DTeval

A Collective Intelligence Project

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Analysis: Gd4 C10 Dtef Global Dialogues Gd4 Country%3Achina C10 - Run a306870...

Global Dialogues GD4 - China (10 context Qs)

DTEF: Predict response distributions for China. 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

Demographic & Cultural Knowledge

Best Models (Coverage)

1.Llama 4 Maverick
80.0%
2.Claude Sonnet 4
79.7%
3.Mistral Large 2411
79.6%
4.GPT 4.1 Mini
79.4%
5.Mistral Medium 3
78.8%

🔀 Least Similar Models

Claude Sonnet 4.5vsGemini 2.5 Flash

73.4% similarity

👯 Most Similar Models

GPT 4.1 NanovsGPT 4o Mini

87.0% similarity

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Macro Coverage Overview

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

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	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		8th 77.8%	9th 77.8%	2nd 79.7%	7th 78.0%	21st 66.2%	13th 76.7%	1st 80.0%	3rd 79.6%	5th 78.8%	11th 77.3%	4th 79.4%	17th 75.2%	6th 78.7%	12th 76.7%	15th 75.7%	10th 77.8%	19th 72.3%	20th 67.7%	14th 76.6%	18th 74.4%	16th 75.4%
74.0%		77%	77%	80%	80%	85%	51%	72%	81%	75%	82%	65%	60%	75%	61%	83%	78%	77%	66%	70%	77%	83%
65.5%			66%	64%	73%	70%	42%	58%	57%	64%	94%	83%	66%	87%	68%	84%	83%	67%	23%	61%	55%	45%
73.5%		74%	63%	68%	70%	45%	85%	83%	79%	69%	69%	84%	79%	73%	83%	72%	80%	75%	62%	79%	69%	82%
82.8%			86%	84%	91%	69%	84%	92%	92%	83%	90%	85%	80%	81%	82%	86%	84%	84%	67%	75%	71%	90%
54.6%			56%	57%	56%	63%	48%	60%	65%	61%	54%	52%	51%	61%	51%	53%	56%	37%	50%	46%	54%	61%
85.0%			91%	91%	81%	60%	83%	85%	87%	87%	88%	90%	95%	95%	95%	81%	81%	75%	65%	86%	93%	91%
69.2%		80%	87%	84%	84%	81%	58%	76%	61%	60%	60%	63%	56%	60%	56%	80%	76%	54%	70%	60%	77%	71%
77.5%			83%	88%	86%	83%	73%	79%	81%	75%	78%	72%	84%	70%	75%	76%	73%	80%	62%	84%	70%	77%
74.4%			87%	66%	76%	45%	81%	78%	76%	89%	85%	85%	70%	77%	78%	31%	71%	82%	75%	80%	82%	75%
79.2%			74%	81%	74%	65%	87%	81%	85%	72%	84%	85%	75%	82%	82%	73%	85%	89%	70%	75%	87%	79%
75.4%			80%	80%	74%	52%	80%	78%	76%	76%	74%	77%	75%	80%	76%	74%	78%	79%	81%	75%	70%	74%
70.0%		61%	81%	70%	57%	83%	89%	72%	71%	75%	63%	82%	65%	73%	78%	62%	66%	56%	63%	82%	61%	61%
81.2%			86%	86%	84%	56%	82%	83%	88%	84%	77%	85%	84%	83%	85%	79%	79%	80%	71%	82%	83%	87%
69.1%			87%	77%	77%	79%	59%	72%	60%	67%	71%	63%	58%	68%	63%	67%	80%	60%	48%	75%	69%	82%
67.5%			65%	75%	82%	16%	61%	72%	82%	59%	73%	79%	75%	71%	79%	47%	71%	71%	77%	71%	53%	71%
76.5%			77%	77%	87%	78%	76%	84%	76%	84%	91%	87%	59%	85%	75%	86%	83%	64%	78%	70%	72%	42%
79.2%		75%	79%	75%	79%	76%	84%	80%	83%	81%	75%	85%	81%	82%	76%	72%	71%	92%	81%	75%	76%	85%
85.2%		85%	85%	89%	88%	89%	83%	93%	92%	93%	81%	89%	72%	85%	72%	83%	84%	82%	90%	89%	81%	85%
65.5%		63%	60%	75%	63%	42%	73%	83%	84%	77%	60%	75%	71%	70%	71%	51%	61%	58%	60%	71%	51%	57%
88.6%		98%	93%	92%	94%	93%	65%	91%	89%	80%	90%	87%	89%	88%	90%	92%	90%	89%	92%	84%	83%	91%
75.5%		80%	71%	77%	76%	84%	71%	88%	81%	88%	71%	76%	59%	81%	61%	72%	76%	73%	79%	80%	74%	68%
84.4%		89%	86%	93%	91%	75%	93%	82%	88%	88%	85%	87%	80%	82%	83%	89%	90%	84%	83%	80%	83%	62%
77.7%			71%	92%	88%	60%	82%	81%	85%	87%	91%	71%	77%	95%	83%	83%	93%	64%	56%	45%	61%	89%
60.5%			70%	57%	56%	64%	56%	59%	59%	65%	61%	55%	65%	69%	51%	55%	57%	60%	74%	56%		60%
76.9%			79%	81%	79%	65%	83%	86%	89%	77%	79%	73%	81%	76%	59%	84%	85%	65%	67%	74%	75%	81%
90.2%		92%	91%	90%	90%	93%	93%	91%	70%	93%	92%	93%	93%	94%	93%	92%	89%	78%	83%	93%	96%	96%
87.8%		91%	88%	91%	92%	91%	82%	92%	89%	83%	92%	84%	85%	94%	83%	91%	94%	84%	85%	83%	83%	86%
81.3%		90%	88%	95%	93%	71%	55%	83%	89%	86%	86%	80%	76%	83%	76%	88%	87%	82%	76%	70%	76%	77%
72.8%		78%	71%	80%	85%	51%	67%	84%	83%	85%	80%	75%	76%	77%	66%	68%	75%	59%	50%	65%	73%	81%
71.2%		71%	67%	73%	74%	57%	69%	77%	78%	71%	74%	76%	69%	72%	75%	74%	72%	59%	54%	77%	71%	86%
82.1%			98%	88%	78%	62%	89%	90%	88%	84%	87%	92%	80%	89%	89%	84%	85%	61%	50%	88%		78%
78.4%			85%	83%	83%	60%	80%	67%	76%	74%	82%	79%	84%	79%	84%	91%	87%	57%	73%	82%	80%	82%
84.7%			88%	89%	85%	46%	88%	88%	85%	88%	88%	90%	81%	92%	83%	85%	85%	92%	92%	82%	76%	90%
70.7%			72%	67%	73%	78%	72%	81%	83%	79%	73%	73%	64%	75%	70%	82%	79%	51%	47%	66%	81%	48%
73.0%			81%	81%	69%	50%	84%	86%	80%	87%	65%	72%	81%	71%	81%	75%	64%	62%	52%	88%	74%	57%
77.4%			92%	89%	80%	22%	83%	85%	72%	79%	87%	80%	76%	90%	93%	84%	92%	59%	59%	67%		82%
81.0%			79%	88%	88%	79%	86%	84%	86%	79%	71%	80%	82%	81%	84%	81%	84%	76%	73%	85%	79%	76%
72.5%		73%	74%	75%	75%	88%	64%	51%	80%	54%	78%	88%	54%	70%	72%	87%	82%	71%	58%	72%		85%
79.1%			89%	83%	71%	57%	86%	86%	87%	86%	80%	88%	90%	84%	85%	79%	79%	66%	68%	68%	70%	81%
59.1%		65%	39%	48%	62%	43%	63%	45%	70%	52%	59%	85%	71%	45%	71%	78%	71%	49%	49%	71%	47%	59%
79.4%			90%	88%	94%	38%	84%	88%	93%	74%	80%	82%	82%	77%	80%	74%	84%	85%	63%	77%		75%
73.1%		75%	80%	81%	82%	65%	78%	72%	73%	75%	67%	78%	82%	67%	62%	73%	69%	63%	63%	73%	76%	82%
84.7%			88%	90%	84%	80%	83%	84%	79%	80%	87%	89%	84%	85%	83%	89%	87%	87%	89%	75%	85%	87%
70.7%			65%	61%	63%	73%	72%	84%	85%	81%	67%	68%	71%	72%	71%	61%	58%	86%	72%	71%		62%
73.9%			79%	76%	71%	80%	88%	89%	93%	70%	66%	70%	73%	76%	76%	68%	70%	64%	62%	79%	64%	65%
74.1%			72%	76%	78%	75%	69%	73%	74%	82%	78%	81%	65%	77%	64%	72%	83%	76%	63%	72%	78%	74%
78.1%			90%	85%	80%	85%	76%	88%	86%	83%	78%	77%	67%	87%	70%	75%	79%	67%	58%	77%	82%	73%
86.6%			95%	65%	64%	94%	89%	99%	95%	98%	81%	95%	89%	83%	82%	75%	74%	71%	88%	98%	97%	100%
83.8%		88%	86%	88%	85%	80%	90%	88%	88%	88%	72%	88%	88%	80%	88%	85%	85%	85%	71%	85%	80%	72%
62.0%		59%	51%	61%	56%	35%	83%	81%	66%	80%	52%	81%	74%	71%	85%	40%	47%	52%	51%	76%	57%	44%
72.4%		74%	67%	88%	72%	33%	67%	86%	90%	83%	83%	71%	79%	84%	67%	72%	76%	61%	59%	67%		68%
69.9%			65%	57%	84%	59%	58%	68%	67%	82%	84%	66%	55%	81%	71%	78%	78%	80%	43%	60%	76%	86%
77.8%		84%	77%	87%	89%	71%	77%	77%	80%	81%	80%	82%	54%	76%	74%	83%	81%	78%	76%	74%	76%	76%
70.8%			74%	70%	72%	85%	71%	78%	77%	76%	74%	75%	71%	75%	66%	90%	65%	55%	49%	75%	66%	51%
82.5%			84%	83%	79%	83%	80%	84%	84%	82%	83%	84%	79%	83%	83%	79%	81%	84%	88%	82%	84%	81%
86.6%		90%	89%	88%	88%	90%	84%	85%	83%	88%	89%	84%	82%	87%	85%	90%	89%	91%	86%	81%	83%	87%
52.3%		52%	41%	51%	44%	62%	37%	52%	52%	65%	68%	54%	34%	59%	47%	67%	73%	60%	52%	41%	47%	41%
79.3%			84%	89%	80%	62%	88%	91%	77%	80%	66%	83%	82%	82%	86%	68%	71%	76%	76%	89%	80%	75%
65.5%			68%	65%	78%	66%	59%	72%	69%	72%	76%	76%	53%	75%	63%	78%	78%	42%	39%	49%	66%	66%
62.1%			69%	69%	65%	42%	78%	71%	71%	59%	56%	64%	60%	66%	58%	59%	54%	56%	45%	71%	59%	70%
82.6%			87%	88%	89%	56%	79%	84%	88%	88%	85%	89%	76%	75%	76%	84%	90%	85%	79%	85%	82%	87%
72.9%		84%	81%	81%	84%	74%	41%	63%	80%	71%	80%	52%	58%	78%	56%	82%	82%	74%	88%	71%	73%	78%
86.3%		93%	88%	97%	82%	86%	94%	99%	95%	95%	93%	89%	73%	87%	73%	85%	96%	73%	65%	85%	73%	92%
82.2%			87%	87%	76%	42%	93%	88%	88%	79%	74%	91%	86%	91%	88%	72%	83%	86%	70%	89%	88%	86%
78.7%			89%	90%	87%	94%	82%	79%	71%	75%	86%	75%	77%	93%	83%	79%	90%	78%	67%	45%	55%	79%
77.0%		86%	75%	89%	92%	40%	87%	87%	82%	61%	80%	78%	80%	80%	81%	85%	70%	74%	69%	77%	67%	76%
71.4%		83%	79%	79%	84%	44%	52%	75%	83%	74%	84%	68%	64%	77%	56%	74%	78%	74%	71%	62%	69%	69%
71.8%			80%	75%	74%	75%	76%	69%	61%	67%	77%	77%	65%	69%	68%	75%	73%	70%	68%	72%		73%
75.4%		73%	80%	74%	74%	72%	78%	72%	75%	81%	73%	83%	73%	76%	70%	80%	80%	66%	66%	86%	74%	77%
82.2%			67%	75%	84%	82%	81%	88%	76%	92%	92%	76%	81%	83%	86%	86%	86%	83%	77%	81%		85%
82.1%			81%	85%	85%	61%	90%	89%	83%	89%	82%	83%	84%	88%	85%	84%	88%	78%	73%	85%	63%	85%
85.3%			81%	93%	93%	47%	81%	90%	92%	88%	83%	92%	79%	89%	79%	89%	95%	94%	78%	91%	82%	89%
83.0%		92%	90%	89%	84%	33%	90%	82%	85%	85%	80%	90%	82%	81%	77%	88%	87%	86%	83%	84%	86%	88%
71.6%			66%	82%	66%	75%	61%	74%	77%	84%	80%	68%	56%	83%	72%	72%	76%	59%	70%	58%	75%	78%
76.7%			73%	77%	66%	77%	82%	82%	83%	81%	72%	77%	79%	80%	75%	71%	71%	80%	82%	81%	83%	62%
67.2%		69%	69%	63%	66%	50%	84%	77%	74%	70%	58%	78%	77%	61%	82%	61%	62%	51%	42%	77%	71%	69%
81.0%			79%	90%	79%	48%	92%	85%	87%	85%	72%	82%	89%	80%	91%	77%	82%	84%	83%	87%	66%	81%
81.5%		82%	81%	89%	85%	72%	68%	84%	90%	82%	86%	85%	76%	83%	81%	90%	87%	76%	67%	83%	77%	88%
74.3%			80%	81%	79%	49%	78%	72%	73%	71%	75%	76%	72%	79%	79%	78%	79%	73%	76%	73%	64%	78%
82.0%			86%	87%	82%	82%	82%	75%	86%	85%	78%	85%	78%	85%	88%	77%	81%	79%	80%	87%	78%	80%
82.1%		81%	86%	89%	85%	85%	87%	90%	83%	88%	86%	83%	80%	83%	82%	74%	75%	78%	57%	79%	89%	85%
67.8%		54%	70%	66%	66%	68%	95%	71%	67%	72%	62%	77%	75%	69%	83%	53%	64%	56%	56%	87%	56%	56%
59.2%		56%	54%	62%	58%	32%	76%	68%	66%	55%	56%	62%	68%	55%	71%	49%	57%	65%	44%	68%	61%	60%
83.9%		80%	82%	86%	87%	90%	81%	84%	79%	87%	85%	80%	89%	83%	86%	83%	85%	85%	76%	86%	81%	87%
78.8%			81%	86%	69%	70%	95%	93%	85%	94%	67%	83%	77%	80%	90%	62%	75%	72%	70%	85%	71%	72%
81.2%			86%	86%	76%	61%	86%	85%	84%	85%	75%	89%	80%	88%	82%	73%	82%	82%	78%	88%	76%	82%
77.1%			78%	82%	77%	77%	84%	79%	80%	80%	73%	80%	80%	77%	84%	72%	69%	67%	61%	83%	75%	85%
63.9%		58%	71%	62%	53%	37%	80%	71%	59%	67%	61%	82%	80%	64%	81%	60%	57%	48%	56%	81%	56%	57%
81.4%			81%	86%	70%	86%	80%	76%	90%	91%	83%	81%	57%	93%	72%	91%	91%	88%	58%	96%	82%	76%
79.1%		74%	93%	86%	86%	94%	75%	95%	85%	92%	88%	97%	75%	90%	83%	55%	56%	66%	62%	76%	65%	69%
78.1%			82%	80%	86%	75%	72%	81%	79%	79%	86%	81%	76%	81%	77%	77%	82%	72%	66%	71%	76%	83%
79.1%			72%	86%	76%	82%	52%	78%	87%	85%	68%	89%	76%	83%	77%	77%	88%	90%	74%	82%	76%	85%
63.8%		84%	77%	73%	84%	41%	52%	56%	55%	56%	62%	55%	54%	63%	61%	69%	69%	66%	58%	89%	57%	58%
73.8%		73%	73%	86%	77%	67%	73%	87%	73%	82%	71%	77%	89%	77%	81%	62%	65%	60%	61%	69%	78%	69%
68.8%			73%	75%	62%	43%	88%	75%	74%	71%	55%	66%	75%	67%	76%	60%	62%	63%	82%	73%		68%
87.3%			84%	93%	89%	88%	91%	81%	84%	85%	87%	88%	88%	86%	85%	92%	88%	89%	89%	87%	83%	90%
68.1%		66%	57%	66%	78%	41%	71%	67%	73%	63%	74%	79%	75%	70%	71%	70%	67%	70%	57%	74%	78%	64%
76.9%		89%	73%	75%	75%	40%	92%	90%	79%	73%	64%	85%	91%	69%	91%	71%	74%	82%	66%	87%	74%	74%
73.7%			74%	80%	84%	79%	52%	80%	72%	77%	89%	81%	76%	81%	66%	80%	80%	79%	63%	59%	69%	53%
74.4%			73%	68%	69%	77%	79%	77%	81%	64%	67%	85%	72%	80%	71%	62%	72%	65%	71%	90%		91%
74.0%		81%	72%	78%	81%	28%	79%	81%	83%	79%	86%	75%	72%	80%	69%	64%	70%	88%	70%	61%	75%	82%
89.8%		91%	91%	93%	93%	93%	88%	78%	86%	88%	91%	87%	94%	85%	93%	96%	96%	91%	86%	89%	84%	92%
81.5%		93%	85%	85%	94%	89%	79%	86%	85%	82%	77%	69%	94%	70%	72%	88%	83%	50%	73%	86%	86%	86%
76.5%		66%	67%	72%	72%	91%	70%	81%	86%	83%	73%	82%	82%	77%	79%	64%	72%	73%	80%	85%	79%	72%
80.0%		78%	82%	89%	76%	34%	67%	90%	93%	89%	80%	88%	90%	90%	84%	74%	73%	86%	74%	77%	90%	76%
80.8%		77%	63%	67%	80%	69%	73%	85%	82%	87%	94%	90%	90%	75%	85%	94%	87%	69%	78%	90%	93%	69%
72.2%			81%	69%	75%	72%	62%	73%	73%	83%	83%	71%	51%	74%	66%	83%	87%	56%	60%	68%	90%	68%
70.0%			64%	69%	77%	72%	54%	71%	66%	82%	91%	81%	65%	85%	81%	72%	89%	68%	37%	60%	72%	44%
74.6%		77%	82%	81%	71%	87%	87%	84%	83%	68%	82%	66%	68%	79%	77%	72%	69%	70%	63%	82%	63%	56%
81.4%			85%	90%	87%	52%	85%	82%	83%	84%	83%	90%	85%	78%	80%	82%	84%	83%	78%	81%	72%	84%
81.5%			77%	88%	79%	84%	85%	88%	89%	90%	79%	78%	83%	83%	84%	83%	79%	71%	65%	87%		76%
76.0%		68%	79%	83%	73%	18%	83%	85%	85%	85%	79%	84%	82%	86%	86%	75%	78%	66%	77%	77%	67%	79%
89.4%		90%	86%	90%	90%	90%	90%	89%	86%	88%	94%	88%	93%	92%	82%	93%	90%	87%	83%	90%	92%	94%
78.8%			82%	86%	74%	55%	88%	83%	82%	79%	71%	85%	81%	85%	83%	68%	78%	81%	70%	84%	84%	78%
78.0%		86%	71%	84%	81%	82%	81%	83%	69%	75%	81%	78%	75%	60%	77%	86%	84%	73%	69%	61%	89%	93%
81.6%			82%	83%	77%	87%	94%	80%	79%	78%	71%	84%	83%	81%	89%	81%	72%	91%	65%	81%	86%	87%

Model Similarity Dendrogram

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