%	94%	77%	89%	89%	89%	87%	89%	88%	78%	92%	82%	93%	94%	73%	64%	94%	81%	81%
81.0%			84%	80%	80%	67%	83%	79%	79%	83%	84%	82%	74%	87%	86%	87%	80%	82%	81%	84%	79%	80%
76.6%			72%	81%	78%	89%	76%	78%	71%	72%	84%	72%	62%	88%	80%	90%	90%	66%	63%	85%	60%	75%
89.4%		97%	95%	89%	97%	87%	87%	96%	92%	90%	91%	96%	84%	90%	86%	95%	92%	89%	81%	78%	73%	92%
81.5%		88%	90%	80%	83%	82%	64%	82%	99%	86%	82%	80%	70%	79%	66%	92%	87%	80%	85%	74%	81%	82%
77.9%			85%	82%	77%	67%	91%	77%	73%	75%	75%	86%	73%	76%	79%	86%	79%	84%	76%	64%	73%	80%
78.8%		80%	79%	82%	82%	81%	77%	83%	79%	79%	86%	74%	71%	79%	75%	78%	79%	82%	82%	72%	76%	79%
84.8%			87%	85%	76%	76%	91%	87%	70%	79%	79%	95%	88%	85%	91%	86%	86%	87%	98%	87%	80%	83%
87.5%			89%	94%	95%	85%	94%	91%	85%	81%	94%	86%	77%	86%	90%	91%	91%	88%	77%	87%	85%	84%
82.9%			85%	82%	73%	63%	93%	87%	91%	71%	84%	82%	83%	90%	90%	75%	80%	90%	84%	88%	77%	90%
87.2%		97%	96%	91%	91%	65%	84%	83%	91%	96%	86%	99%	89%	94%	82%	89%	93%	80%	88%	84%	65%	88%
80.5%		82%	84%	90%	85%	81%	60%	85%	85%	93%	90%	75%	64%	91%	79%	83%	87%	75%	70%	61%		89%
82.8%			78%	89%	83%	85%	88%	85%	85%	74%	86%	85%	83%	83%	80%	89%	84%	82%	75%	80%	79%	83%
79.8%		84%	78%	82%	85%	49%	90%	85%	74%	80%	73%	86%	85%	81%	88%	83%	72%	84%	79%	78%	79%	81%
85.8%			86%	87%	87%	88%	85%	85%	87%	87%	88%	88%	80%	86%	88%	91%	85%	79%	85%	89%	78%	88%
86.1%		94%	92%	81%	83%	91%	73%	82%	85%	84%	82%	89%	89%	91%	79%	93%	88%	90%	77%	89%	92%	85%
77.5%			82%	74%	79%	50%	79%	75%	79%	81%	81%	80%	78%	85%	83%	81%	85%	74%	86%	84%	56%	78%
81.9%			79%	86%	87%	72%	86%	81%	85%	85%	81%	88%	73%	84%	84%	89%	84%	77%	76%	86%	74%	81%
88.5%		84%	88%	85%	85%	85%	87%	83%	90%	93%	96%	89%	95%	89%	96%	90%	94%	89%	84%	85%	95%	77%
87.3%		89%	93%	90%	88%	92%	93%	94%	87%	92%	92%	92%	85%	93%	91%	84%	89%	72%	75%	88%	77%	77%
79.0%		79%	86%	85%	81%	62%	86%	84%	88%	68%	76%	87%	83%	87%	84%	75%	74%	66%	65%	83%	83%	76%
87.3%		86%	93%	86%	92%	85%	88%	83%	84%	98%	83%	89%	92%	77%	90%	86%	88%	96%	66%	87%	90%	94%
83.7%			91%	87%	89%	88%	80%	88%	84%	85%	87%	83%	70%	81%	83%	88%	83%	83%	82%	82%	86%	73%
78.2%			80%	76%	78%	82%	83%	76%	75%	75%	78%	74%	79%	82%	74%	85%	83%	68%	76%	80%	80%	80%
77.0%			83%	87%	84%	76%	81%	80%	80%	73%	73%	83%	65%	75%	80%	73%	71%	78%	59%	84%	76%	80%
74.7%		74%	76%	76%	77%	80%	74%	73%	70%	75%	77%	75%	71%	75%	84%	69%	81%	74%	63%	77%	77%	71%
78.7%			87%	91%	86%	83%	68%	84%	88%	82%	81%	77%	47%	89%	74%	83%	83%	67%	52%	86%	86%	80%
82.0%		93%	92%	93%	78%	98%	80%	86%	81%	81%	93%	97%	75%	84%	78%	62%	64%	78%	70%	93%	74%	71%
73.0%			69%	64%	68%	81%	58%	75%	72%	78%	72%	73%	80%	74%	71%	70%	74%	77%	75%	75%	83%	72%
74.0%			67%	84%	87%	80%	50%	61%	77%	71%	79%	72%	68%	72%	57%	87%	90%	85%	90%	59%	56%	88%
74.9%		83%	86%	67%	82%	45%	72%	65%	67%	67%	62%	67%	81%	73%	70%	79%	85%	90%	80%	96%	81%	75%
87.0%		92%	88%	85%	84%	96%	74%	95%	93%	92%	90%	94%	92%	93%	90%	86%	86%	81%	77%	75%	90%	73%
69.1%			76%	72%	71%	75%	81%	76%	67%	70%	67%	70%	70%	68%	70%	67%	61%	59%	63%	66%	60%	73%
82.4%			78%	78%	75%	86%	86%	77%	86%	84%	83%	86%	80%	86%	80%	87%	83%	84%	83%	85%	78%	83%
82.5%		79%	71%	88%	91%	74%	87%	75%	77%	66%	85%	87%	92%	82%	95%	86%	85%	86%	67%	86%	86%	88%
84.9%		93%	75%	92%	86%	58%	92%	92%	84%	76%	75%	87%	95%	82%	90%	88%	86%	90%	81%	93%	84%	84%
76.6%			80%	89%	90%	95%	54%	81%	72%	70%	93%	85%	58%	87%	79%	87%	84%	55%	66%	63%	71%	74%
82.8%			86%	91%	88%	88%	85%	79%	81%	78%	90%	93%	72%	90%	80%	72%	86%		64%	84%	81%	85%
79.3%		87%	73%	84%	90%	75%	73%	77%	79%	87%	92%	72%	68%	83%	72%	87%	89%	69%	89%	63%	65%	92%
87.5%		88%	84%	82%	87%	78%	86%	77%	93%	96%	86%	94%	95%	86%	97%	90%	78%	71%	93%	85%	96%	95%
82.0%		94%	82%	89%	93%	91%	87%	78%	89%	88%	69%	69%	92%	59%	78%	80%	88%	72%	65%	92%	80%	87%
77.0%		81%	71%	65%	71%	76%	59%	68%	69%	69%	86%	78%	74%	79%	81%	82%	83%	95%	86%	74%	85%	85%
85.3%		82%	84%	79%	80%	86%	85%	84%	86%	86%	86%	88%	90%	92%	91%	88%	91%	85%	78%	86%	87%	77%
86.0%		79%	89%	75%	78%	77%	75%	92%	74%	87%	98%	98%	86%	95%	70%	91%	95%	93%	89%	84%	93%	89%
81.1%		95%	88%	90%	95%	84%	66%	80%	80%	89%	93%	83%	71%	83%	78%	91%	87%	87%	34%	84%	89%	56%
69.4%			65%	65%	80%	79%	37%	67%	60%	78%	88%	70%	60%	80%	75%	81%	80%	64%	68%	55%	77%	58%
81.0%		79%	73%	85%	82%	91%	84%	91%	88%	83%	86%	85%	78%	83%	79%	76%	72%	76%	74%	84%	85%	66%
81.2%			81%	85%	87%	90%	77%	78%	77%	86%	88%	84%	80%	87%	74%	89%	78%	70%	84%	74%	73%	82%
89.0%			94%	84%	81%	92%	94%	90%	92%	88%	92%	88%	85%	91%	85%	92%	91%	89%	89%	91%	83%	90%
85.0%		83%	85%	85%	89%	86%	84%	82%	86%	89%	90%	93%	77%	90%	91%	87%	91%	77%	73%	79%	79%	88%
88.8%		99%	82%	93%	88%	89%	76%	89%	92%	96%	91%	89%	88%	94%	82%	91%	94%	87%	85%	85%		86%
84.8%			88%	83%	81%	63%	91%	82%	82%	88%	87%	91%	86%	90%	89%	91%	88%	85%	70%	85%	92%	84%
90.9%		96%	99%	92%	98%	88%	77%	98%	93%	90%	85%	90%	93%	93%	73%	98%	95%	84%	95%	95%	93%	83%
80.9%			84%	86%	86%	86%	86%	85%	79%	75%	78%	85%	79%	79%	83%	78%	78%	81%	64%	83%	79%	84%

Model Similarity Dendrogram

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