TOON vs JSON: A Modern Data Format Showdown
By @huyhunhngc
🚀 TOON vs JSON: A Modern Data Format Showdown
Introduction
The evolution of data formats tells a fascinating story about how technology adapts to meet changing needs. From humble INI files, to verbose yet powerful XML, lightweight JSON, human-friendly YAML, and now TOON — a token-optimized format built for the AI era — each format emerged to solve the challenges of its time.
Today, as Large Language Models (LLMs) reshape how we process and exchange information, token efficiency has become a new frontier. Every character matters.
This article explores how TOON (Token-Oriented Object Notation) compares with JSON, and why TOON may become a preferred format for GenAI developers.
🕰️ A Brief History of Data Formats
INI Files
The .INI format was one of the earliest ways to store configuration. It relied on simple key–value pairs grouped into sections.
[database]
host=localhost
port=5432
username=admin
password=secret
Despite their simplicity, INI files remain popular for configuration use cases due to their clarity and minimalism.
XML
XML (eXtensible Markup Language) introduced strong structure, hierarchy, and validation. It became the backbone of early web services, SOAP APIs, and document systems.
However, its verbosity came at a cost.
XML’s strictness made it powerful — and painful — for many developers.
JSON
JSON (JavaScript Object Notation) struck the perfect balance between structure and simplicity. It is lightweight, human-readable, and easy to parse.
JSON quickly became the universal language of web APIs.
YAML
YAML (YAML Ain’t Markup Language) focused on human readability using indentation and minimal punctuation. It became popular for configuration files and CI/CD pipelines.
While YAML excels for humans, it can be error-prone for machines due to indentation sensitivity and parsing quirks.
🤖 TOON: The New Era
As AI models process and reason over text, a new challenge emerged: token efficiency.
This led to the birth of TOON (Token-Oriented Object Notation) — a data format built specifically for the LLM age.
users[1]{id,name,role}:
1,Sreeni,admin
TOON is not just another serialization format. It is designed to be compact, structured, and optimized for how language models process text.
⚠️ The Modern Challenge
Traditional formats like JSON are still excellent. However, in LLM-driven workflows:
- Verbosity = higher cost
- More tokens = slower processing
Using 50% fewer tokens to represent the same data can significantly reduce inference cost and latency.
This brings us to the core comparison: TOON vs JSON.
📄 What is JSON?
JSON is a lightweight, text-based format representing structured data using key–value pairs.
Key Characteristics
- Syntax:
{},[],:,, - Readable: Easy for humans and machines
- Flexible: Supports complex nesting
- Compatible: Supported everywhere
- Verbose: Repetitive keys increase size
🧠 What is TOON?
TOON (Token-Oriented Object Notation) is a next-generation format tailored for AI and LLM workflows. Its primary goal is token efficiency.
Key Characteristics
- Syntax: Header + tabular rows
- Efficiency: 30–60% fewer tokens than JSON
- Compactness: Eliminates redundant keys and symbols
- Readability: Spreadsheet-like structure
- Optimization: Purpose-built for AI data flows
users[3]{id,name,role,email}:
1,Sreeni,admin,sreeni@example.com
2,Krishna,admin,krishna@example.com
3,Aaron,user,aaron@example.com
metadata{total,last_updated}:
3,2024-01-15T10:30:00Z
🔍 TOON vs JSON: Key Differences
1. Syntax and Structure
- JSON: Uses braces, brackets, colons, and commas.
- TOON: Uses headers and rows — cleaner and less noisy.
2. Token Efficiency
LLMs charge by tokens.
| Format | Tokens | Savings |
|---|---|---|
| JSON | ~89 | — |
| TOON | ~45 | ~50% |
3. Readability
- JSON is familiar and tooling-rich.
- TOON feels new but becomes intuitive for repetitive structured data — like CSV meets JSON.
4. Use Cases
TOON excels where data is:
- Repetitive
- Tabular
- Consumed directly by LLMs
📊 Real-World Comparison
Token Count Example
- JSON ≈ 180 tokens
- TOON ≈ 85 tokens
- Savings: ~53%
🧭 When to Use Each Format
Use JSON When
- Compatibility and standardization are required
- Building REST APIs or web applications
- Relying on mature ecosystems and tooling
- Team familiarity is critical
Use TOON When
- Working with LLMs and AI agents
- Token cost and performance matter
- Handling large or repetitive datasets
- Designing AI-first data pipelines
🧰 Implementation & Libraries
JSON Support
- Universal across all major languages
- Extensive tooling (linters, validators)
- Native support in browsers and servers
TOON Support
-
JavaScript / TypeScript: https://github.com/toon-format/toon
-
Online Converters:
🏁 Conclusion
Both JSON and TOON have earned their place in modern development.
- JSON remains the universal workhorse for APIs, configurations, and interoperability.
- TOON is a rising star of the LLM era — optimized for cost efficiency, clarity, and AI performance.
As AI systems continue to expand, token-optimized formats like TOON will become increasingly valuable. At the same time, JSON’s universality ensures it will remain relevant.
The future is not JSON or TOON — but JSON and TOON, used side by side, each where it shines the most.