Meet TOON: JSON for LLMs – Benchmarking, Use Cases & Best Practices

Token Inefficiency in JSON: The LLM Challenge

Large Language Models (LLMs) such as GPT-4, Claude, and Gemini have transformed AI-driven applications—whether answering complex queries, powering Retrieval-Augmented Generation (RAG), or automating knowledge workflows. As adoption increases, developers and data scientists encounter a fundamental challenge: the cost and limitations imposed by LLM tokens.

Every character transmitted to or from an LLM counts against token limits—a direct impact on API costs and a constraint on the amount of data that can be processed in a single context window. The default format for structured data exchange, JSON (JavaScript Object Notation), is ubiquitous and machine-friendly. However, JSON’s verbose punctuation, repeated field names, and extraneous syntactic elements noticeably inflate token usage.

For example:

The curly braces, deeply nested quotes, colons, and commas—in addition to whitespace—all translate into extra tokens.
As datasets grow larger or structures grow more complex,
this ballooning overhead can have real financial and technical consequences.

Transition to TOON: Designed for the LLM Era

To address these inefficiencies, developers are rethinking data serialization for AI-first software. TOON is a modern, compact serialization format crafted specifically for optimal token utility in LLM workflows. Unlike JSON,
TOON eliminates unnecessary characters and repetitions—maximizing both clarity and cost-effectiveness
for high-throughput, data-centric pipelines.

• YAML-style indentation: Nesting indicated by whitespace, not brackets.
• CSV-style tables: Schemas declared once, followed by data rows.
• Minimal punctuation: Little to no quotes or brackets, just the core data.
• Explicit field lists and lengths: Enhances comprehension for both LLMs and humans.

Code Example Comparison

JSON:

TOON:

TOON strips away excess punctuation and repetition, presenting data in a way that’s lighter on tokens and easier to interpret for AI models.

Why Use TOON? Real-World Gains

At WireUnwired Research, we’ve conducted extensive tests comparing JSON and TOON in real LLM-powered applications.
The results show dramatic reductions in token consumption when switching to TOON—often with substantial cost and context window savings.

Here’s a summary from our recent WireUnwired benchmarks:

In practice, TOON consistently delivered 30–60% fewer tokens for tabular and flat datasets.
We did find that for highly nested or irregular structures,
TOON’s token footprint can sometimes exceed that of JSON, which underscores the importance of benchmarking with your own data.

Beyond Token Savings

• Readability: TOON proved easier to interpret for both humans and systems—especially in collaborative and retrieval scenarios.
• Accuracy: Simplified declarations and structure led to more reliable outputs in LLM retrieval experiments.

Best Practices and Actionable Advice

• Ideal Use Case: Flat/tabular data—such as bulk records, retrieval pipelines, and uniform API outputs.
• Optimize: Always flatten your data before conversion for the greatest token efficiency.
• Caution Zone: Deeply nested, tree-like, or irregular structures—profile both formats, as TOON may not always be more compact.
• WireUnwired Pro Tip: Benchmark your real prompts before adopting TOON at scale.

Deeply nested or heterogeneous data can occasionally inflate the token count in TOON.
Regular profiling and experimentation are critical to maximizing savings.

Community & Ecosystem

TOON enjoys robust multi-language support—TypeScript, Python, Go, Rust, Dart, Elixir, and more. The ecosystem is bolstered by online playgrounds (jsontoon.com), CLI converters, browser-based editors, and a thriving open-source community with thousands of contributors and frequent enhancements.

Conclusion: TOON in Perspective

For high-volume LLM pipelines, every token counts. WireUnwired’s research confirms TOON’s practical advantages—delivering savings, clarity, and scalability wherever tabular data structures dominate. Flatten data before adopting TOON; reserve JSON for complex or irregular needs. Stay agile: experiment, validate, and leverage open-source tools to automate and optimize your workflow.

Explore TOON Further

• GitHub Repository
• VirtusLab Benchmarks & Blog
• Online Playground