Independent model buying adviceClear verdicts. Visible limits.
Most rankings ask which AI model is smartest. We ask a more useful question: what is the least expensive model that reliably clears your job—and exactly where does the upgrade start earning its keep?
Editorial status: the method is public; the first field tests are in protocol development. We publish no model verdict before there is evidence beneath it.
The lower-cost model clears the job’s real acceptance bar.
The premium materially reduces failure, review, or rework.
You are buying capability this workload does not use.
The buying problem
A model can be overkill for extraction, enough for daily writing, and worth its premium for a long autonomous job. One universal score hides the decision you actually have to make.
Our reviews start with a concrete job and a visible success bar. We test the cheapest serious contender first, move up only when the work exposes a limit, and count the human effort left behind—not just token price.
See the escalation logicFive real-work desks
Small taxonomy. Increasing difficulty. A stopping point you can explain.
From isolated fixes to ambiguous, multi-file repository work.
02From scoped synthesis to contradictory, source-heavy questions.
03Structure, voice, long context, and the cost of editorial cleanup.
04Extraction, cross-page reasoning, validation, and silent errors.
05Tool use, recovery, supervision, and how safely you can step away.
From the review desk
Our opening series starts with real-work ladders, not a 400-model table. Every test must name the job, the bar, the exact model, the environment, and what the human still had to fix.
Read the testing standardInitial field-test queue
A queue is not a conclusion. Model names and verdicts appear only after a reproducible record exists.
The editorial contract
A finished repository change, source-backed brief, or validated document—not a cherry-picked prompt.
Cheap inference can be expensive work once corrections, verification, retries, and cleanup are included.
We separate obvious failure from plausible output that leaves a hidden problem behind.
Every conclusion is dated and tied to an exact model and setup. Material changes trigger a retest state.
New external-research desk
Current identities, prices, integration limits, and upgrade questions—with zero invented firsthand results.
Start cheap, diagnose the failure, and make an upgrade earn its premium.
Read the guide →ComparisonThe 10× token-rate difference and the evidence needed to justify it.
Compare →Cost analysisReproducible token baskets, cache cases, long-context rules, and exclusions.
See the arithmetic →ProtocolFive task types, three configurations, and visible acceptance rules.
Inspect the protocol →Start with the receipts
Read the externally researched repository-coding guide now, then inspect the test standard behind future firsthand verdicts.