Token-Oriented Object Notation is a compact, human-readable encoding of the JSON data model that minimizes tokens and makes structure easy for models to follow.

TOON combines YAML's indentation-based structure for nested objects with CSV-style tabular forms for uniform data. Its sweet spot is uniform objects – same fields across items, whether in an array or keyed by ID – reaching CSV-like compactness while adding explicit structure that helps LLMs parse and validate data reliably. For deeply nested or non-uniform data, JSON may be more efficient.

Think of it as a translation layer: use JSON programmatically, and encode it as TOON for LLM input – a drop-in, lossless representation of the JSON you already have.

• Why TOON?
• Key Features
• When Not to Use TOON
• Benchmarks
• Installation & Quick Start
• CLI
• Using TOON with LLMs
• Ecosystem
• Documentation
• Media Type & File Extension
• Other Implementations
• 📋 Full Specification

LLM tokens cost money – and JSON spends a lot of them on structure. A weather forecast in TOON:

location:
  city: Berlin
  country: DE
  units: metric
alerts[2]: frost,wind
forecast[3]{day,temp{min,max},condition,rainChance}:
  Mon,-2,4,snow,80
  Tue,1,7,cloudy,20
  Wed,3,11,sunny,5

The same data as JSON – ~117 tokens against TOON's ~66:

{
  "location": {
    "city": "Berlin",
    "country": "DE",
    "units": "metric"
  },
  "alerts": [
    "frost",
    "wind"
  ],
  "forecast": [
    {
      "day": "Mon",
      "temp": {
        "min": -2,
        "max": 4
      },
      "condition": "snow",
      "rainChance": 80
    },
    {
      "day": "Tue",
      "temp": {
        "min": 1,
        "max": 7
      },
      "condition": "cloudy",
      "rainChance": 20
    },
    {
      "day": "Wed",
      "temp": {
        "min": 3,
        "max": 11
      },
      "condition": "sunny",
      "rainChance": 5
    }
  ]
}

Three things are happening at once in the TOON above. Two are forms – one rendering of a value, picked automatically from the data's shape – and the third is a header feature:

• alerts[2]: frost,wind is inline form: a primitive array on its header line.
• forecast[3]{day,…}: is tabular form: the field list is declared once in the header, then one row per element.
• temp{min,max} inside that header is a nested field group: the uniform nested temp objects fold into the header while rows stay flat.

The third form is keyed tabular, for objects whose values are uniform objects – config maps, feature flags, records by ID. The colon after the length ([2:]) marks it, and each row carries its own key:

{
  "environments": {
    "production": { "region": "eu-central-1", "replicas": 6, "debug": false },
    "staging": { "region": "eu-central-1", "replicas": 2, "debug": true }
  }
}

environments[2:]{region,replicas,debug}:
  production: eu-central-1,6,false
  staging: eu-central-1,2,true

Anything that fits none of these – mixed types, non-uniform objects – falls back to the fourth form, list form: one - item per element, or a bare - for an empty object. Those four cover the shapes; the Format Overview covers the rest.

cat data.json | npx @toon-format/cli --stats

ℹ Token estimates: ~117 (JSON) → ~66 (TOON)
✔ Saved ~51 tokens (-43.6%)

• 📊 Token-Efficient & Accurate: Matches JSON's retrieval accuracy while using 42.6% fewer tokens – see Benchmarks.
• 🔁 JSON Data Model: Encodes the same objects, arrays, and primitives as JSON with deterministic, lossless round-trips.
• 🛤️ LLM-Friendly Guardrails: Explicit [N] lengths and {fields} field lists give models a clear schema to follow, improving parsing reliability.
• 📐 Minimal Syntax: Uses indentation instead of braces and minimizes quoting, giving YAML-like readability with CSV-style compactness.
• 🧺 Tabular Forms: Uniform arrays of objects – and objects of uniform objects – collapse into tables that declare the field list once and stream row values line by line.
• 🌐 Multi-Language Ecosystem: Spec-driven implementations in many languages.

TOON excels with uniform arrays of objects. Reach for something else when:

• Structures are deeply nested or non-uniform (tabular eligibility ≈ 0%) – compact JSON often wins outright.
• Arrays are semi-uniform (~40–60% eligibility) – savings shrink; stay on JSON if your pipeline already speaks it.
• Data is purely tabular – CSV is smaller. TOON's ~5–10% overhead buys declared lengths, field lists, and delimiter scoping, which is a reliability trade, not a size one.
• Latency dominates – some deployments (notably local or quantized models) process compact JSON faster despite the higher token count. Measure TTFT and total time on your own setup.

Benchmarks below quantify the token and accuracy trade-offs; latency is the one you have to measure yourself.

Two tracks, so every comparison is like-for-like:

• Mixed-Structure Track: Nested and semi-uniform datasets (TOON vs JSON, YAML, XML). CSV is excluded – it cannot represent these structures without lossy flattening.
• Flat-Only Track: Flat, fully tabular-eligible datasets, where CSV is a fair competitor.

Benchmarks test LLM comprehension across different input formats using 244 data retrieval questions on 4 models.

Each format ranked by efficiency (accuracy percentage per 1,000 tokens):

TOON           ████████████████████   29.2 acc%/1K tok  │  72.2%  ±2.8 acc  │  2,474 tokens
JSON compact   ████████████████░░░░   23.8 acc%/1K tok  │  69.0%  ±2.9 acc  │  2,892 tokens
YAML           ██████████████░░░░░░   20.1 acc%/1K tok  │  70.1%  ±2.9 acc  │  3,487 tokens
JSON           ███████████░░░░░░░░░   16.6 acc%/1K tok  │  71.4%  ±2.8 acc  │  4,308 tokens
XML            ██████████░░░░░░░░░░   14.4 acc%/1K tok  │  70.7%  ±2.9 acc  │  4,909 tokens

Efficiency score = (Accuracy % ÷ Tokens) × 1,000. Higher is better.

Every format answers the same 109 flat-dataset questions per model, so CSV can be compared on equal footing here.

Accuracy across 4 LLMs on 244 data retrieval questions:

claude-haiku-4-5-20251001
→ TOON           █████████████░░░░░░░    65.6% ±5.9 (160/244)
  JSON           █████████████░░░░░░░    63.5% ±6.0 (155/244)
  XML            ████████████░░░░░░░░    62.3% ±6.0 (152/244)
  YAML           ████████████░░░░░░░░    62.3% ±6.0 (152/244)
  JSON compact   ████████████░░░░░░░░    61.9% ±6.0 (151/244)
  CSV            ██████████░░░░░░░░░░    49.5% ±9.2 (54/109)

gemini-3.6-flash
→ TOON           ██████████████░░░░░░    69.3% ±5.8 (169/244)
  JSON           ██████████████░░░░░░    68.4% ±5.8 (167/244)
  YAML           ██████████████░░░░░░    67.6% ±5.8 (165/244)
  XML            █████████████░░░░░░░    65.2% ±5.9 (159/244)
  JSON compact   █████████████░░░░░░░    63.5% ±6.0 (155/244)
  CSV            ████████████░░░░░░░░    57.8% ±9.1 (63/109)

gpt-5.4-nano
  XML            ████████████░░░░░░░░    59.4% ±6.1 (145/244)
  JSON           ███████████░░░░░░░░░    57.4% ±6.2 (140/244)
→ TOON           ███████████░░░░░░░░░    57.0% ±6.2 (139/244)
  JSON compact   ███████████░░░░░░░░░    54.9% ±6.2 (134/244)
  YAML           ███████████░░░░░░░░░    54.5% ±6.2 (133/244)
  CSV            █████████░░░░░░░░░░░    46.8% ±9.2 (51/109)

grok-4.5
→ TOON           ███████████████████░    97.1% ±2.2 (237/244)
  JSON           ███████████████████░    96.3% ±2.5 (235/244)
  XML            ███████████████████░    95.9% ±2.6 (234/244)
  YAML           ███████████████████░    95.9% ±2.6 (234/244)
  JSON compact   ███████████████████░    95.5% ±2.7 (233/244)
  CSV            ███████████████████░    94.5% ±4.5 (103/109)

• Models tested: claude-haiku-4-5-20251001, gemini-3.6-flash, gpt-5.4-nano, grok-4.5
• Formats compared: TOON, JSON, XML, YAML, JSON compact, CSV
• Token counting: Using gpt-tokenizer with o200k_base encoding (GPT-5 tokenizer). Other providers tokenize differently, so absolute counts are tokenizer-specific; relative differences between formats hold directionally.
• Reasoning: Disabled via the AI SDK's universal reasoning: 'none' (Gemini 3 floors at minimal thinking, grok-4.5 at low)
• Temperature: Not set (models use their defaults)
• Total evaluations: 244 questions × 6 formats × 4 models = 5,856 LLM calls

What the datasets contain, how the questions are generated, and how answers are validated is documented in the benchmark README.

Token counts are measured using the GPT-5 o200k_base tokenizer via gpt-tokenizer. Savings are calculated against formatted JSON (2-space indentation) as the primary baseline, with additional comparisons to compact JSON (minified), YAML, and XML. Actual savings vary by model and tokenizer.

The benchmarks test datasets across different structural patterns (uniform, semi-uniform, nested, deeply nested) to show where TOON excels and where other formats may be better.

Datasets with nested or semi-uniform structures. CSV excluded as it cannot properly represent these structures.

🛒 E-commerce orders with nested structures  ┊  Tabular: 33%
   │
   TOON                █████████████░░░░░░░    72,832 tokens
   ├─ vs JSON          (−32.9%)               108,611 tokens
   ├─ vs JSON compact  (+5.6%)                 68,944 tokens
   ├─ vs YAML          (−14.0%)                84,701 tokens
   └─ vs XML           (−40.4%)               122,119 tokens

🧾 Semi-uniform event logs  ┊  Tabular: 50%
   │
   TOON                █████████████████░░░   154,084 tokens
   ├─ vs JSON          (−15.0%)               181,201 tokens
   ├─ vs JSON compact  (+19.9%)               128,529 tokens
   ├─ vs YAML          (−0.8%)                155,397 tokens
   └─ vs XML           (−25.2%)               205,859 tokens

🧩 Deeply nested configuration  ┊  Tabular: 0%
   │
   TOON                █████████████░░░░░░░       589 tokens
   ├─ vs JSON          (−34.9%)                   905 tokens
   ├─ vs JSON compact  (+6.7%)                    552 tokens
   ├─ vs YAML          (−11.0%)                   662 tokens
   └─ vs XML           (−40.9%)                   997 tokens

📊 Feature flags keyed by name  ┊  Tabular: 100%
   │
   TOON                █████████░░░░░░░░░░░    10,503 tokens
   ├─ vs JSON          (−54.6%)                23,141 tokens
   ├─ vs JSON compact  (−32.8%)                15,635 tokens
   ├─ vs YAML          (−41.3%)                17,905 tokens
   └─ vs XML           (−63.3%)                28,655 tokens

📊 Contacts with nested address and plan groups  ┊  Tabular: 100%
   │
   TOON                ███████░░░░░░░░░░░░░    26,726 tokens
   ├─ vs JSON          (−66.5%)                79,779 tokens
   ├─ vs JSON compact  (−42.9%)                46,791 tokens
   ├─ vs YAML          (−51.8%)                55,475 tokens
   └─ vs XML           (−70.4%)                90,306 tokens

──────────────────────────────────── Total ────────────────────────────────────
   TOON                █████████████░░░░░░░   264,734 tokens
   ├─ vs JSON          (−32.7%)               393,637 tokens
   ├─ vs JSON compact  (+1.6%)                260,451 tokens
   ├─ vs YAML          (−15.7%)               314,140 tokens
   └─ vs XML           (−40.9%)               447,936 tokens

Datasets with flat, fully tabular-eligible data where CSV is applicable.

👥 Uniform employee records  ┊  Tabular: 100%
   │
   CSV                 ███████████████████░    47,153 tokens
   TOON                ████████████████████    49,978 tokens   (+6.0% vs CSV)
   ├─ vs JSON          (−60.7%)               127,061 tokens
   ├─ vs JSON compact  (−36.8%)                79,057 tokens
   ├─ vs YAML          (−50.0%)               100,054 tokens
   └─ vs XML           (−65.9%)               146,605 tokens

📈 Time-series analytics data  ┊  Tabular: 100%
   │
   CSV                 ██████████████████░░     8,383 tokens
   TOON                ████████████████████     9,115 tokens   (+8.7% vs CSV)
   ├─ vs JSON          (−59.0%)                22,245 tokens
   ├─ vs JSON compact  (−35.9%)                14,211 tokens
   ├─ vs YAML          (−49.0%)                17,858 tokens
   └─ vs XML           (−65.8%)                26,616 tokens

⭐ Top 100 GitHub repositories  ┊  Tabular: 100%
   │
   CSV                 ███████████████████░     8,711 tokens
   TOON                ████████████████████     8,937 tokens   (+2.6% vs CSV)
   ├─ vs JSON          (−41.7%)                15,337 tokens
   ├─ vs JSON compact  (−23.2%)                11,640 tokens
   ├─ vs YAML          (−33.0%)                13,337 tokens
   └─ vs XML           (−48.3%)                17,294 tokens

──────────────────────────────────── Total ────────────────────────────────────
   CSV                 ███████████████████░    64,247 tokens
   TOON                ████████████████████    68,030 tokens   (+5.9% vs CSV)
   ├─ vs JSON          (−58.7%)               164,643 tokens
   ├─ vs JSON compact  (−35.2%)               104,908 tokens
   ├─ vs YAML          (−48.2%)               131,249 tokens
   └─ vs XML           (−64.3%)               190,515 tokens

Token counts use gpt-tokenizer with o200k_base encoding (GPT-5 tokenizer). Other providers tokenize differently, so absolute counts are tokenizer-specific; relative differences between formats hold directionally.

{
  "metrics": [
    {
      "date": "2025-01-01",
      "views": 6138,
      "clicks": 174,
      "conversions": 12,
      "revenue": 2712.49,
      "bounceRate": 0.35
    },
    {
      "date": "2025-01-02",
      "views": 4616,
      "clicks": 274,
      "conversions": 34,
      "revenue": 9156.29,
      "bounceRate": 0.56
    },
    {
      "date": "2025-01-03",
      "views": 4460,
      "clicks": 143,
      "conversions": 8,
      "revenue": 1317.98,
      "bounceRate": 0.59
    },
    {
      "date": "2025-01-04",
      "views": 4740,
      "clicks": 125,
      "conversions": 13,
      "revenue": 2934.77,
      "bounceRate": 0.37
    },
    {
      "date": "2025-01-05",
      "views": 6428,
      "clicks": 369,
      "conversions": 19,
      "revenue": 1317.24,
      "bounceRate": 0.3
    }
  ]
}

metrics[5]{date,views,clicks,conversions,revenue,bounceRate}:
  2025-01-01,6138,174,12,2712.49,0.35
  2025-01-02,4616,274,34,9156.29,0.56
  2025-01-03,4460,143,8,1317.98,0.59
  2025-01-04,4740,125,13,2934.77,0.37
  2025-01-05,6428,369,19,1317.24,0.3

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# npm
npm install @toon-format/toon

# pnpm
pnpm add @toon-format/toon

# yarn
yarn add @toon-format/toon

To keep the CLI around instead of invoking it through npx, install it globally:

npm install -g @toon-format/cli

Example usage:

import { encode } from '@toon-format/toon'

const data = {
  users: [
    { id: 1, name: 'Ada', role: 'admin' },
    { id: 2, name: 'Bob', role: 'user' }
  ]
}

console.log(encode(data))
// users[2]{id,name,role}:
//   1,Ada,admin
//   2,Bob,user

Streaming large datasets:

import { encodeLines } from '@toon-format/toon'

const largeData = await fetchThousandsOfRecords()

// Memory-efficient streaming for large data
for (const line of encodeLines(largeData)) {
  process.stdout.write(`${line}\n`)
}

Transforming values with replacer:

import { encode } from '@toon-format/toon'

// Remove sensitive fields
const user = { name: 'Ada', password: 'secret', email: 'ada@example.com' }
const safe = encode(user, {
  replacer: (key, value) => key === 'password' ? undefined : value
})
// name: Ada
// email: ada@example.com

Command-line tool for quick JSON↔TOON conversions, token analysis, and pipeline integration. Auto-detects format from file extension, supports stdin/stdout workflows, and offers delimiter options (comma, tab, pipe) that trade readability for fewer tokens.

# Encode JSON to TOON (auto-detected)
npx @toon-format/cli input.json -o output.toon

# Decode TOON to JSON (auto-detected)
npx @toon-format/cli data.toon -o output.json

# Pipe from stdin (no argument needed)
cat data.json | npx @toon-format/cli
echo '{"name": "Ada"}' | npx @toon-format/cli

# Output to stdout
npx @toon-format/cli input.json

# Show token savings
npx @toon-format/cli data.json --stats

TOON works best when you show the format instead of describing it. Once a model sees one tabular example, the header – [N] length plus {fields} field list – tells it how to read the rest. Wrap data in ```toon code blocks for input, and show the expected header template when asking models to generate TOON. Tab delimiters buy further token savings. Full-line # comments are stripped on decode, so hand-annotated prompt data – and model output with explainer lines – still decodes cleanly.

Follow the detailed LLM integration guide for strategies, examples, and validation techniques.

Playgrounds – the official playground converts JSON or YAML to TOON in real time, compares token counts, and shares experiments by URL. Community alternatives: Format Tokenization Playground, TOON Tools.

Editors – TOON Language Support for VS Code (code --install-extension vishalraut.vscode-toon) adds highlighting, validation, and token analysis. tree-sitter-toon covers Neovim, Helix, Emacs, and Zed; toon.nvim is a Lua-native alternative. Elsewhere, YAML highlighting is a close approximation.

Tooling – Tooner is an MCP proxy that converts JSON tool responses to TOON.

• Introduction & Installation – What TOON is, when to use it, first steps
• Format Overview – Complete syntax with examples
• Benchmarks – Accuracy & token efficiency results

• CLI – Command-line tool for JSON↔TOON conversions
• Playgrounds – Interactive tools
• Using TOON with LLMs – Prompting strategies & validation

• API Reference – TypeScript/JavaScript encode/decode API
• Syntax Cheatsheet – Quick format lookup
• Specification – Normative rules for implementers
• Glossary – One name per concept, for contributors and tooling

TOON files use the .toon extension and the provisional media type text/toon. Documents are always UTF-8; the charset=utf-8 parameter may be given but is assumed when absent. See SPEC.md §17 for normative details.

TOON has official and community implementations across multiple languages including Python, Rust, Go, Java, Swift, .NET, and many more.

See the full list of implementations in the documentation.

• Logo design by 鈴木ックス(SZKX)

MIT License © 2025-PRESENT Johann Schopplich