TOON vs JSON: A Complete Comparison

Understanding the differences between Token-Oriented Object Notation (TOON) and JSON for AI applications, with real-world examples and performance metrics.

Quick Comparison

Feature	JSON	TOON
Token Efficiency	Baseline (100%)	30-60% fewer tokens
Human Readability	Good	Excellent (tabular format)
LLM Parsing Accuracy	70% (benchmark)	74% (benchmark)
Browser Support	Native	Requires parser
Ecosystem Maturity	Mature	Growing
Best For	Web APIs, General purpose	LLM prompts, AI applications
Array Representation	Bracketed, verbose	Tabular, compact

Detailed Analysis

1. Syntax & Readability

JSON uses brackets, braces, and quotes extensively, while TOON uses indentation and tabular formats similar to YAML and CSV.

JSON Format

{
  "employees": [
    {
      "id": 1,
      "name": "Alice",
      "role": "Engineer",
      "salary": 120000
    },
    {
      "id": 2,
      "name": "Bob",
      "role": "Designer",
      "salary": 95000
    },
    {
      "id": 3,
      "name": "Charlie",
      "role": "Manager",
      "salary": 135000
    }
  ]
}

~380 characters | ~95 tokens

TOON Format

employees[3]{id,name,role,salary}:
  1,Alice,Engineer,120000
  2,Bob,Designer,95000
  3,Charlie,Manager,135000

~140 characters | ~35 tokens (63% savings!)

2. Token Efficiency

TOON's tabular format eliminates repetitive keys and excessive punctuation, resulting in significant token savings:

✓No repeated keys: In arrays of objects, JSON repeats keys for every item. TOON declares keys once.
✓Minimal syntax: No braces, brackets, or quotes needed for simple values.
✓CSV-like efficiency: Tabular data is as compact as CSV but with explicit structure.

💰 Cost Impact

For 1 million API calls with 500 tokens of data each:
JSON: 500M tokens × $0.01/1K = $5,000
TOON: 200M tokens × $0.01/1K = $2,000
Savings: $3,000 (60%)

3. When to Use Each Format

📄 Use JSON When:

• Building web APIs (REST)
• Need browser native support
• Working with existing ecosystems
• Data structure changes frequently
• Deep nesting is common
• Interoperability is critical

🎒 Use TOON When:

• Sending data to LLMs (ChatGPT, Claude)
• Token costs are a concern
• Working with tabular/uniform data
• Need better LLM parsing accuracy
• Optimizing prompt engineering
• Building AI-native applications

4. Performance Benchmarks

According to official TOON benchmarks across 4 LLM models with 209 data retrieval questions:

Model	JSON Accuracy	TOON Accuracy	Improvement
Claude Haiku	55.2%	59.8%	+4.6%
Gemini Flash	84.7%	87.6%	+2.9%
GPT-4o Nano	88.5%	90.9%	+2.4%
Average	70%	74%	+4%

5. Migration from JSON to TOON

Migrating from JSON to TOON is straightforward with the right tools:

1
Identify LLM-bound data: Not all JSON needs conversion. Focus on data sent to language models.
2
Use our converter: Test your data with our free online converter.
3
Integrate libraries: Use official TOON libraries for TypeScript, Python, Go, or Rust.
4
A/B test: Compare response quality and costs between JSON and TOON.

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