How to Compare AI Coding Subscriptions: Work per Dollar

Coding subscriptions should be compared by accepted, tested work relative to total spending rather than by token allowances, credits, request counts, or illustrative marketing multipliers such as Weave’s unverified 2x scenarios. A fair evaluation defines identical tasks, repository states, tools, instructions, and time budgets, counting only changes that pass specified tests and review while separately tracking failures, retries, human correction, review time, and reversions. Evaluators should document plan prices, usage limits, environment details, model-routing behavior, and blocked time because provider terms and performance can change. Key measures include output multiplier, cost per accepted task, and work-per-dollar multiplier, with hypothetical examples showing that increased output does not necessarily improve value when total costs rise. Repeated, alternated trials across representative easy and difficult tasks can reduce run-to-run bias, while public evidence should preserve reproducibility without exposing sensitive information. API benchmarks should remain distinct from subscription evaluations because they do not establish consumer-plan capacity or value, and a practical first step is to test one representative week using a fixed task list and a single configuration change.

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