Nace AI Releases 27B Drex Max Decision Model That Picks Without Generating Tokens
Nace AI has released Drex Max, a 27B decision model aimed at the many small choices inside agent pipelines: it reads a context and question, then returns one probability per candidate option in a single forward pass without generating any tokens. Nace claims it statistically ties GPT-6 Sol on Decision Bench v4 at $0.06 per 1K decisions and 183 ms, versus $10.17 and roughly 3.5 seconds for Claude Opus 5.5, though without chain-of-thought it answers only 21% of sampled HLE questions versus 56% for Opus.
Full Report
Rohan Paul (@rohanpaul_ai) · 2026-11-06 14
This is a brilliant release from @NaceAI .
It goes after the costly part of AI agents, the thousands of small choices, with a 27B model that only picks and never writes.
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Nace AI’s 27B Drex Max statistically ties GPT-6 Sol on Decision Bench v4 without generating a token.
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It reads a context and question, then returns one probability per supplied option in a single forward pass.
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Its RLAF training rewards verified answers plus a reasoning judge’s probabilities, while a KL penalty limits drift from supervised training.
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Drex Max costs $0.06 per 1K decisions at 183 ms, against $10.17 and about 3.5 seconds for Claude Opus 5.5.
Without chain-of-thought or tools, it answers 21% of sampled HLE questions against 56% for Opus.
↳ 引用 @NaceAI (Nace AI): How are Decision Models Trained?
Today, we are publishing the Drex Technical Report for the developer community! 🚀
Discover details on how the State-of-the-art Open-Source decision models are trained with RLAF.
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