TOON Benchmarks

Performance and efficiency comparisons against other popular data formats.

Efficiency Ranking

Each format's overall performance, balancing accuracy against token cost (accuracy per 1K tokens).

TOON████████████████████26.9│73.9% acc│2,744 tokens
JSON compact█████████████████░░░22.9│70.7% acc│3,081 tokens
YAML██████████████░░░░░░18.6│69.0% acc│3,719 tokens
JSON███████████░░░░░░░░░15.3│69.7% acc│4,545 tokens
XML██████████░░░░░░░░░░13.0│67.1% acc│5,167 tokens

TOON achieves 73.9% accuracy (vs JSON's 69.7%) while using 39.6% fewer tokens.

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-2.5-flash

→ TOON██████████████████░░87.6%(183/209)
CSV█████████████████░░░86.2%(94/109)
JSON compact████████████████░░░░82.3%(172/209)
YAML████████████████░░░░79.4%(166/209)
XML████████████████░░░░79.4%(166/209)
JSON███████████████░░░░░77.0%(161/209)

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-fast-non-reasoning

→ TOON███████████░░░░░░░░░57.4%(120/209)
JSON███████████░░░░░░░░░55.5%(116/209)
JSON compact███████████░░░░░░░░░54.5%(114/209)
YAML███████████░░░░░░░░░53.6%(112/209)
XML███████████░░░░░░░░░52.6%(110/209)
CSV██████████░░░░░░░░░░52.3%(57/109)

Token Efficiency

Token counts are measured using the GPT-5 o200k_base tokenizer. Savings are calculated against formatted JSON (2-space indentation) as the primary baseline. Actual savings vary by model and tokenizer.

Mixed-Structure Track

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

🛒 E-commerce orders with nested structures

Tabular: 33%

TOON

72,771 tokens

vs JSON (-33.1%)

vs JSON compact (+5.5%)

vs YAML (-14.2%)

vs XML (-40.5%)

JSON

108,806 tokens

JSON compact

68,975 tokens

YAML

84,780 tokens

XML

122,406 tokens

🧾 Semi-uniform event logs

Tabular: 50%

TOON

153,211 tokens

vs JSON (-15.0%)

vs JSON compact (+19.9%)

vs YAML (-0.8%)

vs XML (-25.2%)

JSON

180,176 tokens

JSON compact

127,731 tokens

YAML

154,505 tokens

XML

204,777 tokens

🧩 Deeply nested configuration

Tabular: 0%

TOON

631 tokens

vs JSON (-31.3%)

vs JSON compact (+11.9%)

vs YAML (-6.2%)

vs XML (-37.4%)

JSON

919 tokens

JSON compact

564 tokens

YAML

673 tokens

XML

1,008 tokens

Flat-Only Track

Datasets with flat tabular structures where CSV is a strong contender.

👥 Uniform employee records

Tabular: 100%

CSV

46,954 tokens

TOON

49,831 tokens

vs JSON (-60.7%)

vs JSON compact (-36.8%)

vs YAML (-50.0%)

vs XML (-66.0%)

JSON

126,860 tokens

JSON compact

78,856 tokens

YAML

99,706 tokens

XML

146,444 tokens