JSON vs TOON: The Token Battle
⚡ JSON vs TOON
Every Token Counts in the LLM Era
🎯 What Makes TOON Different?
While JSON uses explicit delimiters and repeats keys for every object, TOON relies on indentation and column headers to avoid repeating structure. It borrows indentation from YAML and tabular layouts from CSV to represent uniform arrays tightly, eliminating much of JSON’s repeated field names and punctuation.
⚔️ The Showdown: Key Differences
📦 JSON Strengths
- Universal ecosystem support
- Works everywhere (browsers, APIs, databases)
- Handles deeply nested, irregular data
- Industry standard for public APIs
- First-class support in every language
🚀 TOON Advantages
- 40-50% token reduction vs JSON
- Higher LLM retrieval accuracy
- CSV-like compactness for arrays
- Optimized for AI prompts
- Perfect for analytics & event logs
🎪 Where Each Format Shines
- Designing public APIs or browser-facing services
- You need long-term stability and broad language support
- Working with deeply nested or irregular data structures
- Integrating with existing toolchains (Swagger, Postman, ORMs)
- Building microservices with standard REST contracts
- Building internal LLM prompts and RAG pipelines
- Token cost and structured retrieval are priorities
- Working with uniform arrays (analytics, event logs, user records)
- Streaming data into language models
- Building AI-first applications where efficiency matters
💡 The Pragmatic Approach
In many AI-first systems, the winning strategy is hybrid: JSON for the outside world (public APIs, browser communication), and TOON as an internal “prompt wire format” between services and models. This gives you universal compatibility where it matters, and maximum efficiency where it counts.
📊 Real-World Performance
Experiments on multiple LLMs show TOON achieves around 73–74% overall retrieval accuracy versus roughly 69–70% for JSON, while using almost 40% fewer tokens across mixed datasets. On individual models, TOON often matches or beats JSON on field retrieval, aggregation, and structure-awareness questions, especially when the data is mostly tabular.
Ready to Cut Your Token Costs?
TOON isn’t replacing JSON—it’s complementing it. For LLM-heavy workloads, analytics pipelines, and AI applications, TOON offers a compelling path to lower costs and better accuracy. Start experimenting with TOON today and see the token savings add up!
