We've previously reported that decision-theoretic capabilities and favoring EDT/generalised-one-boxing over CDT correlate in LLMs (both measured by DTBench). (Note that EDT, for the most part, doesn't come apart from FDT / UDT on DTBench.) Anthropic also replicate the same finding in their Opus 4.7 and Fable 5 model cards.

We recently noticed something funny: Capabilities and preference against CDT answers basically perfectly for Anthropic models. This holds whether you measure capabilities using DTBench (r=0.97) or TextArena (r=0.95). Also, for flagship models, it's basically the same thing as release date (r=0.97).

Here is the graph for OpenAI models. (graph shows 0.55 vs. DTbench capability. r=0.44 for vs TextArena, r=0.45 for vs release date):

Here is what it looks like with all models included (if you exclude Anthropic, the correlation drops only from 0.8 to 0.78):

Incidentally, the correlation between TextArena scores and DTBench capabilities is also higher for Anthropic models that any other model developer, although the difference is smaller (e.g., 0.98 for Anthropic and 0.87 for OpenAI). We also checked effort level vs. attitudes for the most recent models but it's too noisy to tell us much because models don't get that much better at [...]

The original text contained 1 footnote which was omitted from this narration.

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First published:
August 17th, 2026

Source:
https://www.lesswrong.com/posts/5T6GAsvLPFd3epJtd/for-claude-capability-and-cdt-are-the-same-thing-less-so-for

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Narrated by TYPE III AUDIO.

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“For Claude, capability and CDT are the ~same thing. Less so for GPT.” by Chi Nguyen, Emery Cooper

LessWrong (30+ Karma)

“For Claude, capability and CDT are the ~same thing. Less so for GPT.” by Chi Nguyen, Emery Cooper