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Claude Opus 4.8

anthropic.com

521–530 of 1001 posts

Re: Claude Opus 4.8

#521
post #77

A rambling comment: I think this is the first time we've had a third minor version bump on a frontier Anthropic model. (I count the 0.5s as major here, because they've been issued non-sequentially and also corresponded to massive capability leaps, eg, Sonnet 3.5, Opus 4.5). So now the Opus 4.5 family has successors 4.6, 4.7, and 4.8, each posting fairly modest claimed gains. My own experience w/ 4.6 and 4.7 are that…

I won't be surprised if the next gen frontier models are the last. There's orders of magnitude of low hanging juice to squeeze out of smaller models. It is almost guaranteed that a 60-90B model can outperform current SOTA in coding tasks within 2-3 years (design not certain, probably unlikely). It is far less clear that a 1.2T model will be meaningfully better enough to justify training it. As far as reasoning is con…

> Google, OpenAI, Anthropic could train a 30B GRAM-based model in days - and it could potentially have better local reasoning than the best model available today at >1T param

I agree but with their urgent IPO-driven need to keep increasing prices, the frontier vendors now have every incentive maintain the perception that frontier performance requires endless >$200K racks of unobtanium GPUs and RAM. While they'd love to reduce their actual costs, they'd only want to do it to the extent they are certain they can keep it secret. Otherwise, they can't maintain and keep increasing their prices. And post-IPO audited reporting makes keeping that secret even harder.

Game theory-wise they probably don't want their their armies of leading researchers optimizing frontier performance, at least in any way that would further accelerate the relative price/perf of smaller models or self/cloud-hosting. While they know the open source models will always improve, the still win as long as enough customers demand the latest frontier and the open source lag remains constant.

They profit most in a world where a few frontier labs stay far in front, drag-racing each other and expending vast capital. It keeps their customers reliant and paying top dollar while keeping low-cost alternatives farther back. They probably much prefer competing with a couple other frontier labs who have similar astronomical costs and biz models, than a world where self or cloud-hosted open-source models start closing the gap enough to start commoditizing their business.

Re: Claude Opus 4.8

#522
post #115
post #77

A rambling comment: I think this is the first time we've had a third minor version bump on a frontier Anthropic model. (I count the 0.5s as major here, because they've been issued non-sequentially and also corresponded to massive capability leaps, eg, Sonnet 3.5, Opus 4.5). So now the Opus 4.5 family has successors 4.6, 4.7, and 4.8, each posting fairly modest claimed gains. My own experience w/ 4.6 and 4.7 are that…

4.7 was the first time I had to resort to using the previous version (4.6) for most use cases. Hoping 4.8 rectifies this.

same!

Re: Claude Opus 4.8

#523
> And fast mode for Opus 4.8—where the model can work at 2.5× the speed—is now three times cheaper than it was for previous models.

this is what I'm happy about, if true. Opus 4.7 is frustratingly slow (and, at least in my experience, much slower than 4.5 was)

Re: Claude Opus 4.8

#525

Can anyone explain how this is possible? Developers can update Claude’s instructions mid-task without breaking the prompt cache or routing the update through a user turn. This can be used in a given harness to update permissions, token budgets, or environment context as an agent runs. Does this means the instructions are no longer just something in the early part of the conversation? (If they were, changing them woul…

Perhaps they trained it with a new special system instruction token that is specifically trained to produce the same result as changing the system prompt, but is inserted into the prompt mid-conversation?

Re: Claude Opus 4.8

#526

> Not only that, but we plan to release a new class of model with even higher intelligence than Opus. As part of Project Glasswing, a small number of organizations are currently using Claude Mythos Preview for cybersecurity work. Models of this capability level require stronger cyber safeguards before they can be generally released. We’re making swift progress on developing these safeguards and expect to be able to b…

In the Opus 4.7 release notes they mentioned intentionally making it worse at cybersecurity. [0]

This suggests that they're doing the same thing with Mythos now and the Mythos we get will be nerfed in that department?

Or more precisely, I think they'll have two versions of Mythos, and the scary one will probably continue to require a lot of paperwork.

https://www.anthropic.com/news/claude-opus-4-7

Re: Claude Opus 4.8

#527

Earlier quoted context omitted.

I won't be surprised if the next gen frontier models are the last. There's orders of magnitude of low hanging juice to squeeze out of smaller models. It is almost guaranteed that a 60-90B model can outperform current SOTA in coding tasks within 2-3 years (design not certain, probably unlikely). It is far less clear that a 1.2T model will be meaningfully better enough to justify training it. As far as reasoning is con…

I looked into this "GRAM" stuff a sibling comment links further to, and just to say: - this gets reinvented/rediscovered constantly under different names - it cant be trained very well (right now, will change) - massive theoretical improvements over current models (log_2(vocabsize)=17, residual stream dim is thousands of dimensions, recursivity means more information bandwidth by ~3 OoM) - BUT it cant be interpreted…

> - this gets reinvented/rediscovered constantly under different names

What are the different names? I haven't seen this before.

> - it cant be trained very well (right now, will change)

If you're sure it will change, then why are you certain that it hasn't yet, and if it's proven a 5000x boost in reasoning... why aren't they exploring this path more aggressively?

> the idea is 100% obvious to all the frontier labs and there is a good reason why it isn't used

Surely someone is willing to take a 5000x boost in reasoning on a small research model... None of them have even tried anything resembling this AFAIK. It does not seem like something 100% obvious to them.

Re: Claude Opus 4.8

#530

Given DeepSWE just blew apart the SWE-Bench Pro benchmark and handed a 14-point lead to GPT-5.5, it looks pretty bad that they've listed SWE-Bench first in the model release and no DeepSWE. Like, this isn't obviously an answer. Or maybe it is, but publish the DeepSWE numbers so we can see for ourselves.

I'm highly skeptical of DeepSWE. It rates GPT-5.4-mini as three times better than deepseek-v4-pro, but every time I use GPT-5.4-mini I find that it completely sucks at following directions.

Yeah, I share the same sentiment. I have yet to find a task where gpt-5.4-mini isn't bordering unusable.
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