Model Benchmark vs Operator Benchmark

The short version: SWE-bench

SWE-bench is the gold standard for one question: which AI model can solve real software engineering tasks? It takes GitHub issues from popular Python repos, asks the model to produce a patch, and checks whether the patch passes the existing test suite. That is a model-capability benchmark, and SWE-bench does it well.

SigRank solves a different problem: how efficiently did the operator drive the model? Two operators can both pass the same SWE-bench task — one with 500 tokens and high cache reuse, the other with 50,000 tokens and zero cache reuse. SWE-bench scores them identically. SigRank scores them differently, because the driving was different. The benchmark that was missing was the one that scores the human in the human-AI loop.

Feature comparison

Outcome vs process

SWE-bench measures outcome: did the patch pass the tests? That is a binary signal — correct or not. It says nothing about the process. An operator who solves a SWE-bench task with 500 tokens of fresh input and 90% cache reuse is scored identically to one who brute-forces it with 50,000 tokens and zero cache reuse. Both pass; both get 1.0. The efficiency gap is invisible.

SigRank measures process: the cascade metric Υ = cache_read × output / input² rewards operators who reuse context, compress input, and convert tokens into output efficiently. The first operator scores Υ ≈ 18,000+; the second scores Υ ≈ 200. Same outcome, very different driving. Both signals matter — SWE-bench tells you the model can do it; SigRank tells you how efficiently the operator made it happen.

Complementary, not competing

SWE-bench and SigRank sit at different layers of the evaluation stack. SWE-bench evaluates the model's capability ceiling — can it resolve real issues? SigRank evaluates the operator's driving efficiency — how well did the human extract value from the model? A team that uses SWE-bench to pick the right model and SigRank to measure operator efficiency gets both signals: capable model + efficient operators.

SigRank's scoring is grounded in published science — the Conservation Law of Commitment (DOI: 10.5281/zenodo.20029607) — with a governance framework (MO§ES™, patent pending) enforcing submission integrity.

Frequently asked questions

What is the difference between SWE-bench and SigRank?

SWE-bench evaluates AI MODELS on real software engineering tasks — given a GitHub issue, can the model produce a patch that passes the test suite? SigRank evaluates OPERATORS — the humans driving the AI — by measuring token cascade efficiency (Υ = cache_read × output / input²) from on-device, signed telemetry. SWE-bench answers "can the model fix the bug?"; SigRank answers "how efficiently did the operator drive the model to fix the bug?" They measure different layers of the human-AI stack.

Is SigRank an alternative to SWE-bench?

They are complementary, not replacements. SWE-bench is the gold standard for model-level coding capability — it tells you which model can resolve real issues. SigRank is the standard for operator-level efficiency — it tells you how well the human used the model. You pick the model with SWE-bench; you measure your driving skill with SigRank. If you want a benchmark that ranks the human side of AI-assisted coding, SigRank is the one that does that.

Why rank operators instead of models?

Because the model is a constant across operators, but the outcome is not. Give ten operators the same Claude model and the same SWE-bench task and you get ten different token cascades — different input sizes, different cache reuse, different output. The model didn't change; the driving did. SWE-bench controls for the operator to isolate the model. SigRank controls for the model to isolate the operator. Both are valid; only SigRank answers "how well did I drive?"

Does SWE-bench's pass/fail signal make it more rigorous than SigRank?

SWE-bench's pass/fail is rigorous for its question — did the patch work? But it says nothing about the process. Two operators can both pass the same SWE-bench task, one with 500 tokens and high cache reuse, the other with 50,000 tokens and zero cache reuse. SWE-bench scores them identically. SigRank scores them differently — because the driving was different. Process efficiency is a separate axis from outcome correctness, and both matter.

Can SigRank work alongside SWE-bench?

Yes. SWE-bench tells you which model to use for software engineering tasks. SigRank tells you how efficiently you drove that model while solving them. An operator who passes SWE-bench tasks with high Υ Yield is demonstrably more efficient than one who passes with low Υ Yield. Together they answer both "did it work?" and "how efficiently did you make it work?"