JSON has been our go-to data format for web APIs since 2001, and for good reason—it works everywhere. But here's the thing: when you start working with LLMs like GPT-4 or Claude, JSON suddenly feels expensive. Every repeated property name costs you tokens (and money). That's where TOON comes in.

In this comparison, we'll look at when JSON's universal compatibility makes sense, and when TOON's 50% token savings can seriously cut your API costs. Try our JSON to TOON converter orTOON to JSON converter to see the difference yourself.

Quick Overview

JSON

•Universal compatibility across all platforms
•Native browser and language support
•Extensive tooling and IDE integration
•Repeats property names for each object
•Ideal for web APIs and general use

TOON

•50% fewer tokens than JSON
•Optimized specifically for LLM prompts
•Defines properties once per array
•Reduces API costs and latency
•Ideal for AI applications and prompts

Syntax Comparison

The fundamental difference between TOON and JSON lies in how they structure repeated data. Here's the same customer dataset in both formats:

JSON Format (152 tokens)

{
  "customers": [
    {
      "id": 1,
      "name": "Sarah Mitchell",
      "email": "[email protected]",
      "plan": "Premium",
      "mrr": 299,
      "active": true
    },
    {
      "id": 2,
      "name": "Michael Chen",
      "email": "[email protected]",
      "plan": "Enterprise",
      "mrr": 999,
      "active": true
    },
    {
      "id": 3,
      "name": "Jennifer Kumar",
      "email": "[email protected]",
      "plan": "Basic",
      "mrr": 99,
      "active": false
    },
    {
      "id": 4,
      "name": "David Park",
      "email": "[email protected]",
      "plan": "Premium",
      "mrr": 299,
      "active": true
    },
    {
      "id": 5,
      "name": "Emma Wilson",
      "email": "[email protected]",
      "plan": "Basic",
      "mrr": 99,
      "active": true
    }
  ]
}

TOON Format (76 tokens - 50% reduction!)

customers[5]{id,name,email,plan,mrr,active}:
  1,Sarah Mitchell,[email protected],Premium,299,true
  2,Michael Chen,[email protected],Enterprise,999,true
  3,Jennifer Kumar,[email protected],Basic,99,false
  4,David Park,[email protected],Premium,299,true
  5,Emma Wilson,[email protected],Basic,99,true

Token Analysis: JSON uses 152 tokens because it repeats property names (id, name, email, plan, mrr, active) for all 5 objects. TOON declares these properties once in the header{id,name,email,plan,mrr,active}, reducing the total to 76 tokens.

This 50% reduction scales linearly - 100 customers would be 3,000+ tokens in JSON vs 1,500 tokens in TOON.

Token Efficiency: The Core Difference

Understanding why TOON achieves 50% token reduction requires examining how LLMs tokenize data:

JSON Tokenization

In JSON arrays, every object repeats all property names. For a 100-object array with 6 properties:

• Property names repeated: 100 × 6 = 600 times
• Structural characters: { } [ ] " " for every object
• Colons and commas: Multiple per object
• Total overhead: ~60-70% of tokens are structure, not data

TOON Tokenization

TOON uses a tabular approach where property names appear once in the header:

• Property names repeated: 1 time (in header only)
• Structural characters: Minimal delimiters (commas)
• Length markers: [N] explicitly states array size
• Total overhead: ~20-30% of tokens are structure

Real-World Example: Customer Records

Record Count	JSON Tokens	TOON Tokens	Savings
5 customers	152 tokens	76 tokens	50%
50 customers	1,520 tokens	760 tokens	50%
500 customers	15,200 tokens	7,600 tokens	50%
1,000 customers	30,400 tokens	15,200 tokens	50%

Cost Impact Analysis

Token savings directly translate to cost savings when using LLM APIs. Here's a breakdown using current GPT-4 pricing:

GPT-4 Pricing (as of 2025)

•Input tokens: $0.01 per 1,000 tokens
•Output tokens: $0.03 per 1,000 tokens

Low-Volume Application

Scenario: AI chatbot making 10,000 API calls/month with average 1,000 tokens per prompt

JSON monthly cost:10M tokens × $0.01 = $100

TOON monthly cost:5M tokens × $0.01 = $50

Monthly savings:$50 (50%)

Annual savings:$600

Medium-Volume Application

Scenario: Enterprise RAG system making 100,000 API calls/month with average 2,000 tokens per prompt

JSON monthly cost:200M tokens × $0.01 = $2,000

TOON monthly cost:100M tokens × $0.01 = $1,000

Monthly savings:$1,000 (50%)

Annual savings:$12,000

High-Volume Application

Scenario: Large-scale AI platform processing 1M API calls/month with average 3,000 tokens per prompt

JSON monthly cost:3B tokens × $0.01 = $30,000

TOON monthly cost:1.5B tokens × $0.01 = $15,000

Monthly savings:$15,000 (50%)

Annual savings:$180,000

Performance and Latency

Token count affects not just cost, but also processing speed and response time:

Processing Speed

LLMs process tokens sequentially. GPT-4 typically processes at 50-100 tokens per second. For a 4,000-token JSON prompt:

• JSON (4,000 tokens): 40-80 seconds processing time
• TOON (2,000 tokens): 20-40 seconds processing time
• Improvement: 50% faster responses

Context Window Utilization

With fixed context windows (e.g., GPT-4's 8K, 32K, or 128K token limits), token efficiency means more data capacity:

• JSON in 8K window: ~150 customer records
• TOON in 8K window: ~300 customer records
• Result: 2x data capacity in same context

Network Transfer

While both are text formats, TOON's reduced character count also means smaller payload sizes, resulting in faster network transfers and reduced bandwidth costs for high-volume applications.

Feature-by-Feature Comparison

Feature	JSON	TOON
Token Efficiency	Standard	50% reduction
Readability	Excellent	Very Good
Browser Support	Native (JSON.parse)	Requires library
IDE Support	Extensive	Growing
LLM Optimization	Not optimized	Purpose-built
API Cost (high volume)	Higher	50% lower
Nested Objects	Excellent support	Good support
Array Efficiency	Repeats keys	Keys defined once
Schema Validation	JSON Schema	TOON Validator
Best Use Case	Web APIs, general use	LLM prompts, AI apps

When to Use JSON vs TOON

Use JSON When:

Building Traditional Web APIs

REST APIs consumed by web applications, mobile apps, or third-party integrations should use JSON. Universal compatibility and native browser support make JSON the clear choice for HTTP APIs.

Token Count Is Not a Concern

For applications where you're not working with LLMs or token-based pricing, JSON's broad ecosystem and tooling support outweigh TOON's efficiency benefits.

Maximum Compatibility Required

When integrating with legacy systems, third-party services, or environments where introducing new dependencies is problematic, JSON's universal support is invaluable.

Complex Nested Structures

For deeply nested object graphs with varying structures, JSON's flexibility and extensive tooling (JSON Schema, validators, IDE support) make development easier.

Use TOON When:

Building LLM Applications

Any application making API calls to GPT-4, Claude, Gemini, or other LLMs benefits from TOON's 50% token reduction. Lower costs and faster responses make TOON ideal for production AI systems.

High API Volume

Applications processing millions of tokens monthly see substantial cost savings. The $15,000-$180,000 annual savings for high-volume apps justify the migration effort.

Context Window Optimization

RAG systems and applications needing to maximize data within token limits benefit from TOON's efficiency. Fitting 2x more context means better AI responses and more comprehensive analysis.

Tabular Data for LLMs

When passing structured datasets (customer lists, transaction logs, analytics data) to LLMs, TOON's array format is significantly more efficient than JSON's object-per-row approach.

Converting Between Formats

Converting between JSON and TOON is straightforward and lossless in both directions:

JSON to TOON Converter

Transform your existing JSON data to TOON format. See immediate token savings and measure the impact on your specific use case.

Try Converter →

TOON to JSON Converter

Convert TOON back to JSON when needed. Ensures compatibility with systems expecting JSON format.