Pathway's 150M-Parameter Model Breaks the ARC-AGI-1 Cost-Efficiency Frontier
BDH-CQ scores 29.5% on the public benchmark at a computed $0.0007 per task, less than one tenth of a cent, opening a whole new territory in terms of intelligence per dollar.
Pathway, an AI lab building a Post-Transformer architecture and models, today published benchmark results for BDH-CQ, a 150-million-parameter reasoning model. BDH-CQ scored 29.5% pass@2 on the public ARC-AGI-1 evaluation set at a computed inference cost of $0.0007 per task. Thanks to a new approach to reasoning, BDH-CQ runs approximately 11 times as cheaply per task as GPT 5.6 Luna (Low), even after accounting for OpenAI’s 80% price cut of 5.6 Luna on July 30th. Luna scores 34.2% against BDH-CQ's 29.5%, a modest accuracy gain at 11 times the cost.
This press release features multimedia. View the full release here: https://www.businesswire.com/news/home/20260811268264/en/ ARC-AGI-1 score versus estimated cost per task alongside official ARC Prize leaderboard results as of July 31, 2026. The reported score surpasses the previously reported cost-accuracy Pareto frontier, setting a new benchmark for ARC-AGI-1 cost efficiency.
ARC-AGI-1 is a public reasoning benchmark that tests whether a system can infer an underlying rule from a small number of examples and apply it to a new input, a capability often associated with human-like intelligence.
