Effortless Data Serialization for AI and LLMs

By MiniTools Team on November 27, 2025

Introducing JSON ⇄ TOON

🚀 Up to 30–60 % fewer tokens than JSON — meaning lower LLM costs and more efficient context usage.

Short summary: A converter between JSON and TOON, enabling efficient, human-readable data serialization for AI/LLM applications.

What is TOON?

TOON (Token-Oriented Object Notation) is a compact, human-readable serialization format built on the same data model as JSON — objects, arrays, primitives — but optimized for use with large language models (LLMs). It serves as a drop-in, lossless representation of JSON for prompts and structured data fed into LLMs.

Key characteristics of TOON:

• Uses indentation (rather than braces {} and brackets []) for nested objects, reducing syntactic noise.
• For uniform arrays of objects (same structure across items), TOON supports a table-like layout: a single header declaring fields, followed by rows of comma-separated values — similar to CSV.
• Maintains full JSON semantics: you can convert JSON → TOON → JSON without loss of data.

TOON’s design addresses a core issue in LLM-focused workflows: every bracket, comma, quote, and repeated key in JSON counts as a token — and that token usage impacts cost, context-window usage, and latency.

Why TOON Matters (and when to use it)

Use TOON — via JSON ⇄ TOON — when:

• You need to pass structured data (configurations, records, metadata, arrays of objects) to LLMs.
• The data contains many uniform objects (e.g. list of users, products, logs, tabular records).
• Token efficiency, prompt size, and readability (by humans and models) are important constraints.

Advantages compared to JSON:

• Token reduction: Benchmarks suggest TOON can reduce token count by roughly 30–60 % compared to JSON. 💡
• Better context utilization: Less token overhead leaves more room in LLM context windows.
• Improved readability: Indentation-based syntax plus tabular arrays makes large data dumps easier to read/edit manually.
• Semantic clarity for LLMs: Explicit structure declarations (array lengths, field names) help models parse and interpret data more reliably.

⚠️ Caveat: for deeply nested or non-uniform data (complex nested objects, irregular arrays), JSON might remain more practical than TOON.

What JSON ⇄ TOON Adds

The JSON ⇄ TOON converter offers these benefits:

• Automates transformation of existing JSON data into TOON — no manual rewriting necessary.
• Lets you keep JSON internally (in databases, APIs, backends) while using TOON externally where LLM / prompt context matters.
• Facilitates the reverse transformation, letting you decode TOON back into JSON when needed.
• Delivers token-savings, hence reducing LLM API cost and improving prompt/context efficiency — without sacrificing data fidelity or readability.

Typical Usage Flow

1. Take existing JSON data (object, array, config).
2. Run JSON ⇄ TOON converter → produce TOON formatted text.
3. Provide that TOON output as input to your LLM or AI workflow.
4. Optionally, decode TOON back to JSON when needed.

Because TOON preserves the full data semantics of JSON (objects, nesting, arrays, primitives) and is lossless, the conversion is safe and reversible.

Conclusion

For developers building AI / LLM-driven systems, TOON — accessed via JSON ⇄ TOON — offers a pragmatic path to more efficient, manageable, token-optimized data exchange. Especially when working with uniform, tabular or configuration-style data, TOON can significantly reduce costs and increase prompt clarity — without giving up familiar structure of JSON.

Reference (official): https://github.com/toon-format/toon