The recently released Token-Oriented Object Notation (TOON) aims to be a schema-aware alternative to JSON that significantly reduces token consumption at a similar level of accuracy. While the existence and importance of token saved depend on the data shape. some benchmarks show TOON may use in some cases 40% fewer tokens than JSON, possibly resulting in LLM and inference cost savings.
The recently released Token-Oriented Object Notation (TOON) aims to be a schema-aware alternative to JSON that significantly reduces token consumption at a similar level of accuracy. While the existence and quantity of saved tokens depend on the data shape, some benchmarks show that TOON may use 40% fewer tokens in some cases than JSON, potentially resulting in LLM and inference cost savings.
TOON self-describes as a compact, human-readable encoding of the JSON data model for LLM prompts.
Consider the following JSON: { „context“: { „task“: „Our favorite hikes together“, „location“: „Boulder“, „season“: „spring_2025“ }, „friends“: [„ana“, „luis“, „sam“], „hikes“: [ { „id“: 1, „name“: „Blue Lake Trail“, „distanceKm“: 7.
