Is TOON a Good Format for Passing Table Data to LLMs?

Token-Oriented Object Notation (TOON) is a new format that has been proposed as “a good way to pass structured data to Large Language Models with significantly reduced token usage.”

We recently tested how well an LLM understood table data in a variety of different formats.

A kind reader, Bruno Zell, suggested we add TOON to the comparison.

Here’s what we found…

Strong Accuracy for the Token Costs

We tested the accuracy of gpt-4.1-nano at answering questions based on data in the TOON format compared to in other formats.

Looking at accuracy vs. token costs, TOON was amongst the strongest performers at the token-efficient end of the spectrum.

Lower Accuracy Than With More Token-Hungry Formats

Accuracy with TOON wasn’t as good as with either our slightly less token-efficient ‘markdown table’ format or, unsurprisingly, with more token-hungry formats including markdown-kv, XML, YAML, HTML and JSON.

The difference in accuracy that we saw between using TOON and using the even more token-efficient CSV format wasn’t statistically significant.

(See our original comparison article for details of our methodology.)

Format	Accuracy	95% Confidence Interval	Tokens
Markdown-KV	60.7%	57.6% – 63.7%	52,104
XML	56.0%	52.9% – 59.0%	76,114
INI	55.7%	52.6% – 58.8%	48,100
YAML	54.7%	51.6% – 57.8%	55,395
HTML	53.6%	50.5% – 56.7%	75,204
JSON	52.3%	49.2% – 55.4%	66,396
Markdown-Table	51.9%	48.8% – 55.0%	25,140
Natural-Language	49.6%	46.5% – 52.7%	43,411
TOON	47.5%	44.4% – 50.6%	21,518
JSONL	45.0%	41.9% – 48.1%	54,407
CSV	44.3%	41.2% – 47.4%	19,524
Pipe-Delimited	41.1%	38.1% – 44.2%	43,098

Conclusions

We’re excited to see a format created specifically with LLMs in mind.

TOON could be a format to consider if you’re trying to reduce token usage, especially if the limitations of the CSV format make it hard to represent aspects of your data.