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docs: switch benchmark order
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@@ -36,11 +36,11 @@ Benchmarks test LLM comprehension across different input formats using 209 data
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Each format's overall performance, balancing accuracy against token cost:
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```
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TOON ▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓ 26.9 │ 73.9% acc │ 2,744 tokens
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JSON compact ▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓░░░ 22.9 │ 70.7% acc │ 3,081 tokens
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YAML ▓▓▓▓▓▓▓▓▓▓▓▓▓▓░░░░░░ 18.6 │ 69.0% acc │ 3,719 tokens
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JSON ▓▓▓▓▓▓▓▓▓▓▓░░░░░░░░░ 15.3 │ 69.7% acc │ 4,545 tokens
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XML ▓▓▓▓▓▓▓▓▓▓░░░░░░░░░░ 13.0 │ 67.1% acc │ 5,167 tokens
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TOON ████████████████████ 26.9 │ 73.9% acc │ 2,744 tokens
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JSON compact █████████████████░░░ 22.9 │ 70.7% acc │ 3,081 tokens
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YAML ██████████████░░░░░░ 18.6 │ 69.0% acc │ 3,719 tokens
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JSON ███████████░░░░░░░░░ 15.3 │ 69.7% acc │ 4,545 tokens
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XML ██████████░░░░░░░░░░ 13.0 │ 67.1% acc │ 5,167 tokens
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```
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TOON achieves **73.9%** accuracy (vs JSON's 69.7%) while using **39.6% fewer tokens**.
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@@ -559,7 +559,7 @@ function generateHorizontalEfficiencyChart(
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return ranking
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.map((r) => {
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const normalizedValue = r.efficiency / maxEfficiency
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const bar = createProgressBar(normalizedValue, 1, barWidth, { filled: '▓', empty: '░' })
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const bar = createProgressBar(normalizedValue, 1, barWidth)
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const displayName = FORMATTER_DISPLAY_NAMES[r.format] || r.format
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const formatName = displayName.padEnd(maxFormatWidth)
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const efficiency = r.efficiency.toFixed(1).padStart(4)
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