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TOON: A Fresh, Token-Smart Alternative to JSON for the LLM

Nov 12, 2025•2 min read

TOON is a compact, human-readable data format designed to reduce token usage and lower costs in AI and LLM workflows.

TOON: A Fresh, Token-Smart Alternative to JSON for the LLM

When we talk about data serialization formats, JSON tends to walk into the room wearing sunglasses like a rockstar — it’s everywhere, everyone supports it, and it works well enough. But the world has changed. We now feed structured data to Large Language Models (LLMs), and suddenly every token matters: cost, speed, context window… all are strategic constraints.

Enter TOON — Token-Oriented Object Notation — a compact and elegant data notation tailored for LLM interactions.
Think of it as the data format for conversations, not storage.

🪄 TOON reduces token usage without sacrificing structure or clarity.

Comparison between other formates

🎯 TOON by Example

JSON:

{   "products": [
    {
      "id": 1,
      "name": "Phone",
      "price": 699
    },
    {
      "id": 2,
      "name": "Laptop",
      "price": 1299
     }   
  ] 
}

TOON:

products[2]{id,name,price}:

1,Phone,699

2,Laptop,1299

Notice the philosophy:

  • Show the structure once
  • Then list the data cleanly
  • No repeating keys ✅
  • No quotes unless needed ✅
  • No curly braces everywhere ✅

It’s structured like JSON
But compact like CSV
And read like plain text
A rather elegant bridge

🎨 The Syntax Pattern (Simple and Predictable)

<name>[<count>]{<fields>}:

<row1 values>

<row2 values>

...

Yes — that’s nearly the entire grammar in one breath.

TOON works best when:

  • you have lists of objects
  • with the same fields

In other words, TOON loves tables.

⚡ Performance & Token Savings

When sending data to LLMs, each character turns into tokens, which affect:

  • Prompt cost (yes, your bill 💸)
  • Model response time
  • How much data you can fit into the context window

TOON cuts token usage by removing redundancy.

It’s not uncommon to see 20%–60% token savings on large datasets.

This can translate to:

  • Cheaper AI pipelines
  • Bigger datasets included in context
  • Better reasoning accuracy (because more info fits at once)

This is practical efficiency — not theoretical.

🔗Resource:

https://github.com/toon-format/toon

https://dev.to/sreeni5018/toon-vs-json-a-modern-data-format-showdown-2ooc

https://smali-kazmi.medium.com/toon-vs-json-the-modern-data-format-showdown-built-for-the-ai-era-52549a2c3d1e

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