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.
