If you are building AI agents or working with Large Language Models (LLMs) like GPT-4 or Claude 3.5 Sonnet, you know the pain: Context windows are expensive.
Every time you feed a massive JSON dataset into a prompt, you are burning tokens on structural "fluff"—quotation marks, repeated keys, and endless curly brackets.
There is a better way. It’s called TOON (Token-Oriented Object Notation).
JSON is great for APIs, but it is terrible for LLM context windows. Let's look at a standard user object:
[ { "id": 1, "role": "admin", "status": "active" },
{ "id": 2, "role": "user", "status": "inactive" },
{ "id": 3, "role": "user", "status": "active" }]
The Issue: The keys "id", "role", and "status" are repeated for every single object. In a dataset of 1,000 users, you are paying for those words 1,000 times.
TOON strips away the redundancy. It defines the keys once and then lists the values, similar to a compressed CSV but designed for object structures.
Here is that same data in TOON:
Plaintext
|id|role|status| 1;admin;active 2;user;inactive 3;user;active
The Result:
JSON: ~160 characters (high token count)
TOON: ~65 characters (~60% reduction)
When using models like GPT-4, you pay per million input tokens. If your application processes extensive data (like log analysis, e-commerce product feeds, or user histories), switching the context format from JSON to TOON can literally cut your monthly API bill in half.
The AI model understands TOON perfectly fine because it preserves the relationship between the header (keys) and the rows (values), but creates a much denser information stream.
You don't need to write a custom script to do this. I’ve built a free, client-side tool that instantly converts your heavy JSON files into token-optimized TOON.
Try the Free JSON to TOON Converter Here
It runs entirely in your browser (so your data stays private) and includes a visual diff checker so you can see exactly how much space you’re saving.