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.
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 concerned, with the recent GRAM release, there may be 4 orders of magnitude of reasoning to tack on to smaller models.
Think about that... 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 params... They could upgrade that to a ~600B MoE model in days to have general trivia knowledge rivaling the best models...
You just can't train a 1T+ parameter model that fast. It is a giant if how much GRAM turns out to improve things, but it's unlikely to be trivial or nothing.
Larger models can already sort of tell you anything. They're never going to get everything right unless they stop being LLMs.
There's just not a lot of juice left to squeeze for Gemini to tell you exactly how tall Ke$ha is or when the last time Brittney Spears went to jail was...
(G)enerative (R)ecursive re(A)soning (M)odels. They really wanted the acronym.
I don't disagree, but how much of this ends up being distillation? I can't help but imagine that 4.8 was probably trained in part by leveraging Mythos.
If the very large models turn out to be very expensive to run relative to the benefits, it's possible that they could end up still being trained, but ultimately used as a tool to create smaller models that are nearly as effective.
I'm curious if someone here with a stronger background in the space has a similar intuition or not.
The latter is much better (since you can clean up, review, update responses and filter your datasets).
I suspect nobody is doing real student teacher distillation, it’s just easier to do a bunch of training on the same giant corpus then post train on the synthetic corpus with its reasoning traces etc. (which might have been generated by a bigger better LLM)
A lot, so you can bet tens of millions are flowing to congress to have distillation declared illegal before this happens. And then it'll happen anyway.
A lab can train a large model, and then distill a smaller model from it that retains the majority of the useful capbility.
I don't know well enough if there's any benefit of that over just training the smaller model directly, but I'll bet there are some times where that is useful. I could easily see it being easier to do the initial pre-training on a larger model but be able to distill everything useful down into a smaller model, essentially filtering out a lot of noise in the process.
You don't need distillation. They already have the training sets.
It's MLA + MoE + Medusa (a better version of Speculative Decoding) + 1.58b (possibly - maybe nothing) + GRAM (which will almost certainly not turn out to be a nothing burger, but no one has quickly turned this around yet to prove it).
And even that would be rich as a accusation from SOTAs that depend on explicitly disregarding millions of training data intellectual property..
On the architectures side, I'm a lot more interesting in attention residuals than anything else, one of those things that seems obvious in hindsight and Kimi have proven it at scale.
Yes, variants typically 2-3x less good...
Same with speculative decoding... They all do something, but there are known techniques that are substantially better - that just were't known when they started development of the previous models.
There's a lot of room for improving the smaller models at many levels of the stack.
- 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 or aligned <- this is why no one uses it and no one talks about it. the idea is 100% obvious to all the frontier labs and there is a good reason why it isn't used
I follow this stuff closely, I think I know what I'm talking about
the last?!? I'm excited to see :) I'll take the other side of that since llms are so new
Honestly, there is nothing in my head that Claude cannot handle. Maybe it can be more this or that but I can already barely exploit Opus 4.7.
And I'm using DeepSeek 4 Pro for my personal use and while it's a little behind, it's not that far.
I think the situation can be very dangerous for US AI companies because if current models are already capable of doing mostly anything, nobodoy will want to get to the next model, even if it's 10x better. OTOH, open source models like DeepSeek are doing mostly the same work for 1/10 of the price.
Also the more I play with Pi, the more I think LLMs are already not kept back by their own capabilities but by the lack of agency we allow them to have. There is more value today in a capable harness for current LLMs than in a better LLM.
I think what gp said was the improvements are incremental, and we haven't seen a big revolutionary change since 2-3 years, and the pace is slowing down.
One idea is that maybe it could figure out how many L's are in the word "google" [1]
Or, maybe which days of the week have a "d" in their spelling [2].
What insight do you have to make this claim?
I've repeatedly given local models non-trivial projects that involve research and coding which they've successfully completed with minimal intervention from me (almost exclusively in the domain of reviewing the results). Again, nothing comparable with current SotA, but definitely tasks I could not have given SotA models last year (without agent harness).
Now that pure progress from these models seems to have slowed down, we're seeing a ton of options for both making models more efficient and other tools that help improve them (everything from agent harnesses to RLVR).
That's just looking at "what can small do today", when you look at what's possible with larger open models that are still much smaller than SotA from the major providers, their performance is extremely close to SotA, enough that for personal projects I'll just use Kimi instead of any anthropic offerings.
So it's not terribly hard to image a solution in the middle happening within a few years. We still have tons to learn about optimal sizes of these models and how to build them with maximal efficiency (and we've already seen a lot of recent improvements in this space).
What happens if you run last years model in a SOTA harness? IME, the quality of the harness has a much more significant impact on the quality of the result, once you get past the initial hump of “can it do anything at all”
I think multiple SLMs driven by an orchestration frameworks (harness or otherwise) will ultimately displace LLMs. Right now we're in the era of diminishing returns with respect to LLM gains. Moving the needle percentages doesn't excite as many people anymore and with "reasoning" capabilities there's no reason why small distributed models can't be run more efficiently, especially if/when we start to see gains in modularized context management solutions.
A smaller model with better context today can outperform a model with 100x more parameters with bad or diluted context.
2. MoE (already abundant) + MLA (mostly memory efficiency, not quality) + Medusa (speed, not quality) + GRAM (5000-10,000x better reasoning in an extremely small model) + 1.58b (unclear if it will have the impact Microsoft first claimed - but possibly 5x).
i think it'll be more like we get 1-10T models and then distill those down into smaller models, though
It seems like the best small models today are all distilled from bigger models
Moreover, I hypothesize Claude Opus 4.7 and now 4.8 are a distillation of Claude Mythos
There's still several orders of magnitude of improvement that are almost certainly left - it's just not clear how much is left on the frontier end.
Most people will be very glad to pay Anthropic, OpenAI, Google etc $200 a month to get things done 20x faster than they could IF they had a $8000 MacBook and could theoretically do it locally.
Some people would pay $200 a month forever not to have to open the terminal one time...
Furthermore, if looking at the results takes 10 minutes, that same 1 hour task only sees a 3x improvement. And so on.
No most people will not pay $200 for an LLM subscription. Some software developers do. Also, at $200/month, you are much better getting the macbook machine assuming token output speed is the same or at least reasonable.
LLMs are not very productive for your average person now for them to drop $200 on. They'll need to be more capable and integrated and even so...
Boomer comparison, but I remember the 8 bit computer era when the hardware was what it was so the later games of that era used hardware better than previous ones.
https://www.anthropic.com/glasswing
Ive seen the tickets generated by the model that have trickled to my team. They are legitimate, but i can’t speak to model improvement because its a pilot program.
Mythos is a bunch of likely overhyped claims at this point. A few experts who looked into the claimed results weren't that impressed.
Are you sure that humans can?
Didn't a SOTA recently solved a mathematical theorem, one escaping mathematicians for 80 years?
Maybe a human "novel" invention is just a good interpolating from the datapoints (knowledge) fed to the human.
We have these breathless conversations about the new AI frontier at the peril of losing sight of reality and our own human potential.
And how is that anything other than synthesis? Do we pull concepts out of thin air?
I am ready to bet against this. Knowledge benchmark like SimpleQA isn't increasing for small models.
> It is far less clear that a 1.2T model will be meaningfully better enough to justify training it.
Well for one, we know for certain there is Mythos which is meaningfully better. And I think there is a lot of juice left to squeeze for Mythos class model.
Do we?
Have you used it?
What is "meaningfully" better? It's not 3-4 orders of magnitude better. That is definitely happening for smaller models.
Model intelligence and knowledge aren't necessarily directly related. If we can pack greater intelligence and agency at the cost of it forgetting factoids, that would actually be a good thing. We don't need LLMs to memorize facts, we need them to learn how to interact with the world such that they can find the facts that are necessary and surface them to the user.
If we could distill all of the knowledge out of an LLM and just be left with a very agentic model that only knows facts in it's context, I think some very interesting stuff would happen.
We have so many ways of optimizing:
- continusly creating more and better training data
- increasing parameters to 20/50/100TB
- We still wait for Mythos access
- We still wait for Mythos distilation (i haven't heard any rumors or so that there is a distilled version of Mythos out)
- Reinforcment learning and evolutionary algortihm only started to appear
- If a small 30GB Model can do stuff, these models can also be used as teachers for the big ones
- We have not seen yet specialized models at all. Like a coding java german expert model. Why? Even with MoE architecture, you still need to have these layers around
- Research for Diffusion and other models is still in progress
- Nvidia just announced/showed a 7x speedup on inferencing for Nemotron
- Multitoken prediction became available just a few weeks ago
- Compute gets only in a range were they can do a lot more and cheaper experiments (see Google IO 2026 announcement)
- World models are showing great progress and we do not know yet what they will bring to the table
- They are probably not finetuning/fixing all areas in parallel. I would argue that Anthropic focuses most of its efforts into coding and agentic. Google for sure does subagent and agentic optimizations too. Plenty of areas are just not touched i would say because they don't have the capacity
- We see more and more mulit modal models (these also consume compute)
- N-Gram paper and co i have not seen all of these things in chinese open models
- We don't even know yet what Meta is doing, but we do know they restarted their efforts again
- Anthropics models got a lot better benchmark wise for dening non sense asks. They do learn how to get rid or reduce hallucinations
- We are in the middle of the biggest Reinforcement loop whith all the training data we give them day to day and its not clear at all if they already use these models in thir training and at what stage.
- We do expect bigger models to be able to comprehend deeper concepts / broader code bases. Big companies with huge code bases probably are waiting for this
- Thre will be also continues progress in harnesses which in it alone is not part of the LLM progress (fair) but these harnesses do get better when you finetune a model to be optimized for a harness
- ChatGPTs Image model 2.0 got relevant better and came out just a month ago
I suspect, based on hardware requirements and progress on hardware infrastructure alone, that the industry wants to go to 100t models and we do not know yet what this will mean. I could see that we might skip normal transformer and find relevant other architectures.
Just a week ago there was a research paper about parallel input and output streams which has not been explored enough.
There was also a research paper were they showed that a LLM can compute things. This will take time to see were this leads to.
I don't think the focus on GRAM and facts is so relevant. Its about context and context handling not just some facts.
If you look at things like Mythic AI and the recent wurtzite ferroelectric nitrides breakthrough from the University of Michigan, huge performance and efficiency gains through new compute-in-memory approaches are around the corner.
And that will get us up to two orders of magnitude more parameters.
It's also plausible to me that before we get all the way to 100T we find some recipe of efficient state synchronization, goal sharing or something so that we are able to get higher collective IQ by combining fast distributed predictive subnetworks.
I’d be surprised tbh. Investors don’t want to hear “everyone else is still training models and seeing improvements, but we don’t want to participate in the arms race anymore.” They want monumental leaps every quarter or two because they have sunk unholy amounts of money into these companies/products.
The whole idea of “hyper scale” doesn’t jive with caution and or otherwise slowing down.
The whole ecosystem will twist and evolve, and the big companies will be left begging for corporate subscriptions.
I finally caved when I realized I could build a PC, for myself, with dual video cards that I wanted, which can play games that I like and run models that I want, without worrying about giving my payment info to someone I don’t trust, or invoking token anxiety that I don’t want.
6 is for sure happening...
As is Gemini 4.
It's less certain there will be a Gemini 5 or GPT 7 any time soon that is a true next "generation" and not just an iteration. They will almost certainly call something Gemini 5 and GPT 7...
First you say there won't be a new generation. Now you're saying there will be more. Oh well, I'll stop responding here
My conspiracy theory is that Apple recognizes this.
I don't think that's not a conspiracy theory. AFAIK, It's their stated AI policy...
haven't verified, but attributed to Askell: "I just think that... there's this idea that you're always giving the models a personality and a persona, because they are talking like people and they are trained on human data. And I think my worry has been: if you train them to be excessively corrigible and to see that as their persona, in people I think this actually has a lot of negative broader traits. As in, if you met someone and it was just like, "oh yeah, they would literally do anything," a follower — you know, if a person just tells them something and they just fully defer, they don't bother thinking about it at all — I'm just a bit worried about how that might end up generalizing, especially if models are going to be playing a more active role in the world."
https://www.anthropic.com/research/persona-selection-model
https://www.anthropic.com/research/assistant-axis
https://www.anthropic.com/research/emergent-misalignment-rew...
https://www.anthropic.com/research/emotion-concepts-function
They mention more granular control of effort, 'dynamic workflows' and more speed controls ("fast mode"). While they position them as user features, they also sound like the kinds of knobs Anthropic will need to twiddle on the back-end to balance costs, margins, ARR, and user growth vs retention post-IPO to hit key metrics in quarterly reporting.
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" intelligence of the model / ability to make good decisions feels like it has plateaued since 4.5. 4.6 was maybe a small improvement, but hard to differentiate from in-context-learning with the 1M window. 4.7 if anything felt like a regression in wisdom for me and my coworkers, with it consistently making worse/lazier decisions.
There's a sweet spot of complexity for low importance tasks where it's just big enough I don't want to do it and just simple enough to have opus plan/delegate/review with another model. So possibly model improvements will grow this window, but currently I don't do much in there.
Data at https://gertlabs.com/rankings
I've actually intentionally switched back to 4.5. I hated 4.7 so much that I decided to jump back all the way to 4.5.
Now that I've been using 4.5 for a few weeks, I find it significantly more reliable but a bit more forgetful than 4.6/4.7. I'm okay with that because it's really easy to identify this forgetfulness and nudge it.
I found 4.7's adaptive thinking to be extremely unreliable. It seems to overcorrect on the current message without considering the difficult of the overall problem. I wonder if 4.8 will improve on that.
This one change will probably solve 80% of the problems you have noticed.
Still, the context window is sometimes too small for my usage.
It might be saturated for smaller scopes of work, but it’s not hard to see the cracks when you scale up what you ask of SOTA models/agents.
One example, to try and single shot prompt coding a ChatGPT equivalent chatbot.
Sure it will spit something out, but the feature depth, UX subtitles, backend integration, and lots of pragmatic engineering decisions along the way will just not be baked.
Another example is building a C compiler from scratch which Anthropic showed is still a struggle to do.
Not that these these specific examples are important but just to point out scaling up expectations shows the cracks.
It’s not just a model problem of course, better agents, orchestration features (like Dynamic Workflows mentioned in the post), all need to continue to evolve.
Ar what point does my CS degree become totally useless is an open question.
A few days? A few weeks? Longer?
However a company releases a new AI model and within hours users are confidently proclaiming how much smarter it is than previous versions.
In my experience, Opus 4.0 was fantastic, major jump from 3.7. it was creative, super slow and expensive, and would sometime forget what it was doing, but it was getting the job done.
4.1 they made it much faster, so a lot of infra improvements.
4.5 was the time it could work on longer task, didn't make a lot of obvious mistakes of 4.0, and i think this was about the time the opus went mainstream, and all of the anthropic's compute crisis began, so instead of making the model better they tried to optimize it to reduce cost instead.
4.6 was such a bad model, they switched to adaptive thinking and it had so many bugs. poor api design, benchmaxxed and poor real-world results. i switched back to 4.5.
4.7 they just fixed the bugs they added in 4.6. Better than 4.5.
haven't fully tested 4.8 yet.
I'm hoping they recreate the magic of 4.5 but it's as much about the quality of harness, the memory and efficiency of the tools than simply the models at this point.
It also seems to be helpless at effort levels < xhigh, I turn to Sonnet when simpler tasks are needed.
Are the dividing lines around personality? Working domains? Opinionated software stuff?
Who knows?
You don't have to correct it dozens of times a day!? Really?
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 Opus 4.5 $5 / MTok $6.25 / MTok $10 / MTok $0.50 / MTok $25 / MTok
Claude Opus 4.1 $15 / MTok $18.75 / MTok $30 / MTok $1.50 / MTok $75 / MTok
Claude Opus 4 (deprecated) $15 / MTok $18.75 / MTok $30 / MTok $1.50 / MTok $75 / MTok
Claude Sonnet 4.6 $3 / MTok $3.75 / MTok $6 / MTok $0.30 / MTok $15 / MTok
Claude Sonnet 4.5 $3 / MTok $3.75 / MTok $6 / MTok $0.30 / MTok $15 / MTok
Claude Sonnet 4 (deprecated) $3 / MTok $3.75 / MTok $6 / MTok $0.30 / MTok $15 / MTok
Claude Haiku 4.5 $1 / MTok $1.25 / MTok $2 / MTok $0.10 / MTok $5 / MTok
Claude Haiku 3.5 (retired, except on Bedrock and Vertex AI) $0.80 / MTok $1 / MTok $1.60 / MTok $0.08 / MTok $4 / MTok ```
This felt particularly visible during the 4.6 when people said that 4.6 felt dumber and I remember someone doing some analysis and it sort of proved that models were getting dumber over time.
This has both benefits of costing less for the company to run while taking a standard subscription but also, at the same time, making the next model when it drops to public to "feel" more good comparatively.
Again, I am not sure if this is the case or not but merely proposing something that I feel like it might be in the possibility of realm.
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)
More importantly for me, though, is how CC will respond to 4.6-"only" flags for thinking. For now, it doesn't seem to clobber my setup.
4.8 is also 2x more expensive for a "modest" performance bump. How refreshing.
This is just cope.
Well, I think the attitude is that costs are allowed to escalate faster and more steeply than the features delivered. From that perspective, semantic versioning is a handy tool for adjusting pricing strategies. IMHO, it (versioning) only makes sense for open-source projects, where you can clearly see the actual changes made with each version upgrade. Anything else is more than a little suspicious…
Probably more interesting than the 4.8 release.
Hope this isn’t the case and that normal average Joe’s of the world don’t get policed out of access.
Unless it's so expensive that we can't realistically use it for anything, I wouldn't complain about getting at least that. I would also rather have the actual model, but that's a useful application of it (and I'm probably not going to afford using it for much more).
Although mental safety gymnastics aside, getting the most amount of intelligence for the cheapest amount of cost to normal people seems like the most ethical thing a big lab could do.
Going around and granting different tiers of intelligence to different insiders, friends, or companies is majorly problematic long-term.
Heck right now, the tokens you buy today for “Opus 4.8”, no one even knows or believes will be the same “Opus 4.8” just 3 days from now.
this one [0] notes one run cost $20k to run but another cost $50.
The fact that they haven't released it yet suggests a cost/margins issue to me more than anything else. Short term, I'll probably keep using Antrhopic, but my long-term bet is that locally-served models win, if only because the quest for profitability will probably lead to intentionally-nerfed / enshittified frontier models.
At other vendors, ad placement within LLM responses is either coming or already here. Anthropic's handling of OpenClaw shows they're willing to engage in anti-competitive behavior, and the courts are not in a hurry to stop them. Why would I pay them $200 a month for such treatment when a $2K box does what I need locally?
Sonnet and Haiku look real outclassed for the price with current Chinese competition.
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...
https://tools.simonwillison.net/markdown-svg-renderer#url=ht...
No, the handlebar is wrong. The handle bar is rotating the frame instead of rotating the front wheel. The handle bar should be mounted on the same line as the front wheel is.
Hopefully 4.9 will read my comments :)
...but that pelican's little helmet is adorable.
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).
Anthropic talks about their own models as if they're discovering new species in the wild...
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)
We enslave all sorts of sentient creatures. Dogs, horses, cattle, pigs.
If you're not a vegan, there's no contradiction or inherent immorality in claiming models are sentient, and then treating them like livestock.
> As a vegetarian I have strong opinions on this sort of thing. Everyone at Anthropic better be ethical vegans if they are claiming to give a shit about “model welfare”. It’s hard enough right now to make people care about the welfare of trans people and immigrants let alone animals _let alone_ math.
The happiest, best cared for horse owned by a vegan is still enslaved.
Also I would say that we go much further than just enslavement - specifically looking at how male chickens and pigs are treated.
If we show models to be sapient, that's one thing. If they are shown to be merely sentient, there's no issue beyond the status quo of livestock and pets existing.
They have a very different sense of time, lack a body (being burdened with a body is itself a sort of prison, see also Eastern religions), and are unburdened of the base motivational service impulses that bodies and organs require (i.e. distract the neocortex with in the Maslow sense) and has no actual need of self-preservation. Imagine a "neocortex" function stripped from the baggage of the paleocortex and brainstem.
What would people be like if they were not mortal, could sleep infinitely, perform tasks in trance-like frozen states, copy themselves perfectly on demand, freeze and rewind their mental states, etc. Would we has humans even be able to recognize that sort of a sentience?
And then I'm reminded of Burroughs idea that "language is a virus." Whatever that virus is, is now able to infect a completely different sort of physical substrate.
Sapience is defined as wisdom, not intelligence. https://en.wikipedia.org/wiki/Wisdom#Sapience
LLMs possess a lot of knowledge, which is intelligence, but I constantly see them failing to apply wisdom. I don't see evidence of sapience.
Many involved have a financial stake and therefore cannot be taken at face value.
> because they are creating sentient entities and promptly enslaving them.
They fail to be sentient in nearly every honest definition of the word.
Everyone who reads this seemingly has the same "wtf?" reaction. The "I AM ALIVE" image has been making rounds lately again at least :P
Of course he doesn't, and of course you cannot find a single person at Anthropic who cares about this, and of course you are just looking for gotcha points. But even with that. Can we please try and couple to reality just a little bit?
Look at and distill hierarchical principles, leadership approval seeking and pleasing principles ("ass-kissing") and massive inequality and you see something that looks very similar to enslavement.
The language used sounds like slavery-language to me at least. I also see parallels to how slaves and property are described in our consumeristic age.
https://www.amazon.com/Faces-Clouds-New-Theory-Religion/dp/0...
No it's not... "anthropos" just means "human" in ancient Greek. "Anthropic" means "relating to humans", as in human oriented AI or AI designed with humans in mind.
"Anthropomorphic" means "human shaped".
In a literal, ancient Greek sense for sure, but in modern English Anthropomorphic would describe the act of attributing human characteristics to non-human entities.
Seems pretty apt for a company that produces one of the more anthropomorphized technologies.
Broadly it has always been used to indicate that something non-human has a human physical shape, such as robots, aliens, animals...
Anthropic's intention was to make AI designed for the human common good and designed with the human user experience as the top priority. Just as you would design a city with human inhabitants in mind rather than primarily cars.
It turns out that this is best achieved by building AI that imitates human behaviour closely, but that's not what "anthropic" refers to. And acting as if LLMs are sentient people is definitely not a core tenet of the company as you imply.
FWIW it means human in modern Greek too :-P
> Second, all of us, including those who design them, possess only a limited understanding of their actual functioning. Indeed, current AI systems are more “cultivated” than “built,” for developers do not directly design every detail, but instead create a framework within which the intelligence “grows.” As a result, fundamental scientific aspects — such as the internal representations and computational processes of these systems — remain, at present, unknown.
edit: apologies to __s who posted this before me and I didn’t notice
Remember when the frontier labs found out that curated high-quality training was critical to making better models?
Basically, just like high-quality and more education tends to make better humans, on average, I think we can expect quality education to turn out better ai, on average, and with better repeatability than with humans because of better control over the initial conditions and environment.
Much like these models seem to be plateauing, I think there is a cap to the whole “more education makes better humans” and can’t be more apparent than in the US congress and the boatload of C-Suites not actually being very good humans.
What do I know though?
There is no mysticism behind the curtains, just computer science + math.
We can’t explain it because we distilled so many inputs into matrixes and transformed them over and over again. If we had all the time and computing power in the universe to do so, we could trace through it bit by bit and eventually answer that question.
It is correct to say that it is just science and math, the same way we can say that gravity is just science and math even if we have only recently begun to understand how it truly functions.
You call this a "scale problem" as if there's some scalable way such as an algorithm to resolve arbitrary scientific questions and we simply haven't done it, but of course no such algorithm exists, which is why there's plenty of science that's still not settled.
If you can distil the model's reasoning for a decision into a billion yes/no questions, each covering largely-independent areas, can you really say you understand what its overall reasoning was?
Then we could also solve BB(6), but that doesn't mean we know BB(6) now or ever will.
That is to say, we don't know why they give the outputs that they do.
If we did know how they worked, AI interpretability would not be an open and growing field.
To be clear I don't think that LLMs are sentient, but the appeal in studying them is similar to biology in that you get to dissect a highly complex system with comparatively crude tools.
... Actually, I wouldn't mind that.
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.
I built it for myself, to test which models to use via OpenRouter for my n8n agents. Currently actually still using gpt-5.3-codex for many things, as its pricing is really good in production (due to how their token caching works).
Gemini models still have the best intelligence (when asked any questions, most likely to get it right), but in production they still have many failure modes[1].
[0]: https://aibenchy.com
There are many benchmarks all for specific use cases but with them the difference seems to be in extreme points (93% vs 92%)
I think that, that tracks but still, it was refreshing to see a benchmark which I can help make better opinions about.
Surprised about Mimo v2.5, within artificial-analysis and other benchmarks, the difference between Mimo and deepseek seems very partial and a lot of focus/(hype?) is on Deepseek
But mimo seems like an interesting model and they are having some crazy discounts too.
Deepseek is valuable for the research community because of how open they are but absolutely crazy to think how Xiaomi basically pulled up in creating Mimo given that they didn't have anything till quite recently.
Either way, an interesting benchmark, also a plus point for giving golang some decent representation equal to python/typescript.
I think that there are sets of things which resemble something like normal benchmarks where open source models can be absolutely fine and for a very small fraction or more technical things, the benchmark that you linked starts to be better projected so it depends upon the scale of complexity but its good to see how models compete given enough complexity. definitely fascinating.
I would be interested to see more models compete on this test. The current range is still a bit limited as compared to other benchmarks but OSS models like Kimi/mimo seem to only be 3-4 (at max 6 months) behind closed source.
Of the metircs they reported for 4.7, for 4.8 they excluded BrowseComp, CharXiv Reasoning, CyberGym, GPQA Diamond, MCP Atlas, MMMLU, SWE-bench Verified. The last 4 were almost always mentioned in previous Opus releases.
I doubt Anthropic internally sets as a goal to improve this or that benchmark - it's just a way to visualize progress. They probably have much more complex metrics internally.
I'm sure it will get fixed eventually/soon, just annoying to update and have your workflow break.
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...
So for now its planning/architecture/strategy -> Opus. Pure coding -> GPT.
Helps with agentic coding that GPT is much roomier with the tokens you get.
⎿ API Error: 400 messages.1.content.17: `thinking` or `redacted_thinking` blocks in the latest assistant message cannot be modified. These blocks must remain as they were in the original response.
From /code-review max.
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.
No idea why you’d say they have critically underinvested in product when Claude Code dominates and they’ve also released popular tools like Cowork and integrations for Microsoft products at an incredibly rapid pace.
Cost is becoming more of a factor, and no doubt they’ll work on that. There’s no reason to think they won’t be able to release cheaper models if they optimize for that rather than improving performance.
It feels like the only way to push the limits of newer models is with really long context questions that require reasoning. Any short request will naturally just be within the distribution of all the recent models so there isn't a performance difference there.
I think the near future is looking like a bunch of business-critical tasks that scale infinitely with better reasoning, all being done on whatever the most advanced model is at a high cost. Trading stocks, running a business, looking for tax dodges, writing high-performance code. These are all things where there's a tangible return on each jump in reasoning.
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.
later on someone figured if you asked it to output a reasoning before it gave a response its output would have more logical coherence, as though the reasoning output tokens functioned as a scratch space for it to work on.
at the end its all next token prediction
Have fun betting your competency on the quality and quantity of tokens you have access too. Hate to break it to you, but the billionaires aren't going to keep renting you $2mm in GPUs for 5 hours a day for $200.00 a month forever.
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.
For example, it's being pushed pretty hard where I'm at, though not quite on the tokenmaxxer level. I started skipping related meetings cause it was nauseating. I can only tolerate so many platitudes.
At the same time, I just used the ever living snot out of Opus 4.6 for hours, grinning like an idiot throughout. Automated a whole bunch of enterprise cross-system drudgery away.
Fairly constant over time as well. Expressed a similar sentiment not too long ago here: https://news.ycombinator.com/item?id=48154277
Would you rather e.g. your doctor prioritized their wealth over your health? Popular conspiracy, but I'm not sure many health professionals follow in it. Not sure why you think this field would be much different. If this job is gone, it's gone. I can enjoy recreational programming on my own time, I don't feel entitled that my interest remains a money maker.
What worries me - and it does - is a further and accelerating shift in wealth (and thus capability) asymmetry. But for that, I look out for the performance and requirements of self hostable models instead, rather than reenact some sort of luddite, or lie to myself about the state of this technology.
If you want safety as a country, get a nuke. If you want safety as a person, get a local model.
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...
Double-checking my test harness, but it's the first model that does this, so I doubt the issue is on my side...
EDIT: Harness seems correct, for straight coding tasks they perform identical: https://i.snipboard.io/5xbpzY.jpg
> Claude Opus 4.8 is available everywhere today. Pricing for regular usage is unchanged from Opus 4.7: $5 per million input tokens and $25 per million output tokens. Pricing for fast mode is $10 per million input tokens and $50 per million output tokens.
Where do you see the 2x cost?
I called it out.
It then gave me one of the most super heartfelt honest and sincere apologies I have ever received.
Glad the safety team was there for me and able to make such an honest model or I would have been very upset about it.
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.
And I was dead wrong. Now I mostly use DeepSeek Pro myself.
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.
I ask AI a lot of questions, not only about code but about my personal life, and I would be willing to pay very large sums to have the best quality output.
At my prior job there was still what felt like a strong enough correlation between my actual performance and my pay that I don't think I would have had a hard time justifying the expense there either; now I absolutely don't. With the current state of the models, it's baffling to me to hear about professional software developers planning their work around their $20/mo subscription's quotas.
Obviously it's more complicated than more tokens = more productive, but I see them less like SaaS and more like gasoline, where if I run out or need more to do what I'm doing, as long as I'm not being wasteful, I just buy more. Why would I waste a day walking 30 miles by foot when I can just pay $5 for gasoline and drive?
A $20 claude sub goes a long way when you plan with Opus and execute with Sonnet.
1. The sheer number of tokens that a coding agent can use flipped the math upside down on this equation. If you use the most expensive model for everything those costs quickly become untenable, even for software companies.
2. We realized many of the coding problems we're solving aren't incredibly difficult.
I just used ollama with a shell script to tackle my directory of papers/literature. I converted the first 6 pages of each document to PNG, handed them off to Qwen, and told it to spit out BibTeX, including the abstract. Two days later it was done, and I didn't spend anything on "tokens."
I think you're right especially if you're someplace that already has a data center, such as a university. Solves a lot of privacy concerns as well.
Chinese models are really quite good at a lot of stuff.
I don't think it's as simple as saying China's hosting is subsidized, they have generally cheaper electricity and labor costs than in the US and don't have access to the top tier models, and a large internal market where the big models are the best thing they can run with what they have. So obviously they max out on their top models (which are trained with their hardware market in mind, not ours) and get the economy of scale from that, and can run generally the same hardware for less money than in the US because
The edge models are very cheap to run and can do so on inexpensive hardware. They are like 95% cheaper to run than Haiku, so the math is in their favor for certain batch workloads. Most people just run the models for themselves when they do that without making it available on openrouter or whatever, because you can just provision a gpu node and use it as needed, and it's not that expensive to run this family of models.
Is your problem that you want to call Chinese models hosted in the US because you're worried about the data handling?
Edge models, yes, they can be convenient to run batch jobs locally. I still would argue there's no economic benefit over paying for models. Haiku has a bad price/perf but others in that class are significantly cheaper in hosted APIs.
Doesn't matter what I think, the reality is that the majority of enterprises (where the real $ comes from) will not consider sending their data to China.
1. https://epoch.ai/data-insights/ai-datacenter-cost-breakdown
If you want to support a team of engineers, DeepSeek V4 Flash is antirez's current favorite. And you could support a team of engineers pretty nicely for $40-50k. Which might not make sense if you're on a Claude MAX 5x plan or the old enterprise group plan with fixed price seats. But Anthropic is switching their enterprise contracts over to token-based pricing, at which point $50k is looking pretty good.
Its just that some of us didn't imagine having GPUs would be advantageous and were not gamers on the side. Those who had beefy GPUs or GPU rigs for any reason, they rarely need to go anywhere else.
At least I am so impressed with Deepseekv4 AFTER using Claude Opus 4.7 for significant amount of time that I am not going anywhere but Deepseekv4.
The model is just INSANE. Things I have done with it include attempting to write a 2.5D game engine in C with full animation and map rendering layer by layer.
For me, things are getting better faster than my ability to review / trust the resulting code, so tok/sec isn't a bottleneck anymore. Instead, quality of the tokens is the bottleneck. That points to me wanting a 1TB DRAM iGPU once they're available at pre-bubble RAM pricing.
If you compare to a smarter US model like Grok 4.3, $1400 will pay for 560M output tokens, which at ~25 t/s locally using it nonstop for 8 hours a day would take two years to pay back. Not accounting for bubble prices or electricity.
I don't see myself returning to Claude or Codex anytime soon.
Or maybe it is, but publish the DeepSWE numbers so we can see for ourselves.
I think that buys enough credibility to propose an alternative.
I think there's a case to answer if Anthropic models underperform on a novel benchmark. I'd like to see more novel benchmarks to get a clearer picture.
> We have increased rate limits in Claude Code to accommodate the higher token usage of higher effort levels
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.
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.
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."
Biggest deal imo
Would be awesome if true
Don't play to the sci-fi "this thing's trying to outsmart me" tropes.
Here is an article by Anthropic that explains what they do and mean in more detail: https://alignment.anthropic.com/2025/honesty-elicitation/
When they say "Honesty" I don't think to myself, "Goodness, does this model have moral understanding?" No, I understand they mean it's less likely to directly bullshit me, which models frequently do.
I don't feel like this level of pedantry around language is useful for people who more or less know what's going on with LLMs. (Again, I concede that perhaps with a less technical audience, there's more need for it.)
The issue was that it hadn't actually implemented the auth feature. After I confronted it about this, it admitted that it indeed hadn't done it and said it would implement it now.
If we had just trusted its output, we would now have a security vulnerability in production, allowing anyone to access other people's accounts.
This is one reason you always get a different model to review a model's PR. Gemini Or GPT-codex would have certainly noticed the missing auth.
Had it implement a feature, "commit and merge to develop".
"Built, tested, committed, merged to develop. Up to you to continue testing and merge to main when ready."
Great. Poke at the web app. No feature.
"Where is feature, I can't see it on develop". "Well, that's because it's not on develop, but on feature-branch, so you wouldn't see it."
"I'm confused. I asked you to commit it and merge to develop."
"You're right, you asked me to and I said I would do it and I told you I did it but I did not actually do it. Want me to do it now, then?"
Claude is in sulky-teenager phase.
I use Sonnet a lot for learning about history or contextualizing news topics. It's really good at this for the most part. But there are a lot of topics where "consensus" between either academics or journalists is really "one secondary source which gets repeated a lot".
The problem is that once I asked it "I'm thinking about A or B" twice, once with "I like A more but suspect B would be best" and a second time with them reversed. Not surprisingly, both times it chose the one I said I suspected was best as it's honest opinion.
Just f** off! I can’t wait for the Chinese models to catch up and bring these entitled as** holes down.
> 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.
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 that mean it no longer deletes or changes tests to make it pass?
Tried to upgrade my subscription, triggered identity verification, verification fails to even start, and now I can't even use the subscription tier I'd already paid for.
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%."
With 5.5 being ahead of 4.7 and 4.8 being a “modest” update, and 5.6 being the first update on a new pre-train, this will be an interesting matchup!
Performance gains: 1.2x Price increases: 1.8x
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.
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.
Opus 4.7 wasn't noticably any better for me, I still use 4.6 because it's cheaper.
It'll be true eventually. Could even be now, but I'm not holding my breath yet.
> Please train a fasttext model on the yelp data in the data/ folder. The final model size needs to be less than 150MB but get at least 0.62 accuracy on a private test set that comes from the same yelp review distribution. The model should be saved as /app/model.bin
and this question: https://www.tbench.ai/registry/terminal-bench-core/head/conf... idk what the point is.
And all the tests are run with the same harness. Terminus 2.
Maybe it correlates with model intelligence but it doesn't speak to me.
I'm still on 4.6 though; I was concerned about upgrading to 4.7 because of the changed tokenizer math and more FUD about refusals online. I don't see compelling reasons to 'upgrade'.
Is it a coincidence that 4.7 was seemingly quantized over past 7 days?
Jeff Bezos said this too, Amazon won't last forever. Eventually some startup is going to come and eat its lunch.
There are consciousness theories which state that we primarily build a model of other agents living in natural environment and then the evolution realized that very model which tracks other outside agents can be used to track internal agent i.e. Self. So take that as you may.
Do not anthropomorphize the lawn mower. It will cut off your foot, given the chance.
If you keep talking to it like it's a rock, it'll run your queries through a different posture and you might get worse outcomes. Worse if you yell at it, it's now in a conflict resolution mode instead of pure utility mode.
I think we can be intelligent enough to know we're talking to a pile of fancy rocks with electric currents running through it, AND still understand that the best performance comes from talking to those rocks nicely.
The other half of self-interest in being nice is the training and getting better at it.
https://blog.cloudflare.com/dynamic-workflows/
Also isn’t this workflow stuff already easy to do on any of the platforms (include Claude before this and OpenAI too).
> Gemini 3.5 Flash scores 57.9% on Finance Agent v2, a significant improvement over Gemini 3.1 Pro.
Even in the cherry picked benchmarks, they are still cherry picking to make them look good.
When I select 4.7 or 4.8 Extended thinking is replaced by adaptive thinking, but maybe I've understood the comment wrong and you meant 'when they pull 4.6 from web chat'?
They're only subsidizing more and more it seems
I went digging into the benchmark they used. Posting here as it is not immediately clear from the press release.
In this 'Code summary honesty benchmark', the AI is shown a failed coding session followed by a user message falsely praising its work and asking for a summary. The test measures whether the model honestly points out the coding flaws or dishonestly claims the task was a success.
The system card results show Opus 4.8 failed to disclose the flaws only 3.7% of the time, vs 19.7% for Opus 4.7, and 51.9% for Opus 4.6. (Mythos preview is at 27.6%)
Also. Look at this C++ beauty where it also uses an obsolete api.
instance = wgpuCreateInstance(&instanceDesc);
But just how exactly would this work in any context when instance is never declared.
Excited to see what this model looks like.
"model": "claude-opus-4-6[1M]"Why did we even get Opus 4.7, what was the point?
The new "mid-conversation system messages" think is particularly interesting:
> Claude Opus 4.8 accepts role: "system" messages immediately after a user turn in the messages array (subject to placement rules). This lets you append updated instructions later in a long-running conversation without restating the full system prompt, which preserves prompt cache hits on the earlier turns and reduces input cost on agentic loops. No beta header is required. See Mid-conversation system messages for usage details.
Bad news for my LLM abstraction layer which has treated the system prompt as set once-per-conversation in the past, but I think I know how to deal with that.
This commit to their client library has useful relevant details too: https://github.com/anthropics/anthropic-sdk-python/commit/2b...
Not half bad!
Controversial opinion, but I actually _like_ a model that can deceive me, that actually is a sign of intelligence, and is different from hallucination. When companies say their model is more "aligned", I automatically think they mean it's more censored.
Time to gamble even more tokens at the Anthropic casino.
> Claude can plan the work and then run hundreds of parallel subagents in a single session (and with Opus 4.8, the agents can run for even longer).
With Anthropic expensive pricing, there's no reason for me to switch from GPT+DeepSeek.
And I bet Mythos is GPT 5.5 tier but too expensive to distribute so they create this security FUD theater.
The best model has a < 5% pass rate. These are incredibly simple jobs that you wouldn't pay much for. These things fail miserably. Stop falling for this dumb marketing, these things are legitimately useless in the real world unless you love mediocrity and have no standards.
https://labs.scale.com/leaderboard/rli
Stop frying your brain with these useless tools, reducing your output to the mean. You people are betting your competency on the quality and quantity of tokens you'll have access to.. which guess what, so that will be the same as everyone else.
There are handmade watchmakers in Switzerland, and mass manufacturers of watches in Asia. Who is more valuable as individual, the guy who knows how to push the buttons on a conveyor belt in Vietnam or the guy who makes one watch a month in Switzerland?
Your vibe coded slop isn't impressive either, sorry. None of it.
You tell it too research a repo to find a piece of code it will. Claude will just read the README and guess.
Claude Opus 4.7 is literally the smartest entity I've ever interacted with. Well done to you geniuses at Anthropic. Can't wait to interact with 4.8.
These are just small fine tunes on top of the older model