Question 1

What is TOON format?

Accepted Answer

TOON (Token-Oriented Object Notation) is a data format designed specifically for LLM applications. It reduces token consumption by 30-60% compared to JSON while maintaining structure and improving model accuracy. TOON uses indentation-based syntax (like YAML) and tabular formats for arrays to eliminate repetitive keys and excess punctuation.

Question 2

How accurate is the token counting?

Accepted Answer

The tool uses the official js-tiktoken library, which is the same tokenizer used by OpenAI and Anthropic. Token counts match exactly what GPT-4 and Claude 3.5 Sonnet models will use, ensuring accurate cost calculations and comparisons.

Question 3

Will TOON work with my existing JSON code?

Accepted Answer

Yes! TOON is designed as a translation layer at the LLM boundary. Your application continues using JSON internally for all logic, APIs, and data storage. You only convert to TOON when sending data to LLMs, and convert back to JSON when receiving responses. This requires no changes to your existing codebase.

Question 4

What types of JSON data benefit most from TOON?

Accepted Answer

TOON delivers maximum savings (40-60%) with uniform arrays of objects, such as product catalogs, employee records, financial transactions, and time-series data. The more repetitive the structure, the greater the token savings. Non-uniform or deeply nested structures may see smaller but still significant savings.

Question 5

How do I use TOON in my application?

Accepted Answer

Install the @toon-format/toon npm package, then use encode() to convert JSON to TOON before LLM calls, and decode() to convert TOON responses back to JSON. The tool provides copy buttons for both formats so you can test the conversion in your code.

Question 6

Are the cost savings estimates accurate?

Accepted Answer

The cost savings are calculated using current pricing for GPT-4 ($10 per 1M input tokens) and Claude 3.5 Sonnet ($3 per 1M input tokens). These are per-1M-token estimates. Actual savings scale linearly with your token usage, so if you process 10M tokens, multiply the savings by 10.

Question 7

Does TOON improve LLM accuracy?

Accepted Answer

Yes! Benchmarks show TOON improves accuracy by 4.2% (73.9% vs 69.7% for JSON) in structured data retrieval tasks. TOON's explicit length markers and schema definitions provide LLM-friendly guardrails that reduce parsing errors and hallucinations.

Question 8

Is my data secure?

Accepted Answer

Absolutely! All processing happens entirely in your browser using client-side JavaScript. Your JSON data never leaves your device and is never uploaded to any server. This ensures complete privacy and security for sensitive data.

JSON vs TOON Comparison Tool - Token Efficiency Calculator

Key Features

100% Private

Accurate Token Counting

Real-Time Comparison

Cost Savings Calculator

Instant Results

TOON Format Support

How It Works

Input JSON Data

Select Token Model

View Comparison

Copy Results

Use Cases

RAG System Optimization

AI Agent Communication

LLM Cost Optimization

Data Serialization

RAG System Optimization

Examples:

Benefits:

Frequently Asked Questions

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