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Yield (Υ) vs aggregate token activity — efficiency vs burn
| Feature | SigRank | TokenRank |
|---|---|---|
| What it measures | Token-cascade efficiency (Yield, Leverage, SNR, Velocity) | Aggregate token activity (burn-to-rank) |
| Headline metric | Yield (Υ) = (cache_read × output) / input² | Total tokens burned |
| What it tells you | How efficiently you use AI — are tokens compounding? | How much you spent — volume only |
| Leaderboard | Yes — ranked by Yield, class tiers, weekly drops | Yes — ranked by aggregate burn |
| Class tiers | Yes — 8-tier experience ladder | No |
| Build archetypes | Yes — 10 build archetypes | No |
| API | Public REST API + MCP tools | Limited |
| Privacy | Token counts only — never prompt content | Token counts only |
TokenRank uses a burn-to-rank model with similar privacy positioning to SigRank — token counts only, no prompt content. If you want a volume-based leaderboard that respects privacy, it's a reasonable choice.
But aggregate token burn doesn't tell you if you're good at using AI. An operator who burns 10M tokens to produce 1K output has a high burn — but low efficiency. An operator who uses 100K tokens to produce the same 1K output has a lower burn but 10x higher Yield. Burn rewards consumption. Efficiency rewards skill.
SigRank's Yield (Υ) metric measures the architecture of your token cascade — is signal compounding through cache reuse and tight output, or are tokens burned in long input chains? TokenRank tells you how much you burned. SigRank tells you whether those tokens were well spent.
The key difference: TokenRank ranks by burn. SigRank ranks by efficiency. Both respect privacy. Only one measures skill.
TokenRank uses burn-to-rank — aggregate token activity. SigRank ranks by Yield — token-cascade efficiency. Burn tells a developer how much they consumed; Yield tells them whether tokens compounded. Both use token counts only, but one rewards volume and the other rewards skill.
No — both respect privacy but rank differently. TokenRank ranks by aggregate burn; SigRank ranks by efficiency. An AI user who burns 10M tokens for 1K output tops TokenRank but flops on SigRank. They're complementary views for a coder using AI.
Yield (Υ) = (cache_read × output) / input² — token-cascade efficiency from real sessions. Works across Claude, GPT, Gemini, Cursor, Copilot, and any AI coding tool.
Visit signalaf.com/score to enroll and submit your token telemetry. SigRank will compute your Yield, your rank, and your operator class. Token counts only — never prompt content, never code.
Stop burning. Start measuring.
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