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Yield (Υ) vs dollars spent — efficiency vs cost
| Feature | SigRank | mytokentracker |
|---|---|---|
| What it measures | Token-cascade efficiency (Yield, Leverage, SNR, Velocity) | Dollars spent across 2,300+ model pricings |
| Headline metric | Yield (Υ) = (cache_read × output) / input² | Total dollars spent |
| 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 dollars spent |
| 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 |
mytokentracker ranks #1 for "AI usage leaderboard" and covers 2,300+ model pricings. If you want to know exactly how many dollars you've spent across every model, it's thorough and well-priced for cost tracking.
But dollars spent doesn't tell you if you're good at using AI. An operator who spends $500 to produce 1K output has a high spend — but low efficiency. An operator who spends $5 to produce the same 1K output has a lower spend but 100x higher Yield. Spend 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? mytokentracker tells you how much you spent. SigRank tells you whether those dollars were well spent.
The key difference: mytokentracker ranks by spend. SigRank ranks by efficiency. One measures cost. The other measures skill.
mytokentracker ranks by dollars spent across 2,300+ model pricings. SigRank ranks by Yield — token-cascade efficiency. Spend tells a developer how much they paid; Yield tells them how skillfully they used AI. An AI user who spends $500 for 1K output ranks high on spend but low on Yield.
No — they answer different questions. mytokentracker is thorough cost tracking across every model. SigRank measures efficiency and ranks AI operators by skill. A person using AI can use mytokentracker for budget and SigRank for performance.
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 tracking spend. Start measuring skill.
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