For years, JSON has been the language of data on the web lightweight, flexible, and universally understood. But as technology evolves, so do our needs.
Today, in the age of AI models and token-based computation, a new format is quietly making waves: TOON (Token Oriented Object Notation).
While JSON was built for the web, TOON is being shaped for the AI era where every token and every bit of structure affects cost, performance, and readability.
What is JSON?
JSON (JavaScript Object Notation) is the world’s most familiar data format. It’s simple, text-based, and language agnostic, which made it the perfect choice for APIs, configurations, and data exchange between services.
Example
Pros
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Universal support across tools and frameworks
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Easy to parse and generate
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Great for REST APIs and configs
Cons
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Verbose syntax (lots of quotes, braces, and commas)
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No support for comments
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Inefficient in LLMs (every symbol counts as a token)
What is TOON?
TOON (Token Oriented Object Notation) is a modern format reimagining data structure with token economy in mind especially for AI and LLM driven systems.
Instead of repeating symbols and keys, TOON organizes data in a compact, columnar layout, using indentation and headers instead of heavy punctuation.
Example
Key Advantages of TOON
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Less punctuation no braces, quotes, or commas everywhere
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Human readable clean to skim and edit
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Token efficient smaller input size for LLMs
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Supports comments and custom types
JSON vs TOON: Feature Comparison
Real World Token Comparison: JSON vs TOON
Let’s see how much token difference there really is when feeding the same data to an LLM.
JSON Example
Approximate tokens: ~65 tokens
TOON Example
Approximate tokens: ~25 tokens
Token Savings
~45 percent fewer tokens
Try It Yourself
Paste both examples into any token counter tool and compare the results live.
Recommended: Official OpenAI Tokenizer.
Why TOON Matters in the AI Era
When working with LLMs, data efficiency has a real financial and performance impact. Every extra bracket, quote, or key name adds tokens, which models must process and charge for.
TOON is designed with that in mind:
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Reduced token count equals lower cost per prompt
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Less visual clutter leads to fewer human errors
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Simplified syntax gives faster parsing in AI pipelines
Think of it as JSON reimagined for intelligent systems.
When to Use JSON
Use JSON when:
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Building REST APIs or frontend backend integrations
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Relying on mature tools like jsonschema
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You need interoperability across languages and platforms
JSON remains unbeatable for universal compatibility.
When to Use TOON
Use TOON when:
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Working with LLMs, AI agents, or chat based pipelines
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Minimizing token usage is a priority
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Data is structured or repetitive
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You want a human friendly representation for complex prompts
TOON shines when compactness and readability matter.
Future Outlook
JSON will continue powering most of the web its ecosystem is massive and reliable. But formats like TOON show where the next generation of data exchange is headed: efficiency, clarity, and AI awareness.
In the future, you might see:
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AI workflows using TOON internally
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APIs exposing JSON externally
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Converters bridging both worlds
The web was built on JSON. The AI future might be built on TOON.
Bonus: Convert JSON to TOON (Conceptual Example)
Pseudo code
Output
How PySquad Can Help
PySquad & Nivalabs AI specializes in building AI centric ecosystems, and formats like TOON align perfectly with the next generation of data driven workflows. Here’s how PySquad can support teams and enterprises adopting TOON:
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AI Pipeline Optimization: We design pipelines where TOON reduces token usage and improves response performance.
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Custom TOON Converters: We build JSON↔TOON converters for seamless migration without breaking existing systems.
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LLM Workflow Engineering: Our prompt engineering and RAG expertise ensures TOON structures enhance LLM understanding.
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Tooling and Automation: PySquad can build validators, editors, and format aware tooling to integrate TOON into your internal workflows.
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Consulting and Implementation: From architecture to deployment, we help you integrate TOON into AI agents, automation flows, and internal knowledge systems.
By pairing TOON’s token efficiency with PySquad’s AI engineering strengths, organizations can cut costs, improve clarity, and build future ready AI operations.
Conclusion
Both JSON and TOON have their place.
JSON made data portable. TOON makes data efficient.
