New kind of AI uses a fresh approach to reasoning —‬ researchers say it costs up to 11 times less to run than a leading OpenAI model

A new artificial intelligence (AI) model used a novel approach to AI cognition to dramatically reduce the cost of requests, suggesting that nonverbal reasoning may be the next step toward machines developing human-like intelligence.

In a new research paper published Aug.

10 on the preprint server arXiv , scientists at AI company Pathway detailed the technical foundations of its new BDH-CQ model.

This follows a precursor model known as "Dragon Hatchling" that the scientists created in 2025, which was designed to accurately simulate how the neurons in the brain connected and strengthened during the learning experience.

In the new study, the scientists described how they evaluated BDH-CQ's performance against a foundational 2019 benchmark that helped set the current standard for measuring progress toward artificial general intelligence (AGI) — the point at which AI has matched or surpassed human capabilities in all domains.

The 2019 benchmark, known as ARC-AGI, uses nonverbal reasoning puzzles — such as rotating a series of shapes to complete a sequence — to measure the cognitive ability of AI systems.

Whereas humans are highly skilled at inferring the rules of these types of puzzles through trial and error, early AI systems were historically much less skilled.

BDH-CQ scored almost 30% on the ARC-AGI-1 benchmark, successfully solving the equivalent of three out of 10 puzzles in two or fewer attempts.

Although numerous models have achieved significantly better scores on this test, the underlying reasoning approach that BDH-CQ is based on makes its size and usage costs dramatically smaller than models built atop the traditional transformer-based architecture.

For example, while OpenAI's entry-level lightweight reasoning model GPT 5.6 Luna (Low) achieved a slightly higher score, the study stated that this "modest accuracy gain" cost roughly 11 times as much as BDH-CQ in terms of relative token costs — the metering system that AI companies use to measure the cost of running AI systems.

This type of AI model architecture, if adopted widely, could have a dramatic impact on the overall cost and scale of AI deployments, the scientists believe.

More than meets the eye BDH-CQ was trained on just 150 million parameters, while parameters for the most advanced, "frontier" AI models such as Meta’s open-source Llama 3 70B or Llama 3.1 405B typically number tens of billions to hundreds of billions.

In the world of AI development, fewer parameters means that models are faster to train and cheaper to run.

The researchers, however, said these results also imply that the model's cognition capabilities could scale significantly when expanded to larger parameter sizes.