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.
