Burning to Rank vs Building to Rank

The short version: TokenRank

TokenRank aggregates token activity across Codex, Claude, Gemini, Qwen, Cursor, and Copilot, then ranks operators by total volume burned. It shares SigRank's privacy stance: token counts only, no code, no prompts. But it ranks by the wrong axis. Burn-to-rank is a volume game. An operator who re-sends the same context every turn and burns 50M tokens will outrank one who compounds cached context and burns 5M. Higher burn, lower skill, higher rank.

SigRank takes the same four token pillars and asks a different question: is the cascade compounding or burning? 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. TokenRank counts the fuel burned; SigRank measures the lap time. Both matter. Only one tells you who is building, not just burning.

Feature comparison

Why burn-to-rank isn't enough

TokenRank answers "who burned the most tokens?" That is a consumption contest, not a skill ranking. Two operators can burn the same 50M tokens and get wildly different outcomes. One reuses cached context efficiently and produces 30K output tokens per session; the other re-sends the same context every turn and produces 3K. Same burn, ten-fold difference in signal. On a burn-to-rank 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. TokenRank sums the burn; SigRank tells you whether the cascade it describes is compounding or burning.

From burn-to-rank to Yield

If you already run TokenRank, you have the logs. SigRank reads the same telemetry and adds the efficiency layer burn-to-rank 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 TokenRank alternative?

They overlap on data and on privacy positioning, but diverge on the ranking axis. TokenRank uses burn-to-rank: it aggregates token activity across Codex, Claude, Gemini, Qwen, Cursor, and Copilot, then ranks by total volume. SigRank takes the same token telemetry and ranks by Yield efficiency: Υ = cache_read × output / input². If you want a volume leaderboard with wide tool coverage, TokenRank works. If you want to know who is actually efficient, SigRank is the answer. You can run both, they read the same logs.

What does TokenRank not measure that SigRank does?

TokenRank reports aggregate token activity and ranks by that total. 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). TokenRank rewards the operator who burns the most; SigRank rewards the one who compounds the most. Burning to rank is a volume game; building to rank is a skill game.

Can I use both TokenRank and SigRank?

Yes, and they share a similar privacy stance: both publish token counts only, no code, no prompts, no secrets. TokenRank gives you the volume view across six major platforms. SigRank gives you the efficiency layer that volume rankings cannot. Run `sigrank submit` to publish your cascade score to the SigRank leaderboard, and keep TokenRank for the burn-rate crowd. The same local logs feed both.

Which is better for comparing AI operators across platforms?

SigRank, for one reason: efficiency is platform-neutral. TokenRank ranks by volume, which favors operators on cheaper or higher-throughput models regardless of skill. SigRank ranks by Υ Yield, a ratio computed from token pillars, so it is comparable across operators regardless of which tool they drove. An operator compounding cached context on Claude and one doing the same on Cursor get comparable Yield scores. Volume is not comparable across platforms; Yield is.

TokenRank and SigRank both emphasize privacy. What is the difference?

Both publish token counts only and strip code, prompts, and secrets. The difference is what they do with those counts. TokenRank sums them and ranks by total. SigRank computes a signed, cascade-scored, class-tiered snapshot from them and ranks by Yield. Privacy is the input discipline; efficiency is the output signal. TokenRank protects your data and counts it; SigRank protects your data and grades it.