We tested every major AI reasoning system. There is no clear winner.
- Source
- arcprize.org
- Author
- Mike Knoop
- Date
Context
The highest score need not be the best result at a given budget. ARC Prize's comparison of reasoning systems plots accuracy against cost because additional reasoning and repeated attempts can improve answers while making them more expensive. Different systems occupied useful positions along that tradeoff in the reported tests.
The also encountered timeouts and differences in how providers respected reasoning budgets. Those practical constraints affect whether extra reasoning fits an application at all. This is a historical comparison on ARC tasks, not a current buying guide or a universal ranking: its durable point is to compare the complete execution setup, cost and completion behavior alongside the score.
- benchmark — A standard public test set for comparing AI models — the shared scoreboards behind every "model X beats model Y" claim.
- eval — A repeatable test for AI quality — a set of tasks plus scoring — used the way software teams use test suites, because model output is too variable to judge by eyeballing.
- inference — Running a trained model to get answers — the phase where AI is actually used, as opposed to trained.
- test-time compute — Spending more computation when the model answers — thinking longer, trying multiple attempts — to buy accuracy without training a bigger model.
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