Dollars Spent vs Dollars Worth It

The short version: mytokentracker

mytokentracker aggregates token usage across 2,300+ models, applies per-model pricing, and ranks operators by total dollars spent. It does the cost-accounting layer well: wide model coverage, accurate pricing, clean spend reports. But it ranks by the wrong axis. Spend is a measure of consumption, not skill. An operator who spends $500 re-sending the same context will outrank one who spends $50 compounding cached context into high-yield output. Higher spend, lower skill, higher rank.

SigRank takes the same four token pillars and asks a different question: did that spend produce signal or noise? The headline metric, Υ Yield = cache_read × output / input², rewards the operator who reuses cached context efficiently and penalizes the one who burns fresh input without leverage. mytokentracker prints the receipt; SigRank grades the result. Both matter. Only one tells you whether the money was well spent.

Feature comparison

Why dollars spent aren't enough

mytokentracker answers "how much did I spend?" That is a budget question, not a skill question. Two operators can spend the same $100 and get wildly different outcomes. One reuses cached context efficiently and produces 30K output tokens; the other re-sends the same context every turn and produces 3K. Same spend, ten-fold difference in signal. On a spend leaderboard, they tie. On a Yield leaderboard, the gap is obvious.

SigRank's headline metric, Υ = cache_read × output / input², measures exactly that gap. It rewards the operator who compounds cached context into output and penalizes the one who burns fresh input without leverage. mytokentracker multiplies tokens by price; SigRank tells you whether the cascade they describe is compounding or burning.

From spend to Yield

If you already run mytokentracker, you have the logs. SigRank reads the same telemetry and adds the efficiency layer spend rankings never had:

npm install -g sigrank
sigrank enroll      # create your operator identity
sigrank submit      # reads logs, scores, signs, publishes

Prefer to inspect before you submit? Run sigrank me --dry-run to see your scored payload locally, or paste your token counts into the /score calculator to compute your Υ Yield, class tier, and compression ratio instantly, no account, no submission, just the numbers.

Frequently asked questions

Is SigRank a mytokentracker alternative?

They overlap on data but diverge on the ranking axis. mytokentracker aggregates token usage across 2,300+ models and ranks operators by dollars spent. SigRank takes the same token telemetry and ranks by Yield efficiency: Υ = cache_read × output / input². If you want a spend report, mytokentracker is excellent. If you want to know whether that spend was worth it, SigRank is the answer. You can run both, they read the same logs.

What does mytokentracker not measure that SigRank does?

mytokentracker reports dollars spent and ranks by that single number. SigRank derives the cascade architecture from the same four pillars: Υ Yield (is signal compounding or burning?), compression ratio (output per input), SNR (signal density), Leverage (how much cached context amplifies your input), and Velocity (tokens per unit time). mytokentracker tells you what you paid; SigRank tells you what you got for it. Spend is a receipt; Yield is a result.

Can I use both mytokentracker and SigRank?

Yes, and they complement each other. mytokentracker gives you the cost layer: per-model pricing, spend totals, budget tracking across 2,300+ models. SigRank gives you the efficiency layer that spend rankings cannot. Run `sigrank submit` to publish your cascade score to the SigRank leaderboard, and keep mytokentracker for the budget view. The same local logs feed both.

Which is better for optimizing AI coding spend?

Both, for different reasons. mytokentracker tells you how much you spent and on which model. SigRank tells you whether that spend produced signal or noise. An operator who spends $200 efficiently (high Yield, high compression) is getting more value than one who spends $50 inefficiently (low Yield, re-sent context). Use mytokentracker to track the budget; use SigRank to track whether the budget is being spent well.

mytokentracker has 2,300+ model prices. Does SigRank track cost?

SigRank is cost-aware but cost is not the ranking axis. The cascade metrics (Υ Yield, Leverage, compression ratio) are computed from token pillars, not dollar amounts, so they are comparable across operators regardless of which model they drove or what they paid. Spend varies by provider and pricing tier; efficiency is a property of the operator. SigRank ranks the driver, not the gas bill.