For Claude, capability and dispreferring CDT are the ~same thing. Much more so than for GPT.

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.[1]) 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 DTBench capabilities on higher effort levels. It's unclear what the cause of this is. You can play around with this here. ^ When asked explicitly, models do seem to typically state a preference for FDT/UDT over (updateful) EDT. Discuss