Intelligence per dollar is the new scaling law: A tiny reasoning model breaks the existing cost-accuracy Pareto frontier on Arc-AGI 1

Reddit r/artificial 08/17/26, 03:25 PM Models

ai-model reasoning benchmarking arc-agi cost-efficiency scaling-law latent-reasoning

Summary

Chart Pathway's BDH-CQ, a 150M parameter reasoning model, achieves 29.5% on ARC-AGI-1 at a much lower cost per task compared to larger models like GPT-5.6 Luna, showcasing improved cost-accuracy trade-offs.

Chart Pathway, an AI lab building a post-transformer architecture and models, 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. It 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. They also report early pretraining experiments from 1B to 600B parameters, while preserving the latent reasoning capabilities specific to BDH-CQ. It does it by combining in-context learning with recurrent latent reasoning instead of verbalizing every intermediate result.

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