toon/benchmarks/results/accuracy/accuracy.md

Retrieval Accuracy

Tested across 2 LLMs with data retrieval tasks:

gpt-4o-mini          ██████████████░░░░░░ 72.3% accuracy
claude-haiku-4-5     ███████████████░░░░░ 76.7% accuracy

TOON achieves 73.9% accuracy (vs JSON's 73.6%) while using 46.3% fewer tokens.

Format	Accuracy	Average Tokens
toon	73.9%	4.678
json	73.6%	8.713
markdown-kv	73.6%	8.649
csv	72.3%	4.745
yaml	71.7%	7.091

View detailed breakdown by dataset and model

Performance by Dataset

Uniform employee records (TOON optimal format)

Format	Accuracy	Tokens	Correct/Total
toon	72.4%	2.483	84/116
csv	69.0%	2.337	80/116
yaml	68.1%	4.969	79/116
markdown-kv	68.1%	6.270	79/116
json	68.1%	6.347	79/116

E-commerce orders with nested structures

Format	Accuracy	Tokens	Correct/Total
toon	84.1%	5.967	74/88
csv	83.0%	6.735	73/88
yaml	81.8%	7.328	72/88
markdown-kv	86.4%	9.110	76/88
json	84.1%	9.694	74/88

Time-series analytics data

Format	Accuracy	Tokens	Correct/Total
csv	72.4%	1.393	42/58
toon	70.7%	1.515	41/58
yaml	72.4%	2.938	42/58
json	74.1%	3.665	43/58
markdown-kv	70.7%	3.779	41/58

Popular GitHub repositories

Format	Accuracy	Tokens	Correct/Total
toon	64.3%	8.745	36/56
csv	62.5%	8.513	35/56
json	67.9%	15.145	38/56
markdown-kv	67.9%	15.436	38/56
yaml	62.5%	13.129	35/56

Performance by Model

gpt-4o-mini

Format	Accuracy	Correct/Total
toon	72.3%	115/159
json	71.7%	114/159
markdown-kv	70.4%	112/159
csv	69.2%	110/159
yaml	68.6%	109/159

claude-haiku-4-5

Format	Accuracy	Correct/Total
markdown-kv	76.7%	122/159
toon	75.5%	120/159
json	75.5%	120/159
csv	75.5%	120/159
yaml	74.8%	119/159

Methodology

• Semantic validation: LLM-as-judge validates responses semantically (not exact string matching)
• Token counting: Using gpt-tokenizer with o200k_base encoding
• Question types: Field retrieval, aggregation, and filtering tasks
• Real data: faker.js-generated datasets + real GitHub repository data