Pathway BDH-CQ Model Sets AI Cost-Efficiency Record
Pathway, an AI lab building 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.
BDH-CQ runs approximately 11 times as cheaply per task as GPT 5.6 Luna (Low), even after OpenAI’s 80% price cut on July 30. Luna scores 34.2% against BDH-CQ’s 29.5%, representing a modest accuracy gain at approximately 11 times the cost.
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.
The cost gap comes from a structural difference. Many Transformer-based reasoning systems externalize their work as a chain-of-thought, generating extra tokens sequentially and feeding them back into later steps. As the trace grows, so do inference...
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