DTeval

A Collective Intelligence Project

Loading blueprint versions...

Please wait while we gather all the unique runs for this blueprint.

Analysis: Gd4 C10 Dtef Global Dialogues Gd4 AgeGroup%3A26 35 C10 - Run a1c02ff...

Global Dialogues GD4 - 26-35 (10 context Qs)

DTEF: Predict response distributions for 26-35. 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
86.1%
2.Claude Sonnet 4.5
84.9%
3.Claude Sonnet 4
84.4%
4.GPT 5
84.3%
5.GPT 5.1
84.0%

🔀 Least Similar Models

Claude Sonnet 4.5vsGPT OSS 20b

74.8% similarity

👯 Most Similar Models

Claude Sonnet 4.5vsClaude Sonnet 4

89.4% similarity

Select Prompt:

Macro Coverage Overview

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

Pro Tip

Click on any result cell to open a detailed view.

Advanced view

Highlight best performers

Sort prompts by

Sort models by

Color Scale - Simplified View (Avg. Coverage)

Perfect

Excellent

Good

Fair

Poor

Bad

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 86.1%	7th 83.8%	3rd 84.4%	2nd 84.9%	9th 83.1%	17th 79.8%	13th 81.4%	12th 81.5%	11th 82.1%	6th 83.9%	10th 82.6%	20th 77.1%	8th 83.5%	15th 80.3%	4th 84.3%	5th 84.0%	18th 78.0%	21st 72.5%	16th 80.3%	19th 77.1%	14th 81.0%
76.2%		89%	75%	84%	89%	72%	55%	64%	81%	74%	78%	71%	74%	79%	61%	82%	78%	85%	79%	79%	72%	79%
66.2%			67%	65%	73%	76%	43%	61%	56%	68%	88%	73%	64%	79%	67%	83%	79%	50%	49%	61%	56%	66%
81.2%		84%	79%	89%	89%	73%	81%	86%	80%	86%	79%	83%	77%	81%	78%	88%	82%	79%	77%	75%	78%	81%
84.2%			88%	91%	93%	86%	90%	87%	92%	84%	90%	86%	83%	89%	87%	91%	88%	67%	55%	73%	73%	90%
69.0%			76%	78%	81%	77%	65%	71%	72%	72%	72%	62%	68%	72%	66%	75%	73%	55%	51%	62%	57%	74%
80.9%			83%	79%	85%	89%	78%	79%	77%	81%	91%	85%	81%	92%	92%	91%	92%	70%	55%	82%	55%	80%
66.4%		76%	85%	79%	84%	80%	57%	76%	60%	62%	58%	68%	54%	54%	55%	75%	68%	57%	62%	58%	62%	64%
85.4%		91%	79%	86%	68%	77%	88%	89%	90%	81%	83%	84%	86%	91%	87%	91%	83%	91%	90%	88%	82%	88%
84.6%		91%	88%	82%	80%	87%	92%	89%	88%	83%	88%	96%	77%	82%	85%	75%	91%	88%	85%	83%	80%	67%
76.0%		68%	66%	73%	70%	61%	88%	80%	79%	66%	69%	73%	85%	91%	94%	74%	66%	82%	69%	86%	62%	95%
83.6%			83%	82%	88%	87%	84%	81%	75%	82%	86%	86%	83%	87%	87%	88%	86%	82%	77%	84%	78%	86%
86.2%		86%	96%	92%	95%	95%	95%	77%	82%	88%	91%	87%	70%	87%	87%	93%	87%	77%	75%	81%	76%	93%
83.8%		86%	86%	80%	81%	83%	84%	75%	88%	89%	86%	85%	87%	87%	79%	88%	81%	84%	82%	84%	80%	84%
65.3%			88%	71%	59%	61%	66%	68%	46%	64%	61%	59%	73%	64%	55%	70%	76%	66%	58%	62%	62%	78%
74.5%		79%	64%	72%	75%	70%	63%	67%	78%	65%	86%	77%	80%	66%	75%	81%	82%	76%	84%	71%	72%	82%
79.5%			81%	86%	90%	96%	70%	85%	77%	90%	90%	79%	58%	81%	74%	90%	93%	71%	48%	70%	85%	75%
85.9%		86%	91%	86%	88%	89%	90%	85%	92%	87%	85%	90%	75%	79%	90%	78%	83%	91%	90%	84%	75%	89%
90.8%		89%	91%	91%	92%	91%	91%	93%	97%	94%	95%	90%	86%	92%	81%	85%	92%	94%	87%	94%	93%	88%
83.7%		85%	82%	80%	84%	85%	86%	82%	77%	82%	85%	87%	85%	84%	92%	85%	86%	78%	84%	81%	82%	85%
78.9%		82%	82%	76%	80%	81%	70%	83%	80%	77%	81%	76%	80%	78%	87%	80%	79%	81%	79%	78%	72%	74%
90.5%		92%	96%	96%	96%	89%	82%	90%	95%	94%	98%	85%	77%	96%	74%	95%	96%	94%	81%	91%	89%	94%
85.3%		85%	95%	88%	79%	76%	89%	83%	87%	93%	79%	87%	85%	86%	77%	86%	88%	92%	62%	88%	92%	94%
82.5%			73%	83%	86%	91%	87%	82%	78%	86%	87%	79%	63%	95%	86%	91%	95%	71%	87%	53%	90%	88%
60.0%			65%	66%	65%	72%	59%	67%	56%	61%	64%	54%	55%	66%	46%	53%	61%	61%	57%	48%	66%	57%
85.5%			90%	91%	92%	86%	86%	83%	92%	84%	93%	84%	80%	90%	79%	97%	91%	64%	75%	81%	85%	86%
88.5%		92%	90%	93%	92%	90%	97%	84%	78%	83%	88%	87%	90%	91%	90%	91%	81%	76%	90%	90%	90%	96%
87.1%		94%	89%	91%	90%	95%	80%	97%	84%	81%	92%	84%	80%	93%	79%	87%	94%	84%	82%	81%	84%	88%
84.7%		94%	89%	82%	81%	83%	63%	83%	96%	84%	87%	86%	85%	89%	81%	98%	95%	88%	77%	76%	67%	95%
84.9%		95%	91%	91%	88%	88%	77%	92%	87%	85%	92%	87%	77%	86%	79%	92%	92%	73%	74%	75%	75%	86%
84.2%		89%	90%	92%	87%	89%	77%	82%	88%	85%	86%	81%	82%	85%	81%	92%	86%	78%	81%	85%	75%	77%
86.3%			83%	88%	86%	88%	92%	87%	83%	86%	91%	87%	84%	92%	93%	83%	94%	78%	83%	86%	71%	91%
85.2%			86%	91%	87%	83%	77%	84%	83%	89%	84%	85%	87%	84%	84%	90%	92%	86%	77%	90%	77%	88%
77.9%			77%	76%	93%	88%	71%	73%	70%	82%	84%	79%	63%	72%	65%	91%	89%	73%	79%	67%	79%	86%
69.3%			70%	67%	84%	93%	62%	69%	76%	73%	73%	68%	59%	73%	60%	80%	81%	48%	51%	61%	83%	56%
80.8%			84%	84%	88%	86%	83%	82%	83%	88%	68%	73%	83%	79%	84%	72%	79%	70%	82%	88%	79%	82%
83.2%			85%	88%	88%	85%	87%	83%	76%	84%	93%	78%	66%	91%	84%	92%	90%	75%	77%	83%	79%	81%
86.5%			94%	85%	77%	87%	86%	86%	79%	91%	90%	75%	92%	90%	91%	96%	90%	82%	79%	93%	79%	87%
85.9%		86%	92%	92%	87%	82%	85%	64%	90%	83%	92%	89%	84%	79%	86%	88%	93%	86%	79%	90%	88%	89%
85.4%			84%	88%	85%	91%	87%	88%	84%	88%	91%	91%	80%	94%	91%	94%	92%	73%	79%	82%	64%	81%
79.5%		81%	76%	83%	91%	77%	80%	58%	79%	74%	86%	91%	79%	80%	78%	85%	86%	80%	62%	89%	71%	83%
86.0%			93%	92%	92%	90%	73%	88%	81%	94%	95%	89%	72%	91%	89%	97%	95%	81%	74%	69%	73%	93%
85.1%		88%	90%	93%	93%	82%	92%	93%	85%	87%	80%	92%	93%	71%	71%	92%	92%	71%	70%	84%	84%	85%
79.4%			77%	75%	84%	63%	77%	83%	81%	83%	80%	91%	78%	70%	74%	82%	88%	90%	85%	70%		78%
75.0%			76%	72%	83%	74%	70%	84%	86%	83%	80%	69%	68%	77%	67%	67%	65%	77%	74%	75%	87%	67%
85.8%			90%	94%	94%	82%	88%	88%	94%	84%	90%	83%	80%	86%	85%	95%	86%	74%	72%	88%	78%	85%
80.7%			80%	84%	84%	86%	72%	89%	70%	84%	86%	84%	72%	89%	79%	76%	77%	74%	84%	84%	79%	82%
78.3%			87%	79%	71%	83%	85%	80%	91%	83%	78%	79%	55%	85%	82%	80%	71%	76%	58%	78%	82%	83%
91.5%			96%	92%	92%	86%	85%	89%	90%	89%	99%	97%	79%	95%	99%	92%	91%	95%	89%	92%	87%	96%
85.3%		78%	88%	86%	80%	79%	90%	82%	83%	89%	77%	89%	88%	84%	84%	93%	76%	92%	90%	80%	89%	95%
86.4%		89%	88%	85%	85%	91%	93%	88%	78%	83%	87%	87%	84%	88%	87%	87%	87%	87%	84%	84%		87%
85.2%		90%	89%	85%	86%	86%	86%	91%	84%	86%	85%	89%	79%	91%	87%	86%	89%	80%	78%	83%	78%	81%
74.4%		87%	69%	78%	86%	85%	70%	70%	79%	81%	77%	69%	61%	76%	59%	83%	77%	77%	53%	64%	75%	87%
81.6%		85%	83%	86%	96%	94%	82%	72%	83%	82%	80%	85%	66%	76%	72%	84%	76%	82%	90%	81%	82%	77%
62.9%			70%	61%	62%	74%	55%	66%	67%	73%	60%	60%	70%	69%	56%	75%	77%	47%	45%	60%	58%	54%
90.4%		98%	92%	91%	91%	84%	90%	88%	94%	89%	91%	89%	86%	93%	93%	92%	86%	88%	90%	95%	92%	87%
86.0%		91%	85%	89%	91%	90%	78%	84%	87%	89%	91%	83%	80%	84%	83%	86%	92%	90%	87%	78%	83%	86%
64.0%		61%	53%	69%	66%	72%	49%	64%	70%	82%	82%	57%	43%	68%	63%	75%	84%	54%	54%	50%	63%	66%
86.2%			87%	84%	84%	86%	91%	81%	88%	81%	82%	89%	85%	88%	89%	84%	85%	86%	88%	92%	86%	87%
67.8%			70%	73%	82%	95%	53%	71%	68%	74%	77%	68%	55%	71%	59%	75%	74%	57%	47%	56%	66%	65%
76.4%			83%	84%	83%	76%	94%	81%	89%	68%	75%	80%	79%	81%	80%	63%	69%	71%	58%	75%	66%	73%
85.0%		89%	85%	88%	91%	84%	88%	92%	86%	87%	84%	88%	88%	92%	87%	86%	76%	92%	34%	86%	88%	95%
78.7%		88%	81%	81%	85%	81%	67%	70%	86%	78%	81%	69%	75%	81%	68%	87%	80%	78%	82%	79%	87%	69%
84.6%		98%	88%	95%	99%	83%	96%	94%	96%	94%	96%	89%	75%	96%	81%	93%	91%	69%	60%	91%	46%	46%
87.1%			89%	91%	85%	86%	92%	86%	84%	85%	89%	92%	87%	92%	91%	83%	87%	90%	74%	91%	82%	87%
79.9%			74%	83%	85%	79%	78%	78%	75%	79%	86%	86%	65%	90%	82%	90%	94%	60%	72%	88%	71%	83%
85.1%		94%	91%	88%	88%	93%	86%	89%	88%	87%	85%	91%	81%	85%	84%	93%	88%	80%	37%	86%	84%	89%
79.8%		88%	91%	79%	81%	85%	71%	81%	92%	86%	83%	81%	78%	80%	64%	88%	88%	62%	72%	67%	80%	78%
78.4%		93%	84%	78%	83%	78%	85%	75%	66%	80%	82%	84%	65%	78%	72%	81%	76%	76%	76%	76%		80%
81.6%		81%	82%	87%	87%	85%	88%	80%	84%	86%	91%	81%	78%	87%	80%	82%	84%	81%	58%	77%	73%	82%
84.1%			86%	87%	74%	75%	94%	87%	62%	76%	88%	98%	87%	86%	90%	84%	80%	81%	95%	86%	82%	84%
85.6%		86%	86%	96%	86%	68%	93%	94%	85%	87%	92%	82%	89%	94%	89%	91%	83%	83%	80%	84%	61%	89%
87.6%		90%	89%	87%	87%	84%	92%	91%	98%	76%	89%	93%	78%	93%	85%	86%	77%	94%	88%	91%	83%	88%
90.1%		93%	93%	94%	95%	84%	87%	86%	94%	93%	83%	95%	77%	91%	90%	96%	93%	93%	81%	91%	89%	94%
76.6%		76%	74%	86%	96%	88%	74%	87%	84%	86%	83%	66%	56%	88%	71%	77%	79%	70%	55%	63%	70%	80%
80.4%			79%	89%	81%	84%	86%	85%	83%	80%	80%	81%	84%	87%	80%	76%	76%	80%	68%	80%	68%	81%
80.3%		81%	76%	84%	83%	73%	85%	86%	88%	76%	68%	86%	81%	78%	86%	79%	77%	84%	82%	82%	77%	74%
84.8%			92%	90%	90%	88%	93%	87%	86%	83%	86%	84%	77%	88%	88%	84%	87%	80%	77%	81%	70%	84%
88.5%		92%	93%	84%	91%	95%	74%	87%	91%	91%	91%	92%	90%	92%	82%	87%	94%	91%	83%	90%	84%	85%
80.7%			84%	84%	81%	68%	84%	77%	80%	78%	84%	87%	78%	84%	85%	87%	78%	84%	71%	81%	74%	85%
86.1%			92%	95%	91%	87%	88%	88%	87%	86%	85%	87%	80%	81%	87%	88%	75%	85%	80%	92%	85%	82%
91.8%		96%	95%	91%	94%	86%	93%	93%	98%	95%	94%	96%	88%	89%	84%	97%	98%	91%	86%	88%	96%	79%
81.0%		75%	94%	85%	94%	89%	94%	76%	74%	78%	85%	86%	75%	85%	94%	81%	85%	69%	58%	86%	69%	69%
79.2%		84%	82%	84%	82%	72%	84%	83%	79%	80%	78%	76%	79%	82%	81%	78%	80%	74%	62%	80%	80%	83%
88.3%		87%	95%	89%	93%	82%	89%	82%	79%	95%	88%	96%	95%	82%	93%	87%	84%	96%	78%	90%	79%	95%
83.5%			96%	86%	72%	90%	85%	93%	82%	83%	82%	84%	77%	82%	93%	83%	90%	90%	64%	82%	85%	72%
84.5%			86%	90%	84%	84%	89%	81%	85%	83%	88%	85%	85%	90%	86%	79%	90%	80%	76%	84%	78%	86%
73.0%			85%	88%	71%	77%	75%	84%	76%	73%	66%	80%	75%	65%	76%	70%	70%	59%	54%	81%	67%	69%
73.4%		70%	81%	78%	78%	85%	81%	76%	65%	75%	81%	79%	72%	81%	83%	72%	70%	60%	57%	80%	54%	63%
71.8%			83%	84%	86%	87%	60%	74%	89%	74%	75%	63%	52%	80%	68%	72%	77%	65%	31%	83%	65%	68%
83.1%		83%	92%	96%	94%	87%	76%	86%	87%	85%	91%	94%	82%	83%	80%	68%	65%	80%	67%	96%	78%	76%
75.1%			70%	68%	75%	79%	67%	77%	66%	77%	75%	74%	75%	77%	75%	69%	78%	81%	87%	79%	79%	74%
70.6%		60%	92%	90%	92%	79%	45%	58%	71%	67%	54%	95%	61%	64%	51%	86%	95%	78%	53%	66%	56%	70%
68.9%		84%	86%	77%	78%	47%	79%	54%	62%	60%	57%	60%	61%	71%	74%	79%	69%	75%	52%	94%	66%	61%
86.1%		81%	87%	89%	89%	91%	79%	88%	84%	88%	89%	92%	87%	89%	92%	86%	89%	84%	69%	85%	86%	85%
63.9%			71%	68%	67%	67%	77%	72%	66%	69%	62%	59%	66%	64%	63%	59%	62%	53%	59%	64%	55%	55%
86.5%		86%	85%	89%	87%	86%	84%	82%	80%	91%	85%	87%	90%	88%	88%	91%	89%	87%	72%	92%		90%
86.7%		82%	84%	92%	92%	90%	89%	83%	81%	83%	88%	89%	89%	91%	92%	91%	90%	78%	82%	92%	79%	84%
85.4%		93%	77%	88%	89%	51%	92%	95%	90%	81%	79%	83%	90%	85%	89%	87%	79%	89%	91%	83%	87%	95%
78.5%			72%	88%	89%	92%	54%	81%	69%	74%	94%	83%	64%	86%	68%	90%	90%	77%	89%	66%	87%	58%
82.6%			93%	90%	76%	89%	78%	78%	85%	80%	89%	87%	72%	84%	72%	87%	80%	67%	74%	84%	92%	95%
80.4%		90%	74%	84%	86%	75%	78%	76%	86%	79%	91%	79%	77%	85%	82%	72%	85%	90%	57%	75%	79%	89%
90.4%		92%	90%	92%	90%	88%	92%	84%	92%	93%	93%	89%	92%	87%	91%	92%	86%	91%	89%	90%	93%	93%
86.4%		95%	87%	89%	96%	96%	87%	81%	90%	87%	76%	83%	95%	78%	80%	86%	92%	71%	78%	93%	80%	94%
85.0%		86%	80%	86%	86%	98%	70%	78%	76%	82%	93%	85%	90%	94%	91%	82%	89%	92%	79%	80%	76%	93%
86.2%		80%	87%	80%	82%	88%	81%	86%	85%	87%	87%	91%	93%	90%	94%	85%	89%	77%	90%	83%	87%	89%
82.8%		80%	91%	74%	91%	70%	66%	91%	76%	89%	93%	91%	87%	74%	79%	89%	85%	87%	72%	76%	84%	94%
81.7%		95%	96%	84%	85%	94%	64%	78%	88%	91%	97%	81%	54%	81%	72%	93%	93%	74%	82%	84%	94%	36%
65.6%			62%	65%	78%	77%	38%	67%	58%	68%	85%	70%	60%	70%	63%	85%	79%	56%	44%	57%	74%	56%
74.4%		81%	63%	72%	77%	89%	86%	86%	78%	65%	81%	68%	77%	78%	93%	73%	72%	66%	61%	76%	61%	60%
86.0%			86%	89%	91%	88%	84%	81%	81%	83%	90%	95%	72%	84%	81%	91%	92%	90%	90%	76%	87%	89%
88.8%			93%	94%	85%	90%	91%	92%	92%	92%	91%	86%	94%	93%	93%	91%	93%	76%	74%	90%	78%	88%
89.6%		91%	92%	91%	93%	95%	83%	89%	89%	89%	92%	94%	77%	95%	94%	93%	93%	88%	78%	84%	86%	95%
88.9%		95%	78%	87%	89%	90%	84%	93%	95%	91%	91%	86%	90%	92%	84%	92%	91%	85%	84%	94%	85%	90%
87.4%			87%	89%	84%	84%	93%	85%	87%	87%	85%	90%	84%	93%	90%	90%	90%	93%	79%	91%	80%	87%
88.7%		90%	91%	91%	90%	93%	83%	75%	89%	91%	93%	94%	72%	93%	87%	88%	94%	87%	89%	94%	86%	93%
74.4%			75%	78%	78%	79%	86%	79%	67%	68%	72%	81%	75%	74%	72%	70%	78%	70%	57%	78%	75%	76%

Model Similarity Dendrogram

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