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TOON vs JSON comparison showing token efficiency for large language models

TOON: The New Data Format That Helps LLMs Say “Bye Bye” to JSON

Posted on January 1, 2026 by admin

Introduction: Why JSON Isn’t Ideal for LLMs

Developers and data scientists have long used JSON as the go-to format for APIs and structured data. But in the era of large language models (LLMs), JSON reveals a weakness: token inefficiency.

Every brace, comma, quote, and repeated key in a JSON document counts toward your token budget — and tokens are the primary cost unit for LLMs. That’s where TOON (Token-Oriented Object Notation) steps in — a compact, lightweight alternative built for LLM prompts. Medium

What Is TOON?

TOON is a token-efficient data serialization format designed for large language models. It’s not meant to replace JSON everywhere — especially not in APIs or traditional data storage — but to reduce redundancy when feeding structured data into LLMs. The DATA CHANNEL

Instead of repeating field names and syntax for every object in a list, TOON declares schema once and then streams the values, cutting down the number of tokens used dramatically. Medium

🔗 Backlink idea: Link to a JSON2TOON converter tool on your blog
Example: Check out this JSON to TOON conversion tool (external resource) to easily optimize your datasets for LLMs. JSON to TOON Converter

Example: TOON vs JSON (Side by Side)

Standard JSON:

[
  { "id": 1, "name": "Alice", "role": "admin" },
  { "id": 2, "name": "Bob", "role": "user" }
]

TOON Equivalent:

users[2]{id,name,role}:
  1,Alice,admin
  2,Bob,user

💡 In TOON:

  • You list the field names once (id,name,role)
  • You state how many rows you have ([2])
  • Then stream all rows below

This means fewer tokens and faster LLM responses. Medium

Why Token Efficiency Matters

Unlike traditional systems, LLMs charge by tokens, not by weight or bytes. JSON’s repetitive structure — especially in large datasets — inflates token counts unnecessarily:

✔ Repeated keys
✔ Lots of punctuation
✔ Redundant braces and quotes

TOON eliminates much of that repetition, leading to significant token savings — in tests, JSON often uses nearly double the tokens compared to TOON — especially with large tables or repetitive records. Medium

👉 Backlink idea: Link to your own article about optimizing costs with AI/LLMs.

When to Use TOON vs When to Stick With JSON

✅ Use TOON When:

  • You’re sending large tables or repetitive records to an LLM
  • Token cost matters for your application
  • Your data is consistent in structure (same fields each row) The DATA CHANNEL

❌ Stick With JSON When:

  • You’re building APIs or data services
  • You’re storing data long-term
  • Your dataset has uneven or deeply nested structures
  • You need broad support from legacy tooling and systems The DATA CHANNEL

Is TOON Perfect?

Not quite. TOON isn’t ideal for:

  • Complex deeply nested data
  • Datasets with variable object shapes
  • Systems that expect standard JSON structures

Many backend engineers may prefer traditional formats, and tooling around TOON is still growing — but for LLM prompts and bulk data, it’s already proving useful. Medium

👉 Backlink idea: Link to benchmarks or comparisons between data formats for AI. Example: “Explore token savings in AI data formats.”

Conclusion: TOON Isn’t Here to Replace JSON — Just to Optimize It

TOON may not dethrone JSON across all domains, but it offers a smart, token-efficient way to handle large structured inputs for LLMs. It’s a niche solution — not a revolution — but it’s a powerful optimization where it counts.