11X cheaper than ChatGPT: Tiny 150M model just proved AI doesn't need to "think out loud" to be smart

Date:
Thu, 13 Aug 2026 01:05:00 +0000

Description:
Pathways 150M model achieves 29.5% on ARC-AGI-1 while costing 11 times less than ChatGPTs comparable reasoning model during inference.

FULL STORY ======================================================================Copy link Facebook X Whatsapp Reddit Pinterest Flipboard Threads Email Share this article 0 Join the conversation Follow us Add us as a preferred source on Google Newsletter Subscribe to our newsletter A 150M model reached 29.5%
while costing just $0.0007 per task ChatGPT scored higher, yet its comparable reasoning runs cost substantially more BDH-CQ performs reasoning internally instead of generating lengthy intermediate text Pathway, an AI lab focused on building Post-Transformer architectures, has released new benchmark results for its BDH-CQ reasoning model.

According to the researchers , their 150M-parameter model scored 29.5% pass@2 on the public ARC-AGI-1 evaluation set. It achieved this at a computed inference cost of $0.0007 per task, roughly eleven times cheaper than ChatGPT's underlying GPT 5.6 Luna (Low) model. Latest Videos From TechRadar Watch full video here: A cheaper way to reason Today, many AI tools waste computing power because of how they are designed, not because deep reasoning demands it.

"Today's AI pays a steep token cost for reasoning, but that cost is imposed
by architecture, not by any law of intelligence," said Zuzanna Stamirowska, CEO and co-founder of Pathway. You may like What Sudoku reveals about the limits of LLMs Is ChatGPT too cheap? Startup backed by the world's largest battery maker just launched a supercheap mini PC that competes with Nvidia's $5000 AI DGX Spark PC

We show that a different architecture changes the game and opens up a whole new space in terms of how much intelligence per dollar.

Amazon Web Services believes that BDH-CQs result is a promising step toward using advanced AI reasoning in real products more affordably. Are you a pro? Subscribe to our newsletter Sign up to the TechRadar Pro newsletter to get
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"Customers are increasingly exploring how to move advanced reasoning from experimentation into production, where performance, efficiency, and scalability all matter," said Nicolas Tarducci of AWS.

ARC-AGI-1, a widely used reasoning benchmark for AI systems, checks whether a system can infer an underlying rule from limited examples and apply it correctly to new inputs.

In this test, OpenAI's Luna model scored only slightly higher at 34.2%, yet running it still costs significantly more ($0.008 per task). What to read
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That price gap already includes OpenAI's recent 80% price cut on Luna, which began on July 30th of this year.

Further up the chart, Claude Opus 5 and Gemini 3.1 Pro reach 9798% but cost around $0.5 $0.6 per task, meaning the frontier's very top costs close to a thousand times more than BDH-CQ for the highest scores.

On the cheap end, Qwen3 235B costs over three times more than BDH-CQ while scoring worse than even its Low variant, so it isn't a real competitor on either price or performance.

"Pathway shows that model architecture, not just scale, can drive the next leap in AI reasoning," said ukasz Kaiser, co-author of the original 2017 Transformer paper. Why it costs so much less The efficiency gap stems mainly from a structural difference in how each system actually performs reasoning during inference computations.

Many reasoning AI systems generate intermediate text, adding one token after another before producing their final answers.

The longer that written reasoning becomes, the more it costs to run and the slower the AI responds to each request.

BDH-CQ works quite differently, quietly solving problems inside its own
memory instead of writing everything down first as visible text.

Pathway also confirmed that early experiments already follow standard Transformer-like scaling laws across model sizes from 1B to 600B parameters.

The company also plans to extend this approach toward harder benchmarks, including mathematical reasoning, ARC-AGI-2, and eventually full ARC-AGI-3 evaluations.

If these efficiency gains hold across larger and more difficult tasks, cost rather than raw capability could increasingly separate rival reasoning systems. Follow TechRadar on Google News and add us as a preferred source to get our expert news, reviews, and opinion in your feeds.



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Link to news story: https://www.techradar.com/pro/11x-cheaper-than-chatgpt-tiny-150m-model-just-pr oved-ai-doesnt-need-to-think-out-loud-to-be-smart


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