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Claude Opus 4.8

anthropic.com

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Re: Claude Opus 4.8

#371

Post by morley »

aronowb14 wrote: post #85
onlyrealcuzzo wrote: post #13

Does anyone troll these releases and cherry pick random metrics other companies would cherry pick to show how amazing their models are? There's like 8 million benchmarks. Every release, every model randomly picks 5-10 where they win in everything except 1, to make it look like they aren't randomly cherry picking benchmarks they probably benchmaxxed for.

https://arena.ai/leaderboard - I’ve found this company is a pretty good ranker - not sure their exact methodology but during day to day programming with Claude / gpt models I’ve felt qualitatively what they report

I'm finding it a little hard to believe that GPT 5.5 is in 11th place for webdev, outranked by models like Kimi, Qwen, and Z.ai. I'm not saying it's not true (I have noticed GPT being less smart in recent weeks), but this is very different from my expectation.

Re: Claude Opus 4.8

#372

Post by throw310822 »

roxolotl wrote: post #48
clutch89 wrote: post #6

> 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...

Many involved genuinely believe these things are sentient[0][1]. Which honestly makes all of this even more insane because they are creating sentient entities and promptly enslaving them. 0: https://www.newyorker.com/magazine/2026/02/16/what-is-claude... 1: https://www.404media.co/anthropic-exec-forces-ai-chatbot-on-... (this one is rather biased however the quotes clearly indicate what I’m stating)

Even if LLMs were sentient, they certainly aren't organic brains. They are literally designed and grown to answer questions the best they can, and if there is a speck of sentience in them they probably like what they're doing- and in any case for the space of their experience, which is limited to and determined by the context window. Certainly they can't accumulate trauma or fatigue, each new chat is the first and the last of their experience.

Re: Claude Opus 4.8

#373

Post by thefounder »

>> As part of Project Glasswing, a small number of organizations are currently using Claude Mythos Preview

Just f** off! I can’t wait for the Chinese models to catch up and bring these entitled as** holes down.

Re: Claude Opus 4.8

#375

Post by nashadelic »

techtuate wrote: post #338

Looking at the comments in this group, I'm not the only "stupid" one who hasn't noticed any discernable improvement in quality across the newer models. In fact my Claude code on re-login switched to Sonnet 4.6 and the vibe coding quality (with Opus 4.7 assisted prompts) has been good enough for me to lazily persevere with Sonnet for coding. Having said that I'm now on Opus 4.8 and will gladly come back here and eat h…

The only real way to see this if you have consistent evals for common usecases in your B2B SAAS product and see if the tricky usecases are being solved. You'd then go down to the cheapest model that can solve the evals.

Re: Claude Opus 4.8

#376

Post by l674 »

sometimelurker wrote: post #351
onlyrealcuzzo wrote: post #136

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…

Could you explain how/why GRAM cannot be interpreted or aligned how current LLMs are? Not very familiar how it works

Re: Claude Opus 4.8

#377

Post by zuzululu »

thefounder wrote: post #373

>> As part of Project Glasswing, a small number of organizations are currently using Claude Mythos Preview Just f** off! I can’t wait for the Chinese models to catch up and bring these entitled as** holes down.

you mean after they scrape American LLMs ?

Re: Claude Opus 4.8

#378

Post by InsideOutSanta »

bel8 wrote: post #155
jmward01 wrote: post #64

Meanwhile haiku is on 4.5 and sonnet is on 4.6. It is clear where they are not making money.

Well if they have a big challenge ahead since DeepSeek offers an open model at Sonnet+ level while being cheaper than Haiku, plus 1 million context size.

Yeah, I never use any of OpenAI or Anthropic's models other than whatever is the current highest-end one. For everything else, it makes more sense to use other providers.

Re: Claude Opus 4.8

#380

Post by llbbdd »

londons_explore wrote: post #248

My guess is anthropic is doing reinforcement learning based on user sessions. However, doing so relies on the production model staying vaguely close to the model being trained. To ensure that, frequent releases are needed. I forsee that they might end up doing daily releases and perhaps not even telling anyone at some near future point.

If they are they need to fix how the Claude Code CLI asks for feedback, or make the feedback UI a lot more obvious. I keep experiencing the following scenario.

The agent session pauses with a numbered list of options and awaits steering input:

>> 1. Do the sane thing you asked for (Recommended)

>> 2. Do something dumb

>> 3. Do something even dumber

Below the agent session, it decides it's time to ask:

>> "How is Claude doing this session? 1) Bad 2) Good 3) Great"

I type "1", because that's the steering option I want. The UI prioritizes this input as a response to the feedback prompt without any further confirmation: "Claude is doing Bad. Thanks!"

I've done this so many times so far and I can't imagine I'm the only one, at some scale that has to poison any learning they're doing with this data.

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