Claude Opus 4.8 • Hacker News Board

Re: Claude Opus 4.8

#501

Post by teruakohatu » Thu, May 28, 2026, 8:00 PM UTC

gigatexal wrote: post #425

NiloCK wrote: 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…

why are the models the same price? https://platform.claude.com/docs/en/about-claude/pricing ``` Model Base Input Tokens 5m Cache Writes 1h Cache Writes Cache Hits & Refreshes Output Tokens Claude Opus 4.8 $5 / MTok $6.25 / MTok $10 / MTok $0.50 / MTok $25 / MTok Claude Opus 4.7 $5 / MTok $6.25 / MTok $10 / MTok $0.50 / MTok $25 / MTok Claude Opus 4.6 $5 / MTok $6.25 / MTok $10 / MTok $0.50 / MTok $25 / MTok Claude Op…

Why shouldn’t they be? They are probably the same size and cost the same to run. They are not doing full training runs (eg Mythos) so don’t need to recover insane training costs.

Re: Claude Opus 4.8

#502

Post by 0x696C6961 » Thu, May 28, 2026, 8:00 PM UTC

user- wrote: post #487

Bash(echo "hello"; pwd) ⎿ hello /Users/username/Work/Github/project Bash(echo test123) ⎿ test123 Read 1 file, listed 1 directory (ctrl+o to expand) Bash(echo "checking output works") ⎿ checking output works Read 1 file (ctrl+o to expand) ⎿ API Error: 400 messages.3.content.56: `thinking` or `redacted_thinking` blocks in the latest assistant message cannot be modified. These blocks must remain as they were in the orig…

Update the symlink to point at the previous version:

    ln -s $HOME/.local/share/claude/versions/2.1.153 $HOME/.local/bin/claude

Re: Claude Opus 4.8

#503

Post by MaxikCZ » Thu, May 28, 2026, 8:00 PM UTC

llbbdd wrote: post #380

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 Clau…

I think that filtering out data like yours was an interns afternoon project.

Re: Claude Opus 4.8

#504

Post by digdugdirk » Thu, May 28, 2026, 8:00 PM UTC

senko wrote: post #385

My fav coding benchmark for frontier models is to build a simple RTS game in one file (js/html/css). Claude Code with Opus 4.8 in ultracode mode nailed it, the best result so far: https://bsky.app/profile/senko.net/post/3mmwnrkwboc2v The prompt was: Create a simple but functional real time strategy (RTS) game similar to old WarCraft, StarCraft or Command & Conquer games. The player should be able to build buildings,…

Do you have a collection of these benchmark apps saved anywhere? I'd be particularly interested in seeing the relative cost differences between different models in a use case like this.

Re: Claude Opus 4.8

#505

Post by tancop » Thu, May 28, 2026, 8:01 PM UTC

toastmaster11 wrote: post #330

simonw wrote: post #73

I generated pelicans riding bicycles on both thinking level low and thinking level high: https://gist.github.com/simonw/68560eddb0b268a8417f80ceb7304... The high one is notably better - the bicycle frame is the correct shape, unlike thinking level low. For comparison, here's Opus 4.7: https://gist.github.com/simonw/afcb19addf3f38eb1996e1ebe749c...

I find the most miraculous thing about 4.7 to be that the pelican is facing left, wonder why the right facing everything is so ubiquitous in these images.

[dead]

Re: Claude Opus 4.8

#506

Post by dbbk » Thu, May 28, 2026, 8:01 PM UTC

onlyrealcuzzo wrote: post #136

NiloCK wrote: 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…

I'm frankly surprised the focus is still on these enormous "know everything in the world" models. I would think you could create an incredibly lean and smart "just React and React Native" model.

Re: Claude Opus 4.8

#507

Post by root_axis » Thu, May 28, 2026, 8:01 PM UTC

ishurand4 wrote: post #465

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…

And anyway, with quantum, there will be no need for frontier companies as you might be able to even run a 1T param model on a consumer quantum computer.

Even if quantum computing had any clear implications for LLMs (it doesn't), there is no such thing as a "consumer quantum computer" and there won't be in our lifetimes.

Re: Claude Opus 4.8

#508

Post by dbbk » Thu, May 28, 2026, 8:02 PM UTC

petterroea wrote: post #290

gAI wrote: post #115

Earlier quoted context omitted.

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. 4.7 has done some incredibly stupid things.

I think this is a more a consequence of the introduction of adaptive thinking and removal of extended thinking, than 4.7 specifically

Re: Claude Opus 4.8

#509

Post by staticman2 » Thu, May 28, 2026, 8:03 PM UTC

gigatexal wrote: post #425

NiloCK wrote: 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…

why are the models the same price? https://platform.claude.com/docs/en/about-claude/pricing ``` Model Base Input Tokens 5m Cache Writes 1h Cache Writes Cache Hits & Refreshes Output Tokens Claude Opus 4.8 $5 / MTok $6.25 / MTok $10 / MTok $0.50 / MTok $25 / MTok Claude Opus 4.7 $5 / MTok $6.25 / MTok $10 / MTok $0.50 / MTok $25 / MTok Claude Opus 4.6 $5 / MTok $6.25 / MTok $10 / MTok $0.50 / MTok $25 / MTok Claude Op…

Opus 4.7 and presumably 4.8 are more expensive due to a new tokenizer that translates data into more tokens per input.

Re: Claude Opus 4.8

#510

Post by onlyrealcuzzo » Thu, May 28, 2026, 8:03 PM UTC

dbbk wrote: post #506

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'm frankly surprised the focus is still on these enormous "know everything in the world" models. I would think you could create an incredibly lean and smart "just React and React Native" model.

> I would think you could create an incredibly lean and smart "just React and React Native" model.

You can, but it's not as useful as you might think.

It needs to at least understand 1 human language to understand your intent to implement features.

If GRAM turns out to be a 5000x multiplier for local reasoning, you could theoretically train a 500M parameter model on just a programming language to understand stack traces to fix bugs and be incredibly powerful.

But most people also want it to understand human language to implement features as well.

Because then it can't just understand React and JavaScript - it needs to understand thousands of commonly used dependencies, the DOM, CSS, HTML, etc...

And for that you need A LOT more parameters than you might expect.

You can definitely get a ~3B active parameter model that can run comfortably on today's hardware to be VERY good at coding once all of the SOTA architectures are added to a single model - especially if we get better tool calling to give models better context per language.

You might be thinking: why does it need to memorize dependencies? Can't it just stick all of them in it's context and use its super smart brain? No, context is king. You want to keep it as short as possible. The solution is not having a smart model and putting 10M lines of context in it. The solution is having a model with enough parameters to know what it needs to know. Researchers are already working on having "packs" of knowledge where you could download a 20M param pack just for some common dependencies in JavaScript (as an example) - but AFAIK this is likely years away (and may not prove effective).

You could get 100x performance if you feed the models ideal context... So a 3B model today can perform almost as good as ~300B model if you give it really good context vs flood it with mostly garbage it doesn't need across your repository.

If you feed it 100x more context to make up for its limited memorized general knowledge, it's going to perform thousands of times worse, completely eliminating any advantage it might get from GRAM...