wehub/toon-format--toon
1
0
Fork 0
Code Issues Pull Requests Actions 2 Deployments Models Agent Notes Packages Projects Releases Wiki Activity
Files
main
toon-format--toon/docs/guide/benchmarks.md
T
wehub-resource-sync bde7ea0d58
CI / ci (push) Waiting to run
Details
Deploy Docs / Deploy Docs (push) Waiting to run
Details
chore: import upstream snapshot with attribution
2026-07-13 12:39:56 +08:00

24 KiB
Raw Permalink Blame History

description
description
Retrieval accuracy and token efficiency results for TOON across mixed-structure and flat-only tracks.

Benchmarks

The benchmarks on this page measure TOON's performance across two key dimensions:

  • Retrieval Accuracy: How well LLMs understand and extract information from different input formats.
  • Token Efficiency: How many tokens each format requires to represent the same data.

Benchmarks are organized into two tracks to ensure fair comparisons:

  • Mixed-Structure Track: Datasets with nested or semi-uniform structures (TOON vs JSON, YAML, XML). CSV excluded as it cannot properly represent these structures.
  • Flat-Only Track: Datasets with flat tabular structures where CSV is applicable (CSV vs TOON vs JSON, YAML, XML).

Retrieval Accuracy

Benchmarks test LLM comprehension across different input formats using 209 data retrieval questions on 4 models.

Show Dataset Catalog

Dataset Catalog

Dataset Rows Structure CSV Support Eligibility
Uniform employee records 100 uniform ✓ 100%
E-commerce orders with nested structures 50 nested ✗ 33%
Time-series analytics data 60 uniform ✓ 100%
Top 100 GitHub repositories 100 uniform ✓ 100%
Semi-uniform event logs 75 semi-uniform ✗ 50%
Deeply nested configuration 11 deep ✗ 0%
Valid complete dataset (control) 20 uniform ✓ 100%
Array truncated: 3 rows removed from end 17 uniform ✓ 100%
Extra rows added beyond declared length 23 uniform ✓ 100%
Inconsistent field count (missing salary in row 10) 20 uniform ✓ 100%
Missing required fields (no email in multiple rows) 20 uniform ✓ 100%

Structure classes:

  • uniform: All objects have identical fields with primitive values
  • semi-uniform: Mix of uniform and non-uniform structures
  • nested: Objects with nested structures (nested objects or arrays)
  • deep: Highly nested with minimal tabular eligibility

CSV Support: ✓ (supported), ✗ (not supported – would require lossy flattening)

Eligibility: Percentage of arrays that qualify for TOON's tabular format (uniform objects with primitive values)

Efficiency Ranking (Accuracy per 1K Tokens)

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

TOON           ████████████████████   27.7 acc%/1K tok  │  76.4% acc  │  2,759 tokens
JSON compact   █████████████████░░░   23.7 acc%/1K tok  │  73.7% acc  │  3,104 tokens
YAML           ██████████████░░░░░░   19.9 acc%/1K tok  │  74.5% acc  │  3,749 tokens
JSON           ████████████░░░░░░░░   16.4 acc%/1K tok  │  75.0% acc  │  4,587 tokens
XML            ██████████░░░░░░░░░░   13.8 acc%/1K tok  │  72.1% acc  │  5,221 tokens

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

Tip

TOON achieves 76.4% accuracy (vs JSON's 75.0%) while using 39.9% fewer tokens.

Note on CSV: Excluded from ranking as it only supports 109 of 209 questions (flat tabular data only). While CSV is highly token-efficient for simple tabular data, it cannot represent nested structures that other formats handle.

Per-Model Accuracy

Accuracy across 4 LLMs on 209 data retrieval questions:

claude-haiku-4-5-20251001
→ TOON           ████████████░░░░░░░░    59.8% (125/209)
  JSON           ███████████░░░░░░░░░    57.4% (120/209)
  YAML           ███████████░░░░░░░░░    56.0% (117/209)
  XML            ███████████░░░░░░░░░    55.5% (116/209)
  JSON compact   ███████████░░░░░░░░░    55.0% (115/209)
  CSV            ██████████░░░░░░░░░░    50.5% (55/109)

gemini-3-flash-preview
  XML            ████████████████████    98.1% (205/209)
  JSON           ███████████████████░    97.1% (203/209)
  YAML           ███████████████████░    97.1% (203/209)
→ TOON           ███████████████████░    96.7% (202/209)
  JSON compact   ███████████████████░    96.7% (202/209)
  CSV            ███████████████████░    96.3% (105/109)

gpt-5-nano
→ TOON           ██████████████████░░    90.9% (190/209)
  JSON compact   ██████████████████░░    90.9% (190/209)
  JSON           ██████████████████░░    89.0% (186/209)
  CSV            ██████████████████░░    89.0% (97/109)
  YAML           █████████████████░░░    87.1% (182/209)
  XML            ████████████████░░░░    80.9% (169/209)

grok-4-1-fast-non-reasoning
→ TOON           ████████████░░░░░░░░    58.4% (122/209)
  YAML           ████████████░░░░░░░░    57.9% (121/209)
  JSON           ███████████░░░░░░░░░    56.5% (118/209)
  XML            ███████████░░░░░░░░░    54.1% (113/209)
  JSON compact   ██████████░░░░░░░░░░    52.2% (109/209)
  CSV            ██████████░░░░░░░░░░    51.4% (56/109)

Tip

TOON achieves 76.4% accuracy (vs JSON's 75.0%) while using 39.9% fewer tokens on these datasets.

Performance by dataset, model, and question type

Performance by Question Type

Question Type TOON JSON YAML JSON compact XML CSV
Field Retrieval 99.6% 99.3% 98.5% 98.5% 98.9% 100.0%
Aggregation 61.9% 61.9% 59.9% 58.3% 54.4% 50.9%
Filtering 56.8% 53.1% 56.3% 55.2% 51.6% 50.9%
Structure Awareness 89.0% 87.0% 84.0% 84.0% 81.0% 85.9%
Structural Validation 70.0% 60.0% 60.0% 55.0% 85.0% 80.0%

Performance by Dataset

Uniform employee records
Format Accuracy Tokens Correct/Total
csv 73.2% 2,334 120/164
toon 73.2% 2,498 120/164
json-compact 73.8% 3,924 121/164
yaml 73.8% 4,959 121/164
json-pretty 73.8% 6,331 121/164
xml 74.4% 7,296 122/164
E-commerce orders with nested structures
Format Accuracy Tokens Correct/Total
toon 82.3% 7,458 135/164
json-compact 78.7% 7,110 129/164
yaml 79.9% 8,755 131/164
json-pretty 79.3% 11,234 130/164
xml 77.4% 12,649 127/164
Time-series analytics data
Format Accuracy Tokens Correct/Total
csv 75.0% 1,411 90/120
toon 78.3% 1,553 94/120
json-compact 74.2% 2,354 89/120
yaml 75.8% 2,954 91/120
json-pretty 75.0% 3,681 90/120
xml 72.5% 4,389 87/120
Top 100 GitHub repositories
Format Accuracy Tokens Correct/Total
csv 65.9% 8,527 87/132
toon 66.7% 8,779 88/132
yaml 65.2% 13,141 86/132
json-compact 59.8% 11,464 79/132
json-pretty 63.6% 15,157 84/132
xml 56.1% 17,105 74/132
Semi-uniform event logs
Format Accuracy Tokens Correct/Total
json-compact 68.3% 4,839 82/120
toon 65.0% 5,819 78/120
json-pretty 69.2% 6,817 83/120
yaml 61.7% 5,847 74/120
xml 58.3% 7,729 70/120
Deeply nested configuration
Format Accuracy Tokens Correct/Total
json-compact 90.5% 568 105/116
toon 94.8% 655 110/116
yaml 93.1% 675 108/116
json-pretty 92.2% 924 107/116
xml 91.4% 1,013 106/116
Valid complete dataset (control)
Format Accuracy Tokens Correct/Total
toon 100.0% 535 4/4
json-compact 100.0% 787 4/4
yaml 100.0% 992 4/4
json-pretty 100.0% 1,274 4/4
xml 25.0% 1,462 1/4
csv 0.0% 483 0/4
Array truncated: 3 rows removed from end
Format Accuracy Tokens Correct/Total
csv 100.0% 413 4/4
xml 100.0% 1,243 4/4
toon 0.0% 462 0/4
json-pretty 0.0% 1,085 0/4
yaml 0.0% 843 0/4
json-compact 0.0% 670 0/4
Extra rows added beyond declared length
Format Accuracy Tokens Correct/Total
csv 100.0% 550 4/4
toon 75.0% 605 3/4
json-compact 75.0% 901 3/4
xml 100.0% 1,678 4/4
yaml 75.0% 1,138 3/4
json-pretty 50.0% 1,460 2/4
Inconsistent field count (missing salary in row 10)
Format Accuracy Tokens Correct/Total
csv 100.0% 480 4/4
json-compact 100.0% 782 4/4
yaml 100.0% 985 4/4
toon 100.0% 1,008 4/4
json-pretty 100.0% 1,266 4/4
xml 100.0% 1,453 4/4
Missing required fields (no email in multiple rows)
Format Accuracy Tokens Correct/Total
csv 100.0% 340 4/4
xml 100.0% 1,409 4/4
toon 75.0% 974 3/4
json-pretty 50.0% 1,225 2/4
yaml 25.0% 951 1/4
json-compact 0.0% 750 0/4

Performance by Model

claude-haiku-4-5-20251001
Format Accuracy Correct/Total
toon 59.8% 125/209
json-pretty 57.4% 120/209
yaml 56.0% 117/209
xml 55.5% 116/209
json-compact 55.0% 115/209
csv 50.5% 55/109
gemini-3-flash-preview
Format Accuracy Correct/Total
xml 98.1% 205/209
json-pretty 97.1% 203/209
yaml 97.1% 203/209
toon 96.7% 202/209
json-compact 96.7% 202/209
csv 96.3% 105/109
gpt-5-nano
Format Accuracy Correct/Total
toon 90.9% 190/209
json-compact 90.9% 190/209
json-pretty 89.0% 186/209
csv 89.0% 97/109
yaml 87.1% 182/209
xml 80.9% 169/209
grok-4-1-fast-non-reasoning
Format Accuracy Correct/Total
toon 58.4% 122/209
yaml 57.9% 121/209
json-pretty 56.5% 118/209
xml 54.1% 113/209
json-compact 52.2% 109/209
csv 51.4% 56/109

What's Being Measured

This benchmark tests LLM comprehension and data retrieval accuracy across different input formats. Each LLM receives formatted data and must answer questions about it. This does not test the model's ability to generate TOON output – only to read and understand it.

Datasets Tested

Eleven datasets designed to test different structural patterns and validation capabilities:

Primary datasets:

  1. Tabular (100 employee records): Uniform objects with identical fields – optimal for TOON's tabular format.
  2. Nested (50 e-commerce orders): Complex structures with nested customer objects and item arrays.
  3. Analytics (60 days of metrics): Time-series data with dates and numeric values.
  4. GitHub (100 repositories): Real-world data from top GitHub repos by stars.
  5. Event Logs (75 logs): Semi-uniform data with ~50% flat logs and ~50% with nested error objects.
  6. Nested Config (1 configuration): Deeply nested configuration with minimal tabular eligibility.

Structural validation datasets:

  1. Control: Valid complete dataset (baseline for validation)
  2. Truncated: Array with 3 rows removed from end (tests [N] length detection)
  3. Extra rows: Array with 3 additional rows beyond declared length
  4. Width mismatch: Inconsistent field count (missing salary in row 10)
  5. Missing fields: Systematic field omissions (no email in multiple rows)

Question Types

209 questions are generated dynamically across five categories:

  • Field retrieval (33%): Direct value lookups or values that can be read straight off a record (including booleans and simple counts such as array lengths)

    • Example: "What is Alice's salary?" → 75000
    • Example: "How many items are in order ORD-0042?" → 3
    • Example: "What is the customer name for order ORD-0042?" → John Doe

  • Aggregation (30%): Dataset-level totals and averages plus single-condition filters (counts, sums, min/max comparisons)

    • Example: "How many employees work in Engineering?" → 17
    • Example: "What is the total revenue across all orders?" → 45123.50
    • Example: "How many employees have salary > 80000?" → 23

  • Filtering (23%): Multi-condition queries requiring compound logic (AND constraints across fields)

    • Example: "How many employees in Sales have salary > 80000?" → 5
    • Example: "How many active employees have more than 10 years of experience?" → 8

  • Structure awareness (12%): Tests format-native structural affordances (TOON's [N] count and {fields}, CSV's header row)

    • Example: "How many employees are in the dataset?" → 100
    • Example: "List the field names for employees" → id, name, email, department, salary, yearsExperience, active
    • Example: "What is the department of the last employee?" → Sales

  • Structural validation (2%): Tests ability to detect incomplete, truncated, or corrupted data using structural metadata

    • Example: "Is this data complete and valid?" → YES (control dataset) or NO (corrupted datasets)
    • Tests TOON's [N] length validation and {fields} consistency checking
    • Demonstrates CSV's lack of structural validation capabilities

Evaluation Process

  1. Format conversion: Each dataset is converted to all 6 formats (TOON, JSON, YAML, JSON compact, XML, CSV).
  2. Query LLM: Each model receives formatted data + question in a prompt and extracts the answer.
  3. Validate deterministically: Answers are validated using type-aware comparison (e.g., 50000 = $50,000, Engineering = engineering, 2025-01-01 = January 1, 2025) without requiring an LLM judge.

Models & Configuration

  • Models tested: claude-haiku-4-5-20251001, gemini-3-flash-preview, gpt-5-nano, grok-4-1-fast-non-reasoning
  • Token counting: Using gpt-tokenizer with o200k_base encoding (GPT-5 tokenizer)
  • Temperature: Not set (models use their defaults)
  • Total evaluations: 209 questions × 6 formats × 4 models = 5,016 LLM calls

Token Efficiency

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.

Mixed-Structure Track

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                █████████████░░░░░░░    73,126 tokens
   ├─ vs JSON          (−33.3%)               109,599 tokens
   ├─ vs JSON compact  (+5.3%)                 69,459 tokens
   ├─ vs YAML          (−14.4%)                85,415 tokens
   └─ vs XML           (−40.7%)               123,344 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                ██████████████░░░░░░       620 tokens
   ├─ vs JSON          (−31.9%)                   911 tokens
   ├─ vs JSON compact  (+11.1%)                   558 tokens
   ├─ vs YAML          (−6.3%)                    662 tokens
   └─ vs XML           (−38.2%)                 1,003 tokens

──────────────────────────────────── Total ────────────────────────────────────
   TOON                ████████████████░░░░   227,830 tokens
   ├─ vs JSON          (−21.9%)               291,711 tokens
   ├─ vs JSON compact  (+14.7%)               198,546 tokens
   ├─ vs YAML          (−5.7%)                241,474 tokens
   └─ vs XML           (−31.0%)               330,206 tokens

Flat-Only Track

Datasets with flat tabular structures where CSV is applicable.

👥 Uniform employee records  ┊  Tabular: 100%
   │
   CSV                 ███████████████████░    47,102 tokens
   TOON                ████████████████████    49,919 tokens   (+6.0% vs CSV)
   ├─ vs JSON          (−60.7%)               127,063 tokens
   ├─ vs JSON compact  (−36.9%)                79,059 tokens
   ├─ vs YAML          (−50.1%)               100,011 tokens
   └─ vs XML           (−65.9%)               146,579 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                 ███████████████████░     8,512 tokens
   TOON                ████████████████████     8,744 tokens   (+2.7% vs CSV)
   ├─ vs JSON          (−42.3%)                15,144 tokens
   ├─ vs JSON compact  (−23.7%)                11,454 tokens
   ├─ vs YAML          (−33.4%)                13,128 tokens
   └─ vs XML           (−48.9%)                17,095 tokens

──────────────────────────────────── Total ────────────────────────────────────
   CSV                 ███████████████████░    63,997 tokens
   TOON                ████████████████████    67,778 tokens   (+5.9% vs CSV)
   ├─ vs JSON          (−58.8%)               164,452 tokens
   ├─ vs JSON compact  (−35.3%)               104,724 tokens
   ├─ vs YAML          (−48.3%)               130,997 tokens
   └─ vs XML           (−64.4%)               190,290 tokens
Show detailed examples

📈 Time-series analytics data

Savings: 13,130 tokens (59.0% reduction vs JSON)

JSON (22,245 tokens):

{
  "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
    }
  ]
}

TOON (9,115 tokens):

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

⭐ Top 100 GitHub repositories

Savings: 6,400 tokens (42.3% reduction vs JSON)

JSON (15,144 tokens):

{
  "repositories": [
    {
      "id": 28457823,
      "name": "freeCodeCamp",
      "repo": "freeCodeCamp/freeCodeCamp",
      "description": "freeCodeCamp.org's open-source codebase and curriculum. Learn math, programming,…",
      "createdAt": "2014-12-24T17:49:19Z",
      "updatedAt": "2025-10-28T11:58:08Z",
      "pushedAt": "2025-10-28T10:17:16Z",
      "stars": 430886,
      "watchers": 8583,
      "forks": 42146,
      "defaultBranch": "main"
    },
    {
      "id": 132750724,
      "name": "build-your-own-x",
      "repo": "codecrafters-io/build-your-own-x",
      "description": "Master programming by recreating your favorite technologies from scratch.",
      "createdAt": "2018-05-09T12:03:18Z",
      "updatedAt": "2025-10-28T12:37:11Z",
      "pushedAt": "2025-10-10T18:45:01Z",
      "stars": 430877,
      "watchers": 6332,
      "forks": 40453,
      "defaultBranch": "master"
    },
    {
      "id": 21737465,
      "name": "awesome",
      "repo": "sindresorhus/awesome",
      "description": "😎 Awesome lists about all kinds of interesting topics",
      "createdAt": "2014-07-11T13:42:37Z",
      "updatedAt": "2025-10-28T12:40:21Z",
      "pushedAt": "2025-10-27T17:57:31Z",
      "stars": 410052,
      "watchers": 8017,
      "forks": 32029,
      "defaultBranch": "main"
    }
  ]
}

TOON (8,744 tokens):

repositories[3]{id,name,repo,description,createdAt,updatedAt,pushedAt,stars,watchers,forks,defaultBranch}:
  28457823,freeCodeCamp,freeCodeCamp/freeCodeCamp,"freeCodeCamp.org's open-source codebase and curriculum. Learn math, programming,…","2014-12-24T17:49:19Z","2025-10-28T11:58:08Z","2025-10-28T10:17:16Z",430886,8583,42146,main
  132750724,build-your-own-x,codecrafters-io/build-your-own-x,Master programming by recreating your favorite technologies from scratch.,"2018-05-09T12:03:18Z","2025-10-28T12:37:11Z","2025-10-10T18:45:01Z",430877,6332,40453,master
  21737465,awesome,sindresorhus/awesome,😎 Awesome lists about all kinds of interesting topics,"2014-07-11T13:42:37Z","2025-10-28T12:40:21Z","2025-10-27T17:57:31Z",410052,8017,32029,main

Related Resources

Reference in New Issue View Git Blame Copy Permalink