TOON vs JSON: Which Data Format Wins in 2025?

As AI transforms how developers work with data, the formats we rely on must evolve too. JSON has dominated the web for nearly two decades-but in 2025, a new format is quickly gaining traction: TOON (Token-Oriented Object Notation).

This guide dives deep into TOON vs JSON, comparing syntax, performance, cost, use cases, limitations, and real examples to help you decide which format fits your workflow in 2025.

What Are JSON and TOON? A Quick Overview

Before comparing, let's define each.

JSON (JavaScript Object Notation)

A human-readable data format widely used for APIs, config files, databases, and app data exchange.

Strengths include readability, tooling support, and universal adoption.

TOON (Token-Oriented Object Notation)

A modern, compact data notation designed specifically for LLM efficiency.

TOON aims to solve JSON's verbosity by reducing structural repetition and minimizing tokens-making it ideal for AI-driven workflows.

In short:

• JSON was built for the web.
• TOON was built for AI.

Why TOON Emerged - JSON's Limitations in the Age of LLMs

JSON was never designed for token-based AI models like ChatGPT, Claude, or Gemini.

JSON limitations when used with LLMs:

• Excessive punctuation → higher token counts
• Repeated keys → wasted context space
• Some structures cause parsing errors in AI models
• Expensive when passing large data into prompts
• Less information-dense than AI-optimized formats

Developers began needing a format that is:

• Compact
• Easy for LLMs to interpret
• Token-efficient
• Schema-friendly

This gap led to the rise of TOON, which drastically reduces token usage and improves model comprehension.

Core Differences: Syntax, Structure & Token Efficiency

1. Syntax & Readability

TOON minimizes punctuation and relies on indentation and positional structure.

Example: Simple Object

JSON:

{
  "name": "Ava",
  "age": 28,
  "role": "editor"
}

TOON:

name Ava
age 28
role editor

Result: ~40% fewer tokens.

2. Arrays & Bulk Data (TOON's Superpower)

TOON excels when representing large arrays of repeated structures.

JSON:

[
  { "id": 1, "name": "Ava", "score": 90 },
  { "id": 2, "name": "Liam", "score": 85 },
  { "id": 3, "name": "Maya", "score": 92 }
]

TOON:

students:
  id name score
  1 Ava 90
  2 Liam 85
  3 Maya 92

TOON reduces:

• Repetition of keys
• Curly braces
• Commas and quotes

This results in 30–60% token savings depending on data size.

Performance, Cost & LLM-Specific Advantages

At JsonKithub, we benchmarked TOON vs JSON using multiple LLMs.

Here's what we found:

30–60% fewer tokens across tests

Especially in uniform datasets or tabular arrays.

Lower API costs

Fewer input tokens = cheaper prompt submissions.

More data fits into model context

Useful for retrieval-augmented generation (RAG), analysis tasks, or structured prompts.

Improved model accuracy

LLMs make fewer mistakes when reading TOON because structure is clearer.

Faster processing

Models require fewer reasoning steps.

When JSON Still Makes Sense (Use Cases & Tradeoffs)

TOON is not a universal replacement. JSON continues to dominate in several scenarios.

JSON is better when:

• Working with web APIs
• Handling deeply nested structures
• Using general-purpose applications
• Interoperability with all libraries matters
• Human readability is more important than compression

JSON Strengths:

• Widely adopted
• Huge tooling ecosystem
• Requires zero learning curve
• Perfect for backend-to-backend communication

When TOON Wins - Real Use Cases in 2025

TOON is gaining adoption fast in AI-heavy workflows.

Here's where TOON outperforms JSON:

1. LLM Prompts with Structured Data

Cleaner for both developers and models.

2. AI Agents & Multi-step Pipelines

Reduces cost while increasing efficiency.

3. Tabular datasets

Massive token reduction for large arrays.

4. Summaries, validations, transformations

Models interpret TOON with less ambiguity.

5. Inter-LLM communication

TOON eliminates structural noise.

As more AI tools emerge, TOON is set to become a standard in AI-centric applications.

Practical Guide: Converting JSON ↔ TOON

JsonKithub will soon provide utilities to make conversions easy.

Here's a simple conceptual example:

JSON to TOON (Manual Example)

JSON:

{
  "name": "Ava",
  "tags": ["ai", "nlp", "ml"]
}

TOON:

name Ava
tags:
  ai
  nlp
  ml

TOON to JSON

TOON:

id 1
user Ava
active true

Converted JSON:

{
  "id": 1,
  "user": "Ava",
  "active": true
}

Risks, Limitations & What to Watch Out For

TOON is powerful, but not perfect.

Limitations:

• Not yet a mainstream standard
• Limited training exposure in older LLMs
• Tools for validation and linting are still evolving
• Deeply nested data reduces readability
• Not suitable for public APIs

TOON is best viewed as a complement to JSON-not a replacement.

The Future: Will TOON Replace JSON?

JSON will continue to dominate the web.

But TOON is positioned to dominate AI and LLM workflows.

Prediction by JsonKithub:

• JSON remains the universal API format
• TOON becomes the preferred AI-native data format
• AI developers use both: JSON externally, TOON internally

2025 marks the first year where TOON becomes a real competitor.

Conclusion - Which Format Wins in 2025?

There is no single winner.

The "best" format depends on the use case.

Use JSON when:

• Building apps
• Working with APIs
• Ensuring compatibility

Use TOON when:

• Working with AI models
• Reducing token cost
• Handling structured or tabular data
• Improving LLM clarity

In 2025, TOON wins for AI. JSON wins for everything else.

At JsonKithub, we believe both formats will coexist-and developers who master both will be well-prepared for the AI-driven future.

Tools on JSON Kithub help:

• Convert YAML to JSON
• Convert JSON to YAML
• Stringify JSON
• Parse JSON
• JSON formatter
• Compare JSON
• JSON Validator
• Minify JSON
• JSON Escape
• JSON Unescape
• Convert JSON to TOON
• Convert TOON to JSON

FAQs on TOON vs JSON Which Data Format Wins

1. Is TOON a replacement for JSON?

No. TOON complements JSON. JSON still dominates APIs and general applications.

2. Why is TOON better for LLMs?

It reduces token usage, removes structural noise, and improves model comprehension.

3. How much does TOON reduce token cost?

Between 30–60%, depending on dataset structure.

4. Can TOON handle nested objects?

Yes, but deeply nested structures reduce readability benefits.

5. Will TOON become a standard in AI applications?

Likely - early adoption is growing quickly across AI-driven workflows.

6. Is TOON safe for production systems?

Yes for AI pipelines, but not yet recommended for public APIs.

7. Can TOON convert to JSON easily?

Yes. Conversion is straightforward and JsonKithub will soon offer tools for it.

8. Does TOON support arrays?

Yes - and TOON handles them more efficiently than JSON.

9. Do LLMs understand TOON out of the box?

Most modern models do, thanks to its clear and compact structure.

10. Which format should I learn first?

JSON is essential; TOON is the future for AI developers. Learning both is ideal.