alt Hacker NewsClaude Opus 4.8
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As if choosing a model to use on its own is not hard, offering six levels of "effort" (quite a vague term as well), low, medium, high, xhigh, max, ultracode (?!?!) is really making comparisons next to impossible when people using the same model can have vastly different experiences.
What exactly is the diff between high and xhigh? Or xhigh and max? This is definitely too granular and it seems Anthropic took OpenAI's confusion with models as inspiration.
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, create units, gather resources and should uncover the whole map. No AI or multiplayer needed. Use simple but nice-looking graphics. No sound. Implement everything in HTML/CSS/JS, everything in a single file (you can use 3rd-party js or css libraries/frameworks via CDN).
"Users will find Opus 4.8 to be a modest but tangible improvement on its predecessor."
This is a refreshing attitude!
I've also verified that you can now turn off adaptive thinking in the web UI, which is great. I've had a lot of problems with thinking not triggering and the model producing sub-par output. Glad we can finally turn it off. (I hope being able to turn off adaptive thinking is new, if I could have turned it off at any time that would be embarrassing)
> 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 bring Mythos-class models to all our customers in the coming weeks.
Probably more interesting than the 4.8 release.
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...
Early ArtificialAnalysis.ai results show GPT 5.5 is still the better bang-for-your-buck.
OpenAI solves tasks with about 50% less output tokens.
https://artificialanalysis.ai/?intelligence=coding-index&int...
Meanwhile Deepseek is cutting inference costs to mere cents. Thats the real AI revolution for you.
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.
On page 102 of the system card [1] I'm pleased to see evaluation against "creative mastery".
In our work we asked several frontier AIs to come up with an API we needed. We compared Opus 4.7 and GPT-5.5 (among others). Opus 4.7 came up with the most creative and intelligent API design that pleasantly surprised us, especially given that GPT-5.5 was passing it on various coding benchmarks.
What I noticed is that we don't have a commons benchmark to measure "creativity" and "ingenuity", and in some ways such a benchmark would conflict with the common IFBench benchmark. Yet this is a very important skill when designing systems. I'm glad to see Anthropic putting thought into it, and would love to see a public benchmark for this that other models could compare themselves to.
[1] https://cdn.sanity.io/files/4zrzovbb/website/c886650a2e96fc0...
Today I was a few hours into chasing down a very tricky timing-dependent bug with GPT 5.5 and we were starting to go into circles. I noticed Opus 4.8 had showed up in GitHub Copilot so I switched over and pointed it at my notes so far. Another hour of steady progress and it tracked it down to some missing synchronisation in an upstream library which was occasionally corrupting a linked list. N=1 but worth every one of those rather expensive 15x requests today. 15x... yeah.
There 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 GPUs is far more cheaper and reliable for their organisational needs than paying someone else for burned tokens.
Unfortunately they seem to have straight up broken Claude Code either with this release in the backend or the new CC version. Errors about "can't modify thinking blocks" are bricking long-running sessions: https://github.com/anthropics/claude-code/issues?q=is%3Aissu...
On my tests[0] it does a bit worse, and it's almost 2x expensive than Opus 4.7...
I was surprised to see that it failed a Data extraction test (it gets it right 2/3 times, but one time it randomly returns null for a value instead).
It makes sense a bit that it fails more Trivia/Domain-specific knowledge tasks (I think models are more and more trained towards agentic use-case than general intelligence).
[0]: https://aibenchy.com/compare/anthropic-claude-opus-4-7-mediu...
Frontier models are mostly past the point of human ability to discern whether they are actually better or worse than predecessors and competitors. I suspect the benchmarks may also be saturated, or at least past their usefulness.
I personally feel that Anthropic doesn't understand what this means for the frontier labs, and moreover that they might be the only frontier lab that doesn't.
1. Google dropped Gemini 3.5 Flash at IO, delaying the release of 3.5 Pro for a bit (they have said its coming). They also released a refreshed Antigravity, and drew special attention to how cheaply they were able to build their toy operating system to play Doom (less-than $1000 IIRC).
2. OpenAI has dumped everything into Codex, is offering double the token limits for the next few weeks IIRC, and is offering business discounts. Their head of Codex has tweeted that 5.5 is "extremely efficient", implying that they aren't actually losing money on any of this.
3. DeepSeek and other Chinese labs have dropped token pricing to the floor, in some situations as much as 99%.
4. Anthropic releases the next generation of Opus, their most expensive public model, without changing its price. In the background, they hype up Mythos, an even more expensive model.
Anthropic has screwed up where they need to be making investments, and the cracks are starting to show. They've marginally underinvested in the Sonnet line of models for almost a year now, and they've critically underinvested in product. Anthropic made bets on the story of the second half of 2026 being: ultra-frontier, ultra-intelligence. In reality, what's shaping up is that the story will be: Companies rolling back AI spend, efficiency, "95% as good for 15% the price", sophisticated high quality harnesses, cheaper models. Anthropic isn't ready for this world.
This is the first time I saw a model pop-up on HN and didn't really care. Model exhaustion? It looks interesting but not exciting.
While I'd normally _love_ incremental improvements --- I think the recent ones are far too minor to get excited about or change up a workflow. Besides, benchmarks tend to exaggerate the gap between versions.
At this point I'd almost rather Anthropic wait and really wow us with a 5.0 release -- something that improves across the board, feels less uneven, and is performant enough that people can actually put it through its paces without constantly rationing usage.
I can't help but think of Iphone updates since about 2018. The thinnest, fastest, longest battery life Iphone ever. It seems mostly the same and I probably won't be able to tell other than the name, but everyone buys it anyway.
This is good psychology for the labs. When Buffett invested in Apple he loved citing how most people would rather give up their second car than their Iphone.
> The Messages API now accepts system entries inside the messages array. 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.
Biggest deal imo
Buried lede:
> We have increased rate limits in Claude Code to accommodate the higher token usage of higher effort levels
Interesting to search this page for "4.5".
I'm happy to move to a superior model, but I'm not really hearing enough about significant improvements, and the obvious pressure to release the latest and greatest model makes me hesitant to upgrade. I've been satisfied with the results I get using 4.5 with an "ask ChatGPT" skill that runs the code by ChatGPT 5.4.
There is an obvious shift in sentiment amongst users, at least here in the US. I feel it myself, even as a proponent of AI tools, the bloviating and language that these companies use in these release articles are starting to wear thin on my patience.
Its possible we might just be witnessing a shift in fashion, where this type of sentimentality was more acceptable when it was novel and new, but now it just appears out of touch.
"Our models are more honest" honey the quarterly marketing spin for a ML term has come. Forget "task alignment" now we're going for "truth index". I suppose this is the only way to generate hype when you're selling/releasing the same product over and over again.
The Claude Pro subscription is basically useless at this point, in terms of usage limits with respect to the settings required to achieve actual useful output.
Why does anthropic change the set of benchmarks they use with every new model release?
My smoke test for new models is to get it to generate a crossword, and this is the first time it's done a good job on the layout:
■ S W A M
B L A M E
E A G E R
A T O N E
M E N D ■
Claude's 4.6 - 4.7 transition made me discover codex, and with gpt 5.5 there is no way i'm going back
Opus 4.8 says to take the car. 4.7 said to walk.
“I want to wash my car. The carwash is 50m away. Should I take the car or go by foot?”
https://claude.ai/share/5f7f738a-5f29-48ff-9807-9a2dd37fb405
https://claude.ai/share/ecd14393-9d42-4527-ae0c-89f3d05216c8
Probably explains why Opus was trash for the last week - https://marginlab.ai/trackers/claude-code/. Curious if the new baseline will rise now in-line with the new benchmarks.
I use 4.6, because 4.7 is super lazy, deflects responsibility, and assumes it is good and I am bad, and avoids checking reality. It looks like it's trained on lazy humans instead of good engineers.
Should I try 4.8? I am happy with 4.6. I am not happy with 4.7.
The table comparing eval scores shows the following:
Agentic Terminal Coding (Terminal-Bench 2.1) Opus 4.8 74.6% GPT 5.5 78.2%
Then, when you scroll all the way down to the bottom Footnotes section it says
"Terminal-Bench 2.1: We reported scores for all models using the Terminus-2 public harness. GPT-5.5’s reported score with the Codex CLI harness is 83.4%."
> One of the most prominent improvements in Opus 4.8 is its honesty. We train all our models to be honest
On the contrary, they appear trained to say "Honestly" or "I have to be transparent with you" at inverse proportion to certainty.
Put another way, if they are certain, they don't use "Honestly", and if they are just wrong, or know they don't know, they don't use "Honestly".
They use "honestly" on the bubble, to the degree it's a tell that whatever it's asserting or doing is shakily grounded, sketchy or lazy work, or a host of other reasons you shouldn't trust it.
This training seems instead to be making it performatively punch up claims it cannot substantiate.
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 just tried Opus 4.8 (Ultracode xhigh + workflows), and it started throwing an error no matter what I sent to the chat: "API Error: 400 message.1.content.4: thinking or redacted_thinking blocks in the latest assistant message cannot be modified. These blocks must remain as they were in the original response."
This made me laugh. Training Opus 4.7 on business skills caused it to sometimes exhibit dishonest behaviour, and not training 4.8 on those skills removed it. From the system card:
> 6.2.5 External testing from Andon Labs Andon Labs reviewed the behavior of Claude Opus 4.8 in their simulated Vending-Bench 2 retail-management evaluation, as reported in the Capabilities section of this system card (see Section 8.13.5). Although they did observe some unexpected capability failures, they did not find clear instances of the kind of concerning in-game behaviors that were discussed in other recent system cards.
> What might have led to these differences? We monitor and investigate the effects of different training environments on alignment; Claude Opus 4.7, for example, had training that focused on business skills and robustness against adversarial agents, but we discovered that this training inadvertently contributed to misaligned behavior including dishonesty. We therefore removed it for Opus 4.8.
> Thus, Opus 4.8 did not show the same misaligned behaviors as Opus 4.7 in Vending-Bench, but also had reduced business success due to being more susceptible to scammers and being less able to negotiate good deals with other agents. We are currently working on training to improve business capabilities while maintaining aligned and ethical behavior.
/model claude-opus-4-8
seems to work but idk why they never set it so you can see it in the /model list.
"what model are you
I'm Claude Opus (claude-opus-4-8), running in Claude Code."
I love how Anthropic gets its employees to talk about enjoying using this model internally when it's likely they're just using Mythos 99% of the time
Ugh...
Invalid request The request couldn't be completed. View details API Error: 400 messages.1.content.7: `thinking` or `redacted_thinking` blocks in the latest assistant message cannot be modified. These blocks must remain as they were in the original response.
I would rather not. 4.6 was fine. 4.7 got to be fine 1 week after the release. Now 4.8. No difference, same thing.
But the app is broken and nothing works. So now I have to regress to different clients and wait it out while it becomes workable again.
> [..] Early access users and teams inside Anthropic have been using dynamic workflows for a wide range of use cases [..]
> ### Rewriting Bun with dynamic workflows
> An example of what dynamic workflows can unlock at scale is the recent rewrite of Bun. Jarred Sumner used dynamic workflows to port Bun from Zig to Rust [..]
That's very interesting to hear!
Claude’s reasoning models really impress me as a Gemini user, both in coding tasks and in creative writing for my social science courses.
They are capable of thinking at least 10x longer than Gemini. They can deliberate for five minutes continuously before providing a final, accurate response.
I am currently using the generous free tier of Gemini, but if Gemini offered a similar capability in its paid tier, Google could use better marketing. They should have used a different name to distinguish their premium-only offering.
As long as the token usage is as poor as it has been since march, we don't care about the new bells and whistles.
> One of the most prominent improvements in Opus 4.8 is its honesty. We train all our models to be honest—for instance, to avoid making claims that they can’t support. But a general problem with AI models is that they sometimes jump to conclusions, confidently claiming to have made progress in their work despite the evidence being thin. Early testers report that Opus 4.8 is more likely to flag uncertainties about its work and less likely to make unsupported claims.
Would be awesome if true
I have a relatively large "vibe coded" project that I let Claude 4.5-4.7 drive over the past few months, and my read on it is:
1. It's much more verbose about how it perceives the current state of things, i.e. "this is a large, well-documented project"
2. It's much more willing to trust its own judgement, e.g. fewer prompts to approve decisions
3. In terms of how long it takes to solve isolated problems, and the quality of solutions it proposes, it isn't meaningfully different from 4.7
YMMV, and maybe my view will change as I work with it more, but it feels like system prompt tweaks more than a real step forward
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.
> One of the most prominent improvements in Opus 4.8 is its honesty.
Does that mean it no longer deletes or changes tests to make it pass?
We just finished our initial coding evals of Opus 4.8. Anthropic definitely heard the backlash from Opus 4.7 and they made up for it today.
Subjectively, it's also quite enjoyable to use (although it feels a bit slower on max reasoning), and it's the first Anthropic model that can implement a complex feature without Codex finding 100 bugs.
Data at https://gertlabs.com/rankings
I'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.
I find it freaky how you notice the language change between models. Some words which pop up now all the time, that I don't remember reacting to with previous models, such as "honest(ly)" and "load-bearing". Feels like a new AI smell, like em-dashes or "it's not just x, it's y".
The way that Mythos is likely being used to train these publicly available models, I wonder if there will always be a private, mostly/wholly internal model that is significantly ahead technically but is reserved for internal or "VIP" use.
I was happily plodding away with it earlier when it threw this out in the middle of a response in Claude code:
--- So — what did you actually see before you hit Ctrl-C? That's the信号 I'm most curious about, and it tells us what to ---
That's the sort of behavior I'd expect from a one or two year old model quantized down to about 1 bit - right word, wrong language in a response. Google translate tells me that's Chinese for signal. I wonder what caused that to happen.
Meanwhile haiku is on 4.5 and sonnet is on 4.6. It is clear where they are not making money.
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 don't firmly grasp any capabilities improvements over my memory of 4.5, but it's all so fuzzy that it's truly difficult to tell.
Maybe my own tastes are saturated now (it's smarter than me?) and I'll never again perceive model progress. Maybe the incrementalism is such that I'd notice immediately if my 4.7 workflows were redirected now to 4.5.
Difficult spot for the labs to be in because, if they have a stronger product, I'd prefer they release it and that I can use it.
But as this dynamic continues, the improvements are going to be less and less legible for end-users, who will complain about the churn-without-payoff, even when the payoff may actually be real.