[drdaeman]

Surely that’s the training. Anthropic (and everyone else out there) knows quality degrades when the context gets long, so they trained the model to push for a stop. I’ve even got a very explicit recommendation to continue in a new session or preform a compaction, mentioning context length as a rationale.

[Wowfunhappy]

> Anthropic (and everyone else out there) knows quality degrades when the context gets long

Everyone says this, but for the life of me, I haven't encountered it. I actually think Claude gets smarter the longer a conversation goes on (up until compaction).

I have noticed Claude trying to wrap up long sessions, and it's extremely annoying. Using `/goal` mostly neutralizes it.

[walrus01]

It might be less about quality degrading, but on multi-user platforms running the model, they have an economic incentive to have each user not fill up the full size of the context cache. Filled context cache being held in GPU RAM is context cache RAM that isn't available to other users.

If the model is instructed to periodically ask the user to start from a clean slate context, and some users do comply with that, they probably have good stats on average size of context cache use for users who are presented with that answer (vs users who are not), basic A/B testing stuff.

Might also be performance related in tok/s for what users will perceive as a more speedy experience. For a much smaller scale example, compare local performance of qwen 3.6 27B (not MoE) Q8 with 250,000+ context available, run on local hardware, tok/s generation rate when context is empty vs when context used is at 95,000. Same principle will apply to a much larger model.

[cortesoft]

I used to experience the quality degrading with large context issue before, but I haven't in months. Is this still the case?

[doginasuit]

I've had several very long contexts that continued to be useful without any sign of degradation. I've supposed that it depends on how well things go in that context, if you run into a lot of confusion it falls to pieces pretty fast and obvious mistakes are more likely. It's made me more mindful of useful preliminary steps that were probably a good idea anyway.

[bwhiting2356]

Conversations that go on forever (and spend money forever) makes for a very unhappy customer

[lstodd]

Many people here to explain that they were holding it wrong. Meanwhile mindshare expands, tokens are sold and no one mentions that the only way to hold it 'right' is to not hold it at all.