[cube2222]

This seems to agree with my own previous tests of Sonnet vs Opus (not on this version). If I give them a task with a large list of constraints ("do this, don't do this, make sure of this"), like 20-40, Sonnet will forget half of it, while Opus correctly applies all directives.

My intuition is this is just related to model size / its "working memory", and will likely neither be fixed by training Sonnet with Opus nor by steadily optimizing its agentic capabilities.

[versteegen]

I'd agree that this effect is probably mainly due to architectural parameters such as the number and dimensions of heads, and hidden dimension. But not so much the model size (number of parameters) or less training.

Saw something about Sonnet 4.6 having had a greatly increased amount of RL training over 4.5.