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Claude Opus 4.8
271–280 of 1001 posts
Re: Claude Opus 4.8
#272Why does anthropic change the set of benchmarks they use with every new model release? https://www.anthropic.com/news/claude-opus-4-7 https://www.anthropic.com/news/claude-opus-4-6
1. Benchmarks saturate
2. They select the most impressive improvments
Re: Claude Opus 4.8
#273"We’re making swift progress on developing these safeguards and expect to be able to bring Mythos-class models to all our customers in the coming weeks."
Re: Claude Opus 4.8
#274I'm very suspicious of these same price model launches. It feels like they're benchmaxxed so they can put everyone on them and reduce their compute costs behind the scenes. If the model were genuinely better why wouldn't they charge more for it? Charging the same for something better is a race to the bottom. Opus 4.7 wasn't noticably any better for me, I still use 4.6 because it's cheaper.
Models are already expensive. Increasing price means losing customer. And, I think GPT 5.5 is much better at opus these days.
Re: Claude Opus 4.8
#275My experience with these new releases is that the gains in performance are negated by the price increases and it seems like: Performance gains: 1.2x Price increases: 1.8x
Yet people don't use old models through the API much, because changes in benchmark space dont map linearly to changes in utility space. An improvement from 98% to 99%, which is 1pp, might be 2x as valuable for some application. Also benchmarks will asymptote no matter what, that's baked in.
Re: Claude Opus 4.8
#276There is a hole in the boat's bottom due to Chinese models. They might not be as good but they are not bad either or at least I had hard time finding any issues with Deepseekv4 Flash and Pro variants. They get their job done sometimes rarely giving up till they are done what they are after. So even for enterprise deployments, as the dust settles down, CFO/CTOs might find out that deploying on an internal cluster of G…
I had been saying this on HN repeatedly: people are going to use the smartest models for coding. They don't care how cheap your tokens are if they don't have the highest probability of solving your programming tasks. And I was dead wrong. Now I mostly use DeepSeek Pro myself.
I pretty strongly feel the opposite way. Granted I have not used deepseek enough to “know” their model idiosyncrasies as well as Anthropic, so there is a partial skill issue. But I just find it really hard to justify using a less powerful model while I work.
The most I’ve ever spent in a month extra on API tokens for my own work is $200, and I pay for the $200/mo Claude. I use these models quite a lot, though not idly (I usually just walk around and do other stuff until I know how im going to approach the next set of problems). So it costs me about $3000/year to get as much as I want of the best model available. Already that seems low enough to not be worth stressing out too much about optimizing it, because it feels like an indisputable good value, and trying to save money with a less powerful model would be optimizing for a $1000-$2000 saving at the expense of a large portion of my work taking longer or being more frustrating and iterative.
That’s not a flex or anything, I get that in other countries $3000/yr is a lot of money for a software developer and also a lot of people would perhaps rationally be better off doing X% worse at work or spending Y% more time on tasks to save $Z, if their productivity improvements didn’t translate to more salary. Otherwise if your performance has more upside I really do think that the smartest models are better with the current pricing scheme. Deepseek and the other Chinese models spend a LOT of time thinking, and tend to be much more jagged (benchmaxxed) in performance. How can dealing with that over an entire year be worth $2k?
The only situation I can think of where sacrificing my own time/performance to save on inference is batch compute (of course, $1k vs $100k is different from $30 vs $3k) or work where the tier 2 models have crossed the “good enough” threshold. But I think Opus is not even close to that threshold generally yet. As it gets smarter I, and I think most others probably, just try to do harder things faster and hit the next wall.
Re: Claude Opus 4.8
#277A 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'm curious to poll HN on this issue. Do you feel like we've had meaningful/noticeable gains in terms of your programming workflows between 4.5 and 4.7? My 2¢, I personally feel like all of the productivity gains since 4.5's release (in November 2025!!) have come from improvements to the harnesses (cc, cursor cli, codex, opencode, whatever) AND from the context window expansion from 200k to 1M. But the actual "raw" i…
They all feel, more or less, the same to me in terms of output capabilities. Mostly get simple things right, can get more complex things right with nudging, eventually get stuck hard on something that takes a bunch of iterations through it/logging/etc or me fixing the code manually.
Re: Claude Opus 4.8
#278Earlier 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…
you just need to look at Mythos to see the jump in performance from a 10T(?) model. As they scale, they get more capable. We might have an yearly release, but I believe the releases will continue, as long as scaling laws are in tact, and there's huge problems still need solving. (think cancer)
And how are we meant to look at Mythos? Do you have access?
Re: Claude Opus 4.8
#279Earlier quoted context omitted.
Incremental gains compounds.
meta threw in the towel when it came to producing AI models since their gains couldn't keep up with China.
Has meta stopped producing new models? I figured they were just regrouping after all the drama they’ve had recently. Meta’s massive user base means they don’t need to be involved in the customer acquisition rat race. Once they have a model they’re happy with they can have a billion people interacting with it within a month.
Re: Claude Opus 4.8
#280> One of the most prominent improvements in Opus 4.8 is its honesty Anthropic talks about their own models as if they're discovering new species in the wild...
Models might be sentient or conscious to some degree. Anyone saying they are confident one way or another is being unserious and irrational.