DailyTools

JSON to TOON Converter

Convert JSON to TOON format and reduce AI token usage by up to 50%. Save money on OpenAI, Claude, and other LLM API costs with optimized data formatting. Perfect for uniform data structures.

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JSON to TOON Converter
Convert JSON to TOON format and reduce AI token usage by up to 60%
JSON Input
37 lines0 tokens
TOON Output

Output will appear here

Click ```Convert``` to see the optimized result

Standard JSON Format
Universal: Supported everywhere, standard data format
Token Heavy: Lots of syntax overhead (braces, quotes, colons)
Repetitive: Key names repeated for every object
Best For: Complex nested data, API responses, mixed types
TOON Format (Token Optimized)
40-60% Fewer Tokens: Massive reduction in token usage
Cost Savings: Dramatically lower LLM API costs
Simple Format: Header row + data rows, pipe-separated
Best For: Uniform arrays, tabular data, database records
💡 When to Use TOON Format
Database Records: Converting SQL results to LLM input
User Lists: Customer data, user profiles, analytics
Product Catalogs: E-commerce inventory, pricing data
CSV-like Data: Spreadsheet exports, tabular reports
Log Analysis: Server logs, event data with uniform schema
Batch Processing: Processing multiple similar records
High-Volume Apps: Applications with heavy LLM usage
Cost Optimization: When API costs are a concern
🤖 Using TOON with LLMs

Example Prompt for GPT-4:

"The following data is in TOON format where the first row contains column headers separated by | and subsequent rows contain values in the same order. Please analyze this data...\n\n{your TOON data here}"
GPT-4
Works perfectly with brief explanation
Claude
Understands TOON format naturally
Gemini
Recognizes tabular format easily

Complete Guide to TOON Format and Token Optimization

TOON (Token-Optimized Object Notation) is a specialized data format designed to dramatically reduce token usage when working with Large Language Models (LLMs) like GPT-4, Claude, Gemini, and other AI APIs. By converting uniform JSON data structures to TOON format, you can reduce token consumption by 40-60%, resulting in significant cost savings for AI-powered applications.

What is TOON Format?

Understanding TOON

  • Purpose: Reduce token usage for uniform data structures in LLM contexts
  • Design: Header-based format that defines structure once, then lists values
  • Target: Arrays of objects with consistent properties (tables, lists, records)
  • Benefit: 40-60% token reduction compared to standard JSON
  • Use Case: API responses, database records, CSV-like data, bulk data
  • Format: Header row defines keys, subsequent rows contain only values

How TOON Works

  • Header Definition: First row lists all column names/keys
  • Value Rows: Each row contains values in header order
  • Separator: Pipe (|) character separates columns
  • No Repetition: Keys not repeated for every object
  • Compression: Removes JSON syntax overhead (braces, quotes, colons)
  • Readability: Still human-readable, similar to CSV

JSON vs TOON Comparison

Traditional JSON Format

[
  {"name": "Alice", "age": 30, "city": "NYC"},
  {"name": "Bob", "age": 25, "city": "LA"},
  {"name": "Carol", "age": 35, "city": "Chicago"}
]

Token Count: ~45 tokens

TOON Format

name|age|city
Alice|30|NYC
Bob|25|LA
Carol|35|Chicago

Token Count: ~18 tokens
Savings: 60% fewer tokens!

Token Reduction Benefits

Cost Savings Calculation

  • GPT-4: $0.03 per 1K input tokens → Save $0.018 per 1K tokens (60% reduction)
  • GPT-3.5 Turbo: $0.0015 per 1K tokens → Save $0.0009 per 1K tokens
  • Claude 3 Opus: $0.015 per 1K tokens → Save $0.009 per 1K tokens
  • Claude 3 Sonnet: $0.003 per 1K tokens → Save $0.0018 per 1K tokens
  • Annual Savings: For 1M tokens/month, save $180-540/year with GPT-4

When Token Savings Matter Most

  • High Volume Applications: Apps processing thousands of requests daily
  • Data Analysis: Sending large datasets to LLMs for analysis
  • Batch Processing: Processing multiple records in single API calls
  • Real-time Systems: Applications with tight latency requirements
  • Budget Constraints: Startups and projects with limited API budgets
  • Embeddings: Reducing token count for vector database operations

Ideal Use Cases for TOON

Perfect Scenarios

  • Database Records: Converting SQL query results to LLM input
  • CSV Data: Processing CSV files with LLM analysis
  • API Responses: Sending API list responses to LLMs
  • User Lists: Processing user data, customer records
  • Product Catalogs: E-commerce product data for AI analysis
  • Log Analysis: Server logs, event logs with uniform structure
  • Analytics Data: Time-series data, metrics, statistics
  • Spreadsheet Data: Excel/Google Sheets data for LLM processing

Not Recommended For

  • Deeply Nested Objects: Complex hierarchical JSON structures
  • Inconsistent Schemas: Objects with varying properties
  • Single Objects: One-off object conversion (no benefit)
  • Mixed Data Types: Arrays with different object structures
  • Small Datasets: Less than 3-5 rows (minimal savings)
  • Complex Relationships: Data with many nested relationships

Token Counting Basics

How LLMs Count Tokens

  • Tokenization: Text broken into subword units (tokens)
  • English Words: Average 1 word = 1.3 tokens
  • Punctuation: Each punctuation mark typically 1 token
  • Numbers: Multi-digit numbers may be multiple tokens
  • Spaces: Leading spaces can add tokens
  • JSON Syntax: Braces, brackets, quotes, colons all consume tokens

Token Overhead in JSON

  • Braces: around each object = 2 tokens × objects
  • Brackets: [ ] around array = 2 tokens
  • Quotes: " " around keys and values = 4+ tokens per field
  • Colons: : separator = 1 token per field
  • Commas: , separator = 1 token per field
  • Total Overhead: ~8-10 tokens per object property in JSON

TOON Format Specification

Format Rules

  • Header Row: First row contains column names separated by |
  • Data Rows: Subsequent rows contain values in same order
  • Separator: Pipe character (|) separates columns
  • Line Breaks: Each row on new line
  • Empty Values: Empty string between pipes for null/undefined
  • Escaping: Pipe characters in values should be escaped or quoted
  • Consistency: All rows must have same number of columns

Data Type Handling

  • Strings: Plain text, no quotes needed (unless containing |)
  • Numbers: Plain numbers, no special formatting
  • Booleans: true/false as plain text
  • Null: Empty value or "null" string
  • Arrays: Not supported in basic TOON (flatten or use JSON notation)
  • Objects: Not supported nested (flatten keys or use dot notation)

Practical Implementation

Converting JSON to TOON

Step 1: Identify uniform array of objects

Step 2: Extract all unique keys from objects

Step 3: Create header row with keys

Step 4: Create data rows with values in order

Step 5: Join with pipe separator

Converting TOON to JSON

Step 1: Split into lines

Step 2: Parse header row for keys

Step 3: Parse each data row

Step 4: Map values to keys

Step 5: Create JSON array of objects

Real-World Examples

E-commerce Product Catalog

JSON (120 tokens):

[
  {"id": 1, "name": "Laptop", "price": 999, "stock": 50},
  {"id": 2, "name": "Mouse", "price": 29, "stock": 200},
  {"id": 3, "name": "Keyboard", "price": 79, "stock": 150}
]

TOON (45 tokens - 62% savings):

id|name|price|stock
1|Laptop|999|50
2|Mouse|29|200
3|Keyboard|79|150

User Analytics Data

JSON (180 tokens):

[
  {"user": "alice@email.com", "visits": 45, "conversions": 3, "revenue": 450},
  {"user": "bob@email.com", "visits": 23, "conversions": 1, "revenue": 150},
  {"user": "carol@email.com", "visits": 67, "conversions": 5, "revenue": 890}
]

TOON (65 tokens - 64% savings):

user|visits|conversions|revenue
alice@email.com|45|3|450
bob@email.com|23|1|150
carol@email.com|67|5|890

Advanced Optimization Techniques

Column Ordering

  • Most Important First: Put critical columns at start
  • Alphabetical: For consistency across datasets
  • Type Grouping: Group similar data types together
  • Frequency Based: Most frequently accessed columns first

Value Optimization

  • Abbreviations: Use standard abbreviations where possible
  • Code Values: Replace long strings with codes (Y/N vs Yes/No)
  • Numeric IDs: Use numeric references instead of long names
  • Remove Units: Document units in header, omit from values

LLM Integration Best Practices

Using TOON with GPT-4/ChatGPT

  • Context Prompt: Explain TOON format in system message
  • Example: "Data is in TOON format: header row defines columns, | separates values"
  • Instructions: Tell LLM to interpret first row as headers
  • Parsing: LLMs understand TOON naturally after brief explanation

Using TOON with Claude

  • Format Description: Describe TOON format in prompt
  • XML Tags: Can wrap TOON in XML tags for clarity
  • Example Data: Include 1-2 sample rows in prompt
  • Clear Instructions: Specify how to interpret the format

Using TOON with Other LLMs

  • Universal Understanding: Most LLMs recognize tabular formats
  • Brief Explanation: 1-2 sentence format explanation sufficient
  • Consistent Format: Use same format across requests
  • Test First: Verify LLM understands with small example

Cost Analysis and ROI

Monthly Cost Savings Scenarios

Small Application (100K tokens/month):

  • GPT-4: $3.00 → $1.20 (save $1.80/month, $21.60/year)
  • Claude Opus: $1.50 → $0.60 (save $0.90/month, $10.80/year)
  • GPT-3.5: $0.15 → $0.06 (save $0.09/month, $1.08/year)

Medium Application (1M tokens/month):

  • GPT-4: $30.00 → $12.00 (save $18/month, $216/year)
  • Claude Opus: $15.00 → $6.00 (save $9/month, $108/year)
  • GPT-3.5: $1.50 → $0.60 (save $0.90/month, $10.80/year)

Large Application (10M tokens/month):

  • GPT-4: $300.00 → $120.00 (save $180/month, $2,160/year)
  • Claude Opus: $150.00 → $60.00 (save $90/month, $1,080/year)
  • GPT-3.5: $15.00 → $6.00 (save $9/month, $108/year)

Enterprise Application (100M tokens/month):

  • GPT-4: $3,000 → $1,200 (save $1,800/month, $21,600/year)
  • Claude Opus: $1,500 → $600 (save $900/month, $10,800/year)
  • GPT-3.5: $150 → $60 (save $90/month, $1,080/year)

Performance Considerations

Processing Speed

  • Faster Transmission: Fewer tokens = faster API requests
  • Lower Latency: Reduced network transfer time
  • Quicker Parsing: Simpler format for LLM to process
  • Batch Efficiency: Process more records per request

Context Window Optimization

  • More Data: Fit more records in same context window
  • Better Context: Room for more instructions and examples
  • Longer Conversations: Save tokens for multi-turn dialogues
  • Complex Queries: Space for sophisticated prompts

Limitations and Trade-offs

When Not to Use TOON

  • Complex Nested Data: TOON works best for flat structures
  • Sparse Data: Many null/empty values reduce efficiency
  • Variable Schemas: Objects with different properties
  • Single Records: No benefit for individual objects
  • Mixed Types: Arrays containing different object types
  • Human Readability Priority: JSON is more familiar

Format Limitations

  • No Native Nesting: Nested objects must be flattened
  • Array Handling: Array values need special handling
  • Type Ambiguity: All values treated as strings
  • Separator Conflicts: Pipe character in data needs escaping
  • Less Tool Support: Fewer libraries compared to JSON

Migration Strategy

Gradual Adoption

  • Step 1: Identify high-volume uniform data endpoints
  • Step 2: Convert a single endpoint to TOON
  • Step 3: Measure token savings and performance
  • Step 4: Update LLM prompts to handle TOON
  • Step 5: Roll out to additional endpoints
  • Step 6: Monitor cost savings and adjust

Hybrid Approach

  • Use JSON: For nested, complex, or variable data
  • Use TOON: For uniform tabular data
  • Context Dependent: Choose format based on use case
  • Document Clearly: Make format choice explicit in code

Developer Tools and Libraries

Implementation Tips

  • Conversion Functions: Create reusable converter utilities
  • Type Safety: Add TypeScript types for TOON structures
  • Validation: Validate TOON format before sending
  • Error Handling: Handle conversion errors gracefully
  • Testing: Unit test conversion accuracy
  • Documentation: Document TOON usage in API docs

Future of Token Optimization

Emerging Trends

  • Binary Formats: Even more efficient binary encodings
  • Compression: Built-in compression in API protocols
  • Smart Tokenization: LLMs with better tokenization
  • Format Standards: Industry standards for efficient formats
  • Native Support: LLM APIs natively supporting TOON-like formats

Converter Tool Features

  • Bidirectional Conversion: JSON ↔ TOON with one click
  • Token Counter: Real-time token comparison
  • Savings Calculator: Estimate cost savings in dollars
  • Validation: Automatic format validation
  • Sample Data: Pre-loaded examples for testing
  • Copy & Download: Easy export of converted data
  • Large File Support: Handle thousands of records
  • Client-Side Processing: Complete privacy, no uploads
  • Instant Results: Real-time conversion as you type
  • Mobile Friendly: Works on all devices