When working with AI models like GPT-4, Claude, or Gemini, API costs are directly tied to token usage. While JSON works well for traditional web APIs, its structure becomes inefficient when used with Large Language Models, as you're charged for every character in your payload.
TOON addresses this issue by providing a data format specifically optimized for AI applications. It uses approximately 50% fewer tokens than JSON, effectively cutting API costs in half while allowing you to include twice as much data within model context limits.
Summary: Use JSON for traditional web APIs and general purposes. Use TOON for AI model interactions to reduce token costs.
Use our JSON to TOON converter to calculate potential savings with your data.
The Main Differences at a Glance
Let's start with a simple comparison so you know what each format is good at:
JSON
- •Works everywhere - browsers, APIs, databases
- •No extra libraries needed
- •Every developer knows it
- •Repeats field names over and over
- •Expensive when using AI models
BEST FOR:
Web APIs, mobile apps, general data exchange
TOON
- •50% fewer tokens than JSON
- •Designed specifically for AI/LLMs
- •Saves money on API costs
- •Needs a library (but simple to use)
- •Newer format, smaller ecosystem
BEST FOR:
AI prompts, LLM applications, cost optimization
Side-by-Side: Same Data, Different Format
Here's the same customer data in both formats. Notice how JSON repeats "id", "name", "email" etc. for every customer, while TOON lists those field names just once at the top:
Real Token Count:
JSON = 152 tokens | TOON = 76 tokens | Savings = 50%
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% less!)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,trueWhy TOON Uses Fewer Tokens
JSON Structure:
- • Repeats "id" 5 times
- • Repeats "name" 5 times
- • Repeats "email" 5 times
- • Repeats "plan" 5 times
- • Repeats "mrr" 5 times
- • Repeats "active" 5 times
- • Additional syntax overhead
TOON Structure:
- • Lists each field name once in header
- •
{id,name,email...} - • Only values for each row
- • Minimal structure
- • Significantly fewer characters
Scaling Effect: With 100 records, JSON uses approximately 3,000 tokens while TOON uses only 1,500 tokens. The efficiency advantage increases with dataset size.
How Much Can You Really Save?
Let's look at real numbers. Here's how the token savings scale as you add more data. (These numbers are from actual token counting with GPT-4):
| Number of Records | JSON Tokens | TOON Tokens | You Save |
|---|---|---|---|
| 5 customers | 152 | 76 | 50% |
| 50 customers | 1,520 | 760 | 50% |
| 500 customers | 15,200 | 7,600 | 50% |
| 1,000 customers | 30,400 | 15,200 | 50% |
JSON Token Growth
Token usage increases linearly with data volume. With 1,000 records, JSON requires 30,400 tokens due to repeated field names and structural overhead.
TOON Efficiency
The same 1,000 records require only 15,200 tokens. The savings remain consistent at approximately 50% regardless of dataset size.
Cost Comparison Analysis
Token usage directly impacts API costs. The following analysis shows potential savings with TOON based on typical GPT-4 pricing ($0.01 per 1,000 input tokens):
Small Application
10,000 API calls/month • 1,000 tokens per call
JSON Cost
$100/mo
TOON Cost
$50/mo
Annual Savings
$600
Growing Business
100,000 API calls/month • 2,000 tokens per call
JSON Cost
$2,000/mo
TOON Cost
$1,000/mo
Annual Savings
$12,000
Enterprise Scale
1,000,000 API calls/month • 3,000 tokens per call
JSON Cost
$30,000/mo
TOON Cost
$15,000/mo
Annual Savings
$180,000
Note:
Savings scale with usage volume. For high-volume applications processing millions of tokens monthly, the cost reduction can be substantial. Use ourconverter to calculate savings for your specific use case.
Performance Benefits
Reduced token count provides performance advantages beyond cost savings:
Faster Processing
AI models process tokens sequentially. A 50% reduction in token count proportionally decreases processing time.
Increased Context Capacity
Models have fixed context windows. TOON allows twice as much data within the same token limit.
Reduced Network Overhead
Smaller payloads result in faster data transmission and reduced bandwidth consumption.
Beneficial for mobile applications and users with limited connectivity.
Quick Comparison Table
Need a quick reference? Here's everything side-by-side:
| Feature | JSON | TOON |
|---|---|---|
| Token Usage | Standard (baseline) | 50% fewer tokens |
| 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) | Baseline | 50% lower |
| Best For | Web APIs, general use | LLM prompts, AI apps |
When to Use Each Format
Both formats serve distinct purposes. JSON remains optimal for general use, while TOON is specialized for AI model interactions.
JUse JSON For:
Web APIs
For browser-based applications, mobile apps, and third-party API integrations, JSON offers universal compatibility and native support.
Non-AI Applications
When not interacting with Large Language Models, token costs are irrelevant, making JSON's widespread adoption the practical choice.
Maximum Compatibility
Legacy systems and environments with strict dependency constraints benefit from JSON's native support across all platforms.
Complex Nested Structures
Deeply nested objects with heterogeneous structures are well-served by JSON's extensive tooling ecosystem.
TUse TOON For:
AI Model Interactions
When working with GPT-4, Claude, Gemini, or similar LLMs, TOON's 50% token reduction directly translates to cost savings.
High-Volume Processing
Applications processing substantial token volumes benefit from TOON's efficiency, with savings scaling linearly with usage.
Context Window Optimization
When maximizing data within model token limits, TOON's 2x capacity advantage enables more comprehensive context.
Tabular Data Transfer
Structured datasets like customer lists, transaction records, and analytics benefit from TOON's array-oriented format.
Recommended Strategy
The optimal approach involves using both formats strategically:
- •Use JSON for public APIs, mobile applications, and web services
- •Use TOON for AI prompts and LLM interactions
- •Convert between formats as needed (conversion is lossless in both directions)
Format Conversion
Conversion between JSON and TOON is lossless in both directions, preserving all data integrity. Use these tools for format transformation:
JSON to TOON
Convert existing JSON to TOON format and analyze token savings. Includes real-time token count comparison for accurate cost estimation.
Try the ConverterTOON to JSON
Convert TOON back to JSON format for integration with systems expecting standard JSON structure. Maintains complete data fidelity.
Try the ConverterMigration Approach
For production implementation, follow this systematic approach:
- 1.Identify Token-Sensitive Operations
Locate code sections that transmit data to LLMs. These are primary candidates for TOON implementation.
- 2.Validate with Production Data
Test conversion with actual datasets to quantify token savings for your specific use case.
- 3.Verify Model Compatibility
Test TOON format with your LLM to confirm response quality equivalence with JSON inputs.
- 4.Gradual Rollout
Begin with non-critical features in development/staging environments before production deployment.
- 5.Maintain Format Separation
Use TOON exclusively for LLM interactions while maintaining JSON for traditional APIs.
Available Tools
Free tools for working with JSON and TOON formats:
Additional Resources
Further documentation and learning resources:
TOON Resources
- TOON GitHub Repository
Official specification and libraries
- What is TOON Format?
Comprehensive introduction
- TOON Format Specification
Technical documentation
JSON Resources
- JSON Official Website
Official JSON specification
- What is JSON?
JSON introduction guide
- MDN Web Docs - JSON
Mozilla's JSON guide
AI and LLM Resources
- OpenAI Tokenizer
Test token counts
- OpenAI API Pricing
Current pricing information
- Anthropic Claude API
Claude API documentation
Related Guides
- TOON for LLM Prompts
Best practices
- JSON to TOON Guide
Conversion instructions
- TOON Examples
Real-world use cases