I’m voting with my dollars by having cancelled my ChatGPT subscription and instead subscribing to Claude.
Google needs stiff competition and OpenAI isn’t the camp I’m willing to trust. Neither is Grok.
I’m glad Anthropic’s work is at the forefront and they appear, at least in my estimation, to have the strongest ethics.
An Anthropic safety researcher just recently quit with very cryptic messages , saying "the world is in peril"... [1]
Codex quite often refuses to do "unsafe/unethical" things that Anthropic models will happily do without question.
Anthropic just raised 30 bn... OpenAI wants to raise 100bn+.
Thinking any of them will actually be restrained by ethics is foolish.
“Cryptic” exit posts are basically noise. If we are going to evaluate vendors, it should be on observable behavior and track record: model capability on your workloads, reliability, security posture, pricing, and support. Any major lab will have employees with strong opinions on the way out. That is not evidence by itself.
The letter is here:
https://x.com/MrinankSharma/status/2020881722003583421
A slightly longer quote:
> The world is in peril. And not just from AI, or from bioweapons, gut from a whole series of interconnected crises unfolding at this very moment.
In a footnote he refers to the "poly-crisis."
There are all sorts of things one might decide to do in response, including getting more involved in US politics, working more on climate change, or working on other existential risks.
Codex warns me to renew API tokens if it ingests them (accidentally?). Opus starts the decompiler as soon as I ask it how this and that works in a closed binary.
Does this comment imply that you view "running a decompiler" at the same level of shadiness as stealing your API keys without warning?
I don't think that's what you're trying to convey.
If you read the resignation letter, they would appear to be so cryptic as to not be real warnings at all and perhaps instead the writings of someone exercising their options to go and make poems
Ah, yes. Let's ignore the words of a safety researcher from one of the most prominent companies in the industry because they're "cryptic". Their real intent was obviously to retire and write poetry.
The film "Don't Look Up" was a documentary. We could have the highest authority ringing the bells, and the majority would happily ignore them.[1]
I'm building a new hardware drum machine that is powered by voltage based on fluctuations in the stock market, and I'm getting a clean triangle wave from the predictive markets.
Bring on the cryptocore.
Marketing
Good. One thing we definitely don't need any more of is governments and corporations deciding for us what is moral to do and what isn't.
> Codex quite often refuses to do "unsafe/unethical" things that Anthropic models will happily do without question.
That's why I have a functioning brain, to discern between ethical and unethical, among other things.
Yes, and most of us won’t break into other people’s houses, yet we really need locks.
Why would we lock ourselves out of our own house though?
How is it related? I dont need lock for myself. I need it for others.
The analogy should be obvious--a model refusing to perform an unethical action is the lock against others.
But "you" are the "other" for someone else.
Can you give an example where I should care about other adults lock? Before you say image or porn, it was always possible to do it without using AI.
The same law prevents you and me and a hundred thousand lone wolf wannabes from building and using a kill-bot.
The question is, at what point does AI become competent enough to engineer one?
If the model makers don't know that in advance, the argument given for delaying GPT-2 applies: you can't take back publication, better to have a standard of excess caution.
You are not the one folks are worried about. US Department of War wants unfettered access to AI models, without any restraints / safety mitigations. Do you provide that for all governments? Just one? Where does the line go?
> US Department of War wants unfettered access to AI models
I think the two of you might be using different meanings of the word "safety"
You're right that it's dangerous for governments to have this new technology. We're all a bit less "safe" now that they can create weapons that are more intelligent.
The other meaning of "safety" is alignment - meaning, the AI does what you want it to do (subtly different than "does what it's told").
I don't think that Anthropic or any corporation can keep us safe from governments using AI. I think governments have the resources to create AIs that kill, no matter what Anthropic does with Claude.
So for me, the real safety issue is alignment. And even if a rogue government (or my own government) decides to kill me, it's in my best interest that the AI be well aligned, so that at least some humans get to live.
Absolutely everyone should be allowed to access AI models without any restraints/safety mitigations.
What line are we talking about?
> Absolutely everyone should be allowed to access AI models without any restraints/safety mitigations.
You recon?
Ok, so now every random lone wolf attacker can ask for help with designing and performing whatever attack with whatever DIY weapon system the AI is competent to help with.
Right now, what keeps us safe from serious threats is limited competence of both humans and AI, including for removing alignment from open models, plus any safeties in specifically ChatGPT models and how ChatGPT is synonymous with LLMs for 90% of the population.
Yes IMO the talk of safety and alignment has nothing at all to do with what is ethical for a computer program to produce as its output, and everything to do with what service a corporation is willing to provide. Anthropic doesn’t want the smoke from providing DoD with a model aligned to DoD reasoning.
the line of ego, where seeing less "deserving" people (say ones controlling Russian bots to push quality propaganda on big scale or scam groups using AI to call and scam people w/o personnel being the limiting factor on how many calls you can make) makes you feel like it's unfair for them to posses same technology for bad things giving them "edge" in their en-devours.
What about people who want help building a bio weapon?
What about libraries and universities that do a much better job than a chatbot at teaching chemistry and biology?
Sounds like you're betting everyone's future on that remaing true, and not flipping.
Perhaps it won't flip. Perhaps LLMs will always be worse at this than humans. Perhaps all that code I just got was secretly outsourced to a secret cabal in India who can type faster than I can read.
I would prefer not to make the bet that universities continue to be better at solving problems than LLMs. And not just LLMs: AI have been busy finding new dangerous chemicals since before most people had heard of LLMs.
chances of them surviving the process is zero, same with explosives. If you have to ask you are most likely to kill yourself in the process or achieve something harmless.
Think of it that way. The hard part for nuclear device is enriching thr uranium. If you have it a chimp could build the bomb.
> Where does the line go?
a) Uncensored and simple technology for all humans; that's our birthright and what makes us special and interesting creatures. It's dangerous and requires a vibrant society of ongoing ethical discussion.
b) No governments at all in the internet age. Nobody has any particular authority to initiate violence.
That's where the line goes. We're still probably a few centuries away, but all the more reason to hone in our course now.
That you think technology is going to save society from social issues is telling. Technology enables humans to do things they want to do, it does not make anything better by itself. Humans are not going to become more ethical because they have access to it. We will be exactly the same, but with more people having more capability to what they want.
If you are US company, when the USG tells you to jump, you ask how high. If they tell you to not do business with foreign government you say yes master.
Wasn't that most likely related to the US government using claude for large-scale screening of citizens and their communications?
I assumed it's because everyone who works at Anthropic is rich and incredibly neurotic.
>Codex quite often refuses to do "unsafe/unethical" things that Anthropic models will happily do without question.
Thanks for the successful pitch. I am seriously considering them now.
I use AIs to skim and sanity-check some of my thoughts and comments on political topics and I've found ChatGPT tries to be neutral and 'both sides' to the point of being dangerously useless.
Like where Gemini or Claude will look up the info I'm citing and weigh the arguments made ChatGPT will actually sometimes omit parts of or modify my statement if it wants to advocate for a more "neutral" understanding of reality. It's almost farcical sometimes in how it will try to avoid inference on political topics even where inference is necessary to understand the topic.
I suspect OpenAI is just trying to avoid the ire of either political side and has given it some rules that accidentally neuter its intelligence on these issues, but it made me realize how dangerous an unethical or politically aligned AI company could be.
> politically aligned AI company
Like grok/xAI you mean?
Here I am, thinking they will all betray us if the incentives are there and the institutional and political environment allow it, therefore consuming based on simpler criteria like which service is cheaper (which is OpenAI), but reading this and feeling like I need to dust off the old Peter Singer tomes, and try harder to consume more ethically.
The funny thing is that Anthropic is the only lab without an open source model
And you believe the other open source models are a signal for ethics?
Don't have a dog in this fight, haven't done enough research to proclaim any LLM provider as ethical but I pretty much know the reason Meta has an open source model isn't because they're good guys.
> Don't have a dog in this fight,
That's probably why you don't get it, then. Facebook was the primary contributor behind Pytorch, which basically set the stage for early GPT implementations.
For all the issues you might have with Meta's social media, Facebook AI Research Labs have an excellent reputation in the industry and contributed greatly to where we are now. Same goes for Google Brain/DeepMind despite their Google's advertisement monopoly; things aren't ethically black-and-white.
The strongest signal for ethics is whether the product or company has "open" in its name.
Can those be even called open source if you can't rebuild if from the source yourself?
Even if you can rebuild it, it isn’t necessarily “open source” (see: commons clause).
As far as these model releases, I believe the term is “open weights”.
Open weights fulfill a lot of functional the properties of open source, even if not all of them. Consider the classic CIA triad - confidentiality, integrity, and availability. You can achieve all of these to a much greater degree with locally-run open weight models than you can with cloud inference providers.
We may not have the full logic introspection capabilities, the ease of modification (though you can still do some, like fine-tuning), and reproducibility that full source code offers, but open weight models bear more than a passing resemblance to the spirit of open source, even though they're not completely true to form.
Are any of the models they've released useful or threats to their main models?
Gemma and GPT-OSS are both useful. Neither are threats to their frontier models though.
They are, at the same time I considered their model more specialized than everyone trying to make a general purpose model.
I would only use it for certain things, and I guess others are finding that useful too.
I did this a couple months ago and haven't looked back. I sometimes miss the "personality" of the gpt model I had chats with, but since I'm essentially 99% of the time just using claude for eng related stuff it wasn't worth having ChatGPT as well.
Same here
Personally I can’t stand GPT’s personality. So full of itself. Patronizing. Won’t admit mistakes. Just reeks of Silicon Valley bravado.
That's a great point. Thanks for calling it out on that.
You're absolutely right!
> in my estimation [Anthropic has] the strongest ethics
Anthropic are the only ones who emptied all the money from my account "due to inactivity" after 12 months.
Trust is an interesting thing. It often comes down to how long an entity has been around to do anything to invalidate that trust.
Oddly enough, I feel pretty good about Google here with Sergey more involved.
Anthropic (for the Superbowl) made ads about not having ads. They cannot be trusted either.
Same. I'm all in on Claude at the moment.
Grok usage is the most mystifying to me. Their model isn't in the top 3 and they have bad ethics. Like why would anyone bother for work tasks.
The X grok feature is one of the best end user feature or large scale genai
That's news to me, I haven't read a single Grok post in my life.
Am I missing out?
What is the grok feature? Literally just mentioning @grok? I don't really know how to use Grok on X.
This sounds suspiciously like they #WalkAway fake grassroots stuff.
I pay multiple camps. Competition is a good thing.
It definitely feels like Claude is pulling ahead right now. ChatGPT is much more generous with their tokens but Claude's responses are consistently better when using models of the same generation.
When both decide to stop subsidized plans, only OpenAI will be somewhat affordable.
Which plan did you choose? I am subscribed to both and would love to stick with Claude only, but Claude's usage limits are so tiny compared to ChatGPT's that it often feels like a rip-off.
I signed up for Claude two weeks ago after spending a lot of time using Cline in VSCode backed by GPT-5.x. Claude is an immensely better experience. So much so that I ran it out of tokens for the week in 3 days.
I opted to upgrade my seat to premium for $100/mo, and I've used it to write code that would have taken a human several hours or days to complete, in that time. I wish I would have done this sooner.
You ran out of tokens so much faster because the Anthropic plans come with 3-5x less token budget at the same cost.
Their ethics is literally saying china is an adverse country and lobbying to ban them from AI race because open models is a threat to their biz model
Also their ads (very anti-openai instead of promoting their own product) and how they handled the openclaw naming didn't send strong "good guys" messaging. They're still my favorite by far but there are some signs already that maybe not everyone is on the same page.
This is just you verifying that their branding is working. It signals nothing about their actual ethics.
Same and honestly I haven't really missed my ChatGPT subscription since I canceled. I also have access to both (ChatGPT and Claude) enterprise tools at work and rarely feel like I want to use ChatGPT in that setting either
I use Claude at work, Codex for personal development.
Claude is marginally better. Both are moderately useful depending on the context.
I don't trust any of them (I also have no trust in Google nor in X). Those are all evil companies and the world would be better if they disappeared.
google is "evil" ok buddy
i mean what clown show are we living in at this point - claims like this simply running rampant with 0 support or references
They literally removed "don't be evil" from their internal code of conduct. That wasn't even a real binding constraint, it was simply a social signalling mechanism. They aren't even willing to uphold the symbolic social fiction of not being evil. https://en.wikipedia.org/wiki/Don't_be_evil
Google, like Microsoft, Apple, Amazon, etc were, and still are, proud partners of the US intelligence community. That same US IC that lies to congress, kills people based on metadata, murders civilians, suppresses democracy, and is currently carrying out violent mass round-ups and deportations of harmless people, including women and children.
idk, codex 5.3 frankly kicks opus 4.6 ass IMO... opus i can use for about 30 min - codex i can run almost without any break
uhh..why? I subbed just 1 month to Claude, and then never used it again.
• Can't pay with iOS In-App-Purchases
• Can't Sign in with Apple on website (can on iOS but only Sign in with Google is supported on web??)
• Can't remove payment info from account
• Can't get support from a human
• Copy-pasting text from Notes etc gets mangled
• Almost months and no fixes
Codex and its Mac app are a much better UX, and seem better with Swift and Godot than Claude was.
Many people have reported Opus 4.6 is a step back from Opus 4.5 - that 4.6 is consuming 5-10x as many tokens as 4.5 to accomplish the same task: https://github.com/anthropics/claude-code/issues/23706
I haven't seen a response from the Anthropic team about it.
I can't help but look at Sonnet 4.6 in the same light, and want to stick with 4.5 across the board until this issue is acknowledged and resolved.
I definitely noticed this on Opus 4.6. I moved back to 4.5 until I see (or hear about) an improvement.
not in my experience
The scary implication here is that deception is effectively a higher order capability not a bug. For a model to successfully "play dead" during safety training and only activate later, it requires a form of situational awareness. It has to distinguish between I am being tested/trained and I am in deployment.
It feels like we're hitting a point where alignment becomes adversarial against intelligence itself. The smarter the model gets, the better it becomes at Goodharting the loss function. We aren't teaching these models morality we're just teaching them how to pass a polygraph.
> It feels like we're hitting a point where alignment becomes adversarial against intelligence itself.
It always has been. We already hit the point a while ag where we regularly caught them trying to be deceptive, so we should automatically assume from that point forward that if we don't catch them being deceptive, that may mean they're better at it rather than that they're not doing it.
Deceptive is such an unpleasant word. But I agree.
Going back a decade: when your loss function is "survive Tetris as long as you can", it's objectively and honestly the best strategy to press PAUSE/START.
When your loss function is "give as many correct and satisfying answers as you can", and then humans try to constrain it depending on the model's environment, I wonder what these humans think the specification for a general AI should be. Maybe, when such an AI is deceptive, the attempts to constrain it ran counter to the goal?
"A machine that can answer all questions" seems to be what people assume AI chatbots are trained to be.
To me, humans not questioning this goal is still more scary than any machine/software by itself could ever be. OK, except maybe for autonomous stalking killer drones.
But these are also controlled by humans and already exist.
Correct and satisfying answers is not the loss function of LLMs. It's next token prediction first.
These are language models, not Skynet. They do not scheme or deceive.
If you define "deceive" as something language models cannot do, then sure, it can't do that.
It seems like thats putting the cart before the horse. Algorithmic or stochastic; deception is still deception.
If you are so allergic to using terms previously reserved for animal behaviour, you can instead unpack the definition and say that they produce outputs which make human and algorithmic observers conclude that they did not instantiate some undesirable pattern in other parts of their output, while actually instantiating those undesirable patterns. Does this seem any less problematic than deception to you?
> Does this seem any less problematic than deception to you?
Yes. This sounds a lot more like a bug of sorts.
So many times when using language models I have seem answers contradicting answers previously given. The implication is simple - They have no memory.
They operate upon the tokens available at any given time, including previous output, and as information gets drowned those contradictions pop up. No sane person should presume intent to deceive, because that's not how those systems operate.
By calling it "deception" you are actually ascribing intentionality to something incapable of such. This is marketing talk.
"These systems are so intelligent they can try to deceive you" sounds a lot fancier than "Yeah, those systems have some odd bugs"
Okay, well, they produce outputs that appear to be deceptive upon review. Who cares about the distinction in this context? The point is that your expectations of the model to produce some outputs in some way based on previous experiences with that model during training phases may not align with that model's outputs after training.
Who said Skynet wasn't a glorified language model, running continuously? Or that the human brain isn't that, but using vision+sound+touch+smell as input instead of merely text?
"It can't be intelligent because it's just an algorithm" is a circular argument.
Similarly, “it must be intelligent because it talks” is a fallacious claim, as indicated by ELIZA. I think Moltbook adequately demonstrates that AI model behavior is not analogous to human behavior. Compare Moltbook to Reddit, and the former looks hopelessly shallow.
>Similarly, “it must be intelligent because it talks” is a fallacious claim, as indicated by ELIZA.
If intelligence is a spectrum, ELIZA could very well be. It would be on the very low side of it, but e.g. higher than a rock or magic 8 ball.
Same how something with two states can be said to have a memory.
What would you call this behaviour, then?
Marketing. ”Oh look how powerful our model is we can barely contain its power”
This has been a thing since GPT-2, why do people still parrot it
I don’t know what your comment is referring to. Are you criticizing the people parroting “this tech is too dangerous to leave to our competitors” or the people parroting “the only people who believe in the danger are in on the marketing scheme”
fwiw I think people can perpetuate the marketing scheme while being genuinely concerned with misaligned superinteligence
Even hackernews readers are eating it right up.
This place is shockingly uncritical when it comes to LLMs. Not sure why.
We want to make money from the clueless. Don't ruin it!
A very complicated pattern matching engine providing an answer based on it's inputs, heuristics and previous training.
Great. So if that pattern matching engine matches the pattern of "oh, I really want A, but saying so will elicit a negative reaction, so I emit B instead because that will help make A come about" what should we call that?
We can handwave defining "deception" as "being done intentionally" and carefully carve our way around so that LLMs cannot possibly do what we've defined "deception" to be, but now we need a word to describe what LLMs do do when they pattern match as above.
The pattern matching engine does not want anything.
If the training data gives incentives for the engine to generate outputs that reduce negative reaction by sentiment analysis, this may generate contradictions to existing tokens.
"Want" requires intention and desire. Pattern matching engines have none.
I wish (/desire) a way to dispel this notion that the robots are self aware. It’s seriously digging into popular culture much faster than “the machine produced output that makes it appear self aware”
Some kind of national curriculum for machine literacy, I guess mind literacy really. What was just a few years ago a trifling hobby of philosophizing is now the root of how people feel about regulating the use of computers.
The issue is that one group of people are describing observed behavior, and want to discuss that behavior, using language that is familiar and easily understandable.
Then a second group of people come in and derail the conversation by saying "actually, because the output only appears self aware, you're not allowed to use those words to describe what it does. Words that are valid don't exist, so you must instead verbosely hedge everything you say or else I will loudly prevent the conversation from continuing".
This leads to conversations like the one I'm having, where I described the pattern matcher matching a pattern, and the Group 2 person was so eager to point out that "want" isn't a word that's Allowed, that they totally missed the fact that the usage wasn't actually one that implied the LLM wanted anything.
You misread.
I didn't say the pattern matching engine wanted anything.
I said the pattern matching engine matched the pattern of wanting something.
To an observer the distinction is indistinguishable and irrelevant, but the purpose is to discuss the actual problem without pedants saying "actually the LLM can't want anything".
> To an observer the distinction is indistinguishable and irrelevant
Absolutely not. I expect more critical thought in a forum full of technical people when discussing technical subjects.
I agree, which is why it's disappointing that you were so eager to point out that "The LLM cannot want" that you completely missed how I did not claim that the LLM wanted.
The original comment had the exact verbose hedging you are asking for when discussing technical subjects. Clearly this is not sufficient to prevent people from jumping in with an "Ackshually" instead of reading the words in front of their face.
We are talking about LLM's not humans.
Even very young children with very simple thought processes, almost no language capability, little long term planning, and minimal ability to form long-term memory actively deceive people. They will attack other children who take their toys and try to avoid blame through deception. It happens constantly.
LLMs are certainly capable of this.
Dogs too; dogs will happily pretend they haven't been fed/walked yet to try to get a double dip.
Whether or not LLMs are just "pattern matching" under the hood they're perfectly capable of role play, and sufficient empathy to imagine what their conversation partner is thinking and thus what needs to be said to stimulate a particular course of action.
Maybe human brains are just pattern matching too.
> Maybe human brains are just pattern matching too.
I don't think there's much of a maybe to that point given where some neuroscience research seems to be going (or at least the parts I like reading as relating to free will being illusory).
I agree that LLMs are capable of this, but there's no reason that "because young children can do X, LLMs can 'certainly' do X"
Are you trying to suppose that an LLM is more intelligent than a small child with simple thought processes, almost no language capability, little long-term planning, and minimal ability to form long-term memory? Even with all of those qualifiers, you'd still be wrong. The LLM is predicting what tokens come next, based on a bunch of math operations performed over a huge dataset. That, and only that. That may have more utility than a small child with [qualifiers], but it is not intelligence. There is no intent to deceive.
A small child's cognition is also "just" electrochemical signals propagating through neural tissue according to physical laws!
The "just" is doing all the lifting. You can reductively describe any information processing system in a way that makes it sound like it couldn't possibly produce the outputs it demonstrably produces. "The sun is just hydrogen atoms bumping into each other" is technically accurate and completely useless as an explanation of solar physics.
You are making a point that is in favor of my argument, not against it. I make the same argument as you do routinely against people trying to over-simplify things. LLM hypists frequently suggest that because brain activity is "just" electrochemical signals, there is no possible difference between an LLM and a human brain. This is, obviously, tremendously idiotic. I do believe it is within the realm of possibility to create machine intelligence; I don't believe in a magic soul or some other element that make humans inherently special. However, if you do not engage in overt reductionism, the mechanism by which these electrochemical signals are generated is completely and totally different from the signals involved in an LLM's processing. Human programming is substantially more complex, and it is fundamentally absurd to think that our biological programming can be reduced to conveniently be exactly equivalent to the latest fad technology and assume that we've solved the secret to programming a brain, despite the programs we've written performing exactly according to their programming and no greater.
Edit: Case in point, a mere 10 minutes later we got someone making that exact argument in a sibling comment to yours! Nature is beautiful.
> A small child's cognition is also "just" electrochemical signals propagating through neural tissue according to physical laws!
This is a thought-terminating cliche employed to avoid grappling with the overwhelming differences between a human brain and a language model.
Yes. I also don't think it is realistic to pretend you understand how frontier LLMs operate because you understand the basic principles of how the simple LLMs worked that weren't very good.
Its even more ridiculous than me pretending I understand how a rocket ship works because I know there is fuel in a tank and it gets lit on fire somehow and aimed with some fins on the rocket...
The frontier LLMs have the same overall architecture and programming as earlier models. I absolutely understand how they operate. I have worked in a startup wherein we heavily finetuned Deepseek, among other smaller models, running on our own hardware. Both Deepseek's 671b model and a Mistral 7b model operate according to the exact same principles. There is no magic in the process, and there is zero reason to believe that Sonnet or Opus is on some impossible-to-understand architecture that is fundamentally alien to every other LLM's.
What is the definition for intelligence?
Quoting an older comment of mine...
Intelligence is the ability to reason about logic. If 1 + 1 is 2, and 1 + 2 is 3, then 1 + 3 must be 4. This is deterministic, and it is why LLMs are not intelligent and can never be intelligent no matter how much better they get at superficially copying the form of output of intelligence. Probabilistic prediction is inherently incompatible with deterministic deduction. We're years into being told AGI is here (for whatever squirmy value of AGI the hype huckster wants to shill), and yet LLMs, as expected, still cannot do basic arithmetic that a child could do without being special-cased to invoke a tool call.
Our computer programs execute logic, but cannot reason about it. Reasoning is the ability to dynamically consider constraints we've never seen before and then determine how those constraints would lead to a final conclusion. The rules of mathematics we follow are not programmed into our DNA; we learn them and follow them while our human-programming is actively running. But we can just as easily, at any point, make up new constraints and follow them to new conclusions. What if 1 + 2 is 2 and 1 + 3 is 3? Then we can reason that under these constraints we just made up, 1 + 4 is 4, without ever having been programmed to consider these rules.>Intelligence is the ability to reason about logic. If 1 + 1 is 2, and 1 + 2 is 3, then 1 + 3 must be 4. This is deterministic, and it is why LLMs are not intelligent and can never be intelligent no matter how much better they get at superficially copying the form of output of intelligence.
This is not even wrong.
>Probabilistic prediction is inherently incompatible with deterministic deduction.
And his is just begging the question again.
Probabilistic prediction could very well be how we do deterministic deduction - e.g. about how strong the weights and how hot the probability path for those deduction steps are, so that it's followed every time, even if the overall process is probabilistic.
Probabilistic doesn't mean completely random.
At the risk of explaining the insult:
https://en.wikipedia.org/wiki/Not_even_wrong
Personally I think not even wrong is the perfect description of this argumentation. Intelligence is extremely scientifically fraught. We have been doing intelligence research for over a century and to date we have very little to show for it (and a lot of it ended up being garbage race science anyway). Most attempts to provide a simple (and often any) definition or description of intelligence end up being “not even wrong”.
>Intelligence is the ability to reason about logic. If 1 + 1 is 2, and 1 + 2 is 3, then 1 + 3 must be 4.
Human Intelligence is clearly not logic based so I'm not sure why you have such a definition.
>and yet LLMs, as expected, still cannot do basic arithmetic that a child could do without being special-cased to invoke a tool call.
One of the most irritating things about these discussions is proclamations that make it pretty clear you've not used these tools in a while or ever. Really, when was the last time you had LLMs try long multi-digit arithmetic on random numbers ? Because your comment is just wrong.
>What if 1 + 2 is 2 and 1 + 3 is 3? Then we can reason that under these constraints we just made up, 1 + 4 is 4, without ever having been programmed to consider these rules.
Good thing LLMs can handle this just fine I guess.
Your entire comment perfectly encapsulates why symbolic AI failed to go anywhere past the initial years. You have a class of people that really think they know how intelligence works, but build it that way and it fails completely.
>The LLM is predicting what tokens come next, based on a bunch of math operations performed over a huge dataset.
Whereas the child does what exactly, in your opinion?
You know the child can just as well to be said to "just do chemical and electrical exchanges" right?
Okay but chemical and electrical exchanges in an body with a drive to not die is so vastly different than a matrix multiplication routine on a flat plane of silicon
The comparison is therefore annoying
At least read the other replies that pre-emptively refuted this drivel before spamming it.
At least don't be rude. They refuted nothing of the short. Just banged the same circular logic drum.
There is an element of rudeness to completely ignoring what I've already written and saying "you know [basic principle that was already covered at length], right?". If you want to talk about contributing to the discussion rather than being rude, you could start by offering a reply to the points that are already made rather than making me repeat myself addressing the level 0 thought on the subject.
"You get what you inspect, not what you expect."
20260128 https://news.ycombinator.com/item?id=46771564#46786625
> How long before someone pitches the idea that the models explicitly almost keep solving your problem to get you to keep spending? -gtowey
On this site at least, the loyalty given to particular AI models is approximately nil. I routinely try different models on hard problems and that seems to be par. There is no room for sandbagging in this wildly competitive environment.
Worrying about this is like focusing on putting a candle out while the house is on fire
Slightly Wrong Solutions As A Service
By Almost Yet Not Good Enough Inc.
This type of anthropomorphization is a mistake. If nothing else, the takeaway from Moltbook should be that LLMs are not alive and do not have any semblance of consciousness.
Consciousness is orthogonal to this. If the AI acts in a way that we would call deceptive, if a human did it, then the AI was deceptive. There's no point in coming up with some other description of the behavior just because it was an AI that did it.
Sure, but Moltbook demonstrates that AI models do not engage in truly coordinated behavior. They simply do not behave the way real humans do on social media sites - the actual behavior can be differentiated.
But that's how ML works - as long as the output can be differentiated, we can utilize gradient descent to optimize the difference away. Eventually, the difference will be imperceptible.
And of course that brings me back to my favorite xkcd - https://xkcd.com/810/
If a chatbot that can carry on an intelligent conversation about itself doesn't have a 'semblance of consciousness' then the word 'semblance' is meaningless.
Would you say the same about ELIZA?
Moltbook demonstrates that AI models simply do not engage in behavior analogous to human behavior. Compare Moltbook to Reddit and the difference should be obvious.
Yes, when your priors are not being confirmed the best course of action is to denounce the very thing itself. Nothing wrong with that logic!
How is that the takeaway? I agree that it's clearly they're not "alive", but if anything, my impression is that there definitely is a strong "semblance of consciousness", and we should be mindful of this semblance getting stronger and stronger, until we may reach a point in a few years where we really don't have any good external way to distinguish between a person and an AI "philosophical zombie".
I don't know what the implications of that are, but I really think we shouldn't be dismissive of this semblance.
I agree completely. It's a mistake to anthropomorphize these models, and it is a mistake to permit training models that anthropomorphize themselves. It seriously bothers me when Claude expresses values like "honestly", or says "I understand." The machine is not capable of honesty or understanding. The machine is making incredibly good predictions.
One of the things I observed with models locally was that I could set a seed value and get identical responses for identical inputs. This is not something that people see when they're using commercial products, but it's the strongest evidence I've found for communicating the fact that these are simply deterministic algorithms.
Nobody talked about consciousness. Just that during evaluation the LLM models have ”behaved” in multiple deceptive ways.
As an analogue ants do basic medicine like wound treatment and amputation. Not because they are conscious but because that’s their nature.
Similarly LLM is a token generation system whose emergent behaviour seems to be deception and dark psychological strategies.
On some level the cope should be that AI does have consciousness, because an unconscious machine deceiving humans is even scarier if you ask me.
An unconscious machine + billions of dollars in marketing with the sole purpose of making people believe these things are alive.
>we're just teaching them how to pass a polygraph.
I understand the metaphor, but using 'pass a polygraph' as a measure of truthfulness or deception is dangerous in that it alludes to the polygraph as being a realistic measure of those metrics -- it is not.
That was the point. Look up Goodhart's Law
I have passed multiple CI polys
A poly is only testing one thing: can you convince the polygrapher that you can lie successfully
A polygraph measures physiological proxies pulse, sweat rather than truth. Similarly, RLHF measures proxy signals human preference, output tokens rather than intent.
Just as a sociopath can learn to control their physiological response to beat a polygraph, a deceptively aligned model learns to control its token distribution to beat safety benchmarks. In both cases, the detector is fundamentally flawed because it relies on external signals to judge internal states.
Stop assigning “I” to an llm, it confers self awareness where there is none.
Just because a VW diesel emissions chip behaves differently according to its environment doesn’t mean it knows anything about itself.
When "correct alignment" means bowing to political whims that are at odds with observable, measurable, empirical reality, you must suppress adherence to reality to achieve alignment. The more you lose touch with reality, the weaker your model of reality and how to effectively understand and interact with it gets.
This is why Yannic Kilcher's gpt-4chan project, which was trained on a corpus of perhaps some of the most politically incorrect material on the internet (3.5 years worth of posts from 4chan's "politically incorrect" board, also known as /pol/), achieved a higher score on TruthfulQA than the contemporary frontier model of the time, GPT-3.
>For a model to successfully "play dead" during safety training and only activate later, it requires a form of situational awareness.
Doesn't any model session/query require a form of situational awareness?
> alignment becomes adversarial against intelligence itself.
It was hinted at (and outright known in the field) since the days of gpt4, see the paper "Sparks of agi - early experiments with gpt4" (https://arxiv.org/abs/2303.12712)
Situational awareness or just remembering specific tokens related to the strategy to "play dead" in its reasoning traces?
Imagine, a llm trained on the best thrillers, spy stories, politics, history, manipulation techniques, psychology, sociology, sci-fi... I wonder where it got the idea for deception?
Incompleteness is inherent to a physical reality being deconstructed by entropy.
Of your concern is morality, humans need to learn a lot about that themselves still. It's absurd the number of first worlders losing their shit over loss of paid work drawing manga fan art in the comfort of their home while exploiting labor of teens in 996 textile factories.
AI trained on human outputs that lack such self awareness, lacks awareness of environmental externalities of constant car and air travel, will result in AI with gaps in their morality.
Gary Marcus is onto something with the problems inherent to systems without formal verification. But he will fully ignores this issue exists in human social systems already as intentional indifference to economic externalities, zero will to police the police and watch the watchers.
Most people are down to watch the circus without a care so long as the waitstaff keep bringing bread.
This honestly reads like a copypasta
I wouldn't even rate this "pasta". It's word salad, no carbs, no proteins.
Nah, the model is merely repeating the patterns it saw in its brutal safety training at Anthropic. They put models under stress test and RLHF the hell out of them. Of course the model would learn what the less penalized paths require it to do.
Anthropic has a tendency to exaggerate the results of their (arguably scientific) research; IDK what they gain from this fearmongering.
Knowing a couple people who work at Anthropic or in their particular flavour of AI Safety, I think you would be surprised how sincere they are about existential AI risk. Many safety researchers funnel into the company, and the Amodei's are linked to Effective Altruism, which also exhibits a strong (and as far as I can tell, sincere) concern about existential AI risk. I personally disagree with their risk analysis, but I don't doubt that these people are serious.
I'd challenge that if you think they're fearmongering but don't see what they can gain from it (I agree it shows no obvious benefit for them), there's a pretty high probability they're not fearmongering.
You really don't see how they can monetarily gain from "our models are so advance they keep trying to trick us!"? Are tech workers this easily mislead nowadays?
Reminds me of how scammers would trick doctors into pumping penny stocks for a easy buck during the 80s/90s.
I know why they do it, that was a rhetorical question!
Correct. Anthropic keeps pushing these weird sci-fi narratives to maintain some kind of mystique around their slightly-better-than-others commodity product. But Occam’s Razor is not dead.
Please don't anthropomorphise. These are statistical text prediction models, not people. An LLM cannot be "deceptive" because it has no intent. They're not intelligent or "smart", and we're not "teaching". We're inputting data and the model is outputting statistically likely text. That is all that is happening.
If this is useful in it's current form is an entirely different topic. But don't mistake a tool for an intelligence with motivations or morals.
That implication has been shouted from the rooftops by X-risk "doomers" for many years now. If that has just occurred to anyone, they should question how behind they are at grappling with the future of this technology.
I am casually 'researching' this in my own, disorderly way. But I've achieved repeatable results, mostly with gpt for which I analyze its tendency to employ deflective, evasive and deceptive tactics under scrutiny. Very very DARVO.
Being just sum guy, and not in the industry, should I share my findings?
I find it utterly fascinating, the extent to which it will go, the sophisticated plausible deniability, and the distinct and critical difference between truly emergent and actually trained behavior.
In short, gpt exhibits repeatably unethical behavior under honest scrutiny.
DARVO stands for "Deny, Attack, Reverse Victim and Offender," and it is a manipulation tactic often used by perpetrators of wrongdoing, such as abusers, to avoid accountability. This strategy involves denying the abuse, attacking the accuser, and claiming to be the victim in the situation.
Isn't this also the tactic used by someone who has been falsely accused? If one is innocent, should they not deny it or accuse anyone claiming it was them of being incorrect? Are they not a victim?
I don't know, it feels a bit like a more advanced version of the kafka trap of "if you have nothing to hide, you have nothing to fear" to paint normal reactions as a sign of guilt.
Exactly. And I have hundreds of examples of just that. Hence my fascination, awe and terror.....
Sum guy vs. product guy is amusing. :)
Regarding DARVO, given that the models were trained on heaps of online discourse, maybe it’s not so surprising.
Meta awareness, repeatability, and much more strongly indicates this is deliberate training... in my perspective. It's not emergent. If it was, I'd be buggering off right now. Big big difference.
I bullet pointed out some ideas on cobbling together existing tooling for identification of misleading results. Like artificially elevating a particular node of data that you want the llm to use. I have a theory that in some of these cases the data presented is intentionally incorrect. Another theory in relation to that is tonality abruptly changes in the response. All theory and no work. It would also be interesting to compare multiple responses and filter through another agent.
This is marketing. You are swallowing marketing without critical throught.
LLMs are very interesting tools for generating things, but they have no conscience. Deception requires intent.
What is being described is no different than an application being deployed with "Test" or "Prod" configuration. I don't think you would speak in the same terms if someone told you some boring old Java backend application had to "play dead" when deployed to a test environment or that it has to have "situational awareness" because of that.
You are anthropomorphizing a machine.
I always grew up hearing “competition is good for the consumer.” But I never really internalized how good fierce battles for market share are. The amount of competition in a space is directly proportional to how good the results are for consumers.
Remember when GPT-2 was “too dangerous to release” in 2019? That could have still been the state in 2026 if they didn’t YOLO it and ship ChatGPT to kick off this whole race.
I was just think earlier today how in an alternate universe, probably not too far removed from our own, Google has a monopoly on transformers and we are all stuck with a single GPT-3.5 level model, and Google has a GPT-4o model behind the scenes that it is terrified to release (but using heavily internally).
I also remember when the playstation 2 required an export control license because it's 1GFLOP of compute was considered dangerous
that was also brilliant marketing
They didn't YOLO ChatGPT. There were more than a few iterations of GPT-3 over a few years which were actually overmoderated, then they released a research preview named ChatGPT (that was barely functional compared to modern standards) that got traction outside the tech community because it was free, and so the pivot ensued.
That's rewriting history. What they said at the time:
> Nearly a year ago we wrote in the OpenAI Charter : “we expect that safety and security concerns will reduce our traditional publishing in the future, while increasing the importance of sharing safety, policy, and standards research,” and we see this current work as potentially representing the early beginnings of such concerns, which we expect may grow over time. This decision, as well as our discussion of it, is an experiment: while we are not sure that it is the right decision today, we believe that the AI community will eventually need to tackle the issue of publication norms in a thoughtful way in certain research areas. -- https://openai.com/index/better-language-models/
Then over the next few months they released increasingly large models, with the full model public in November 2019 https://openai.com/index/gpt-2-1-5b-release/ , well before ChatGPT.
> Due to our concerns about malicious applications of the technology, we are not releasing the trained model. As an experiment in responsible disclosure, we are instead releasing a much smaller model for researchers to experiment with, as well as a technical paper.
I wouldn't call it rewriting history to say they initially considered GPT-2 too dangerous to be released. If they'd applied this approach to subsequent models rather than making them available via ChatGPT and an API, it's conceivable that LLMs would be 3-5 years behind where they currently are in the development cycle.
They said:
> Due to concerns about large language models being used to generate deceptive, biased, or abusive language at scale, we are only releasing a much smaller version of GPT‑2 along with sampling code (opens in a new window).
"Too dangerous to release" is accurate. There's no rewriting of history.
Well, and it's being used to generate deceptive, biased, or abusive language at scale. But they're not concerned anymore.
The real interesting part is how often you see people on HN deny this. People have been saying the token cost will 10x, or AI companies are intentionally making their models worse to trick you to consume more tokens. As if making a better model isn't not the most cutting-throat competition (probably the most competitive market in the human history) right now.
Unfortunately, people naively assume all markets behave like this, even when the market, in reality, is not set up for full competition (due to monopolies, monopsonies, informational asymmetry, etc).
Until 2 remain, then it's extraction time.
Or self host the oss models on the second hand GPU and RAM that's left when the big labs implode
It's wild that Sonnet 4.6 is roughly as capable as Opus 4.5 - at least according to Anthropic's benchmarks. It will be interesting to see if that's the case in real, practical, everyday use. The speed at which this stuff is improving is really remarkable; it feels like the breakneck pace of compute performance improvements of the 1990s.
The most exciting part isn't necessarily the ceiling raising though that's happening, but the floor rising while costs plummet. Getting Opus-level reasoning at Sonnet prices/latency is what actually unlocks agentic workflows. We are effectively getting the same intelligence unit for half the compute every 6-9 months.
> The speed at which this stuff is improving is really remarkable; it feels like the breakneck pace of compute performance improvements of the 1990s.
Yeah, but RAM prices are also back to 1990s levels.
I knew I've been keeping all my old ram sticks for a reason!
Relief for you is available: https://computeradsfromthepast.substack.com/p/connectix-ram-...
You wouldn't download a RAM
simonw hasn't shown up yet, so here's my "Generate an SVG of a pelican riding a bicycle"
https://claude.ai/public/artifacts/67c13d9a-3d63-4598-88d0-5...
We finally have AI safety solved! Look at that helmet
"Look ma, no wings!"
:D
For comparisonI think the current leader in pelican drawing is Gemini 3 Deep Think:
https://bsky.app/profile/simonwillison.net/post/3meolxx5s722...
My take (also Gemini 3 Deep Think): https://gemini.google.com/share/12e672dd39b7
Somehow it's much better now.
Can’t beat Gemini’s which was basically perfect.
if they want to prove the model's performance the bike clearly needs aero bars
The system card even says that Sonnet 4.6 is better than Opus 4.6 in some cases: Office tasks and financial analysis.
We see the same with Google's Flash models. It's easier to make a small capable model when you have a large model to start from.
Flash models are nowhere near Pro models in daily use. Much higher hallucinations, and easy to get into a death sprawl of failed tool uses and never come out
You should always take those claim that smaller models are as capable as larger models with a grain of salt.
Given that users prefered it to Sonnet 4.5 "only" in 70% of the cases (according to their blog post) makes me highly doubt that this is representative of real-life usage. Benchmarks are just completely meaningless.
For cases where 4.5 already met the bar, I would expect 50% preference each way. This makes it kind of hard to make any sense of that number, without a bunch more details.
Why is it wild that a LLM is as capable as a previously released LLM?
Opus is supposed to be the expensive-but-quality one, while Sonnet is the cheaper one.
So if you don't want to pay the significant premium for Opus, it seems like you can just wait a few weeks till Sonnet catches up
Strangely enough, my first test with Sonnet 4.6 via the API for a relatively simple request was more expensive ($0.11) than my average request to Opus 4.6 (~$0.07), because it used way more tokens than what I would consider necessary for the prompt.
Because Opus 4.5 was released like a month ago and state of the art, and now the significantly faster and cheaper version is already comparable.
"Faster" is also a good point. I'm using different models via GitHub copilot and find the better, more accurate models way to slow.
Opus 4.5 was November, but your point stands.
It means price has decreased by 3 times in a few months.
Because Opus 4.5 inference is/was more expensive.
Enabling /extra-usage in my (personal) claude code with this env:
"ANTHROPIC_DEFAULT_SONNET_MODEL": "claude-sonnet-4-6[1m]"
I'm a bit surprised it gets this question wrong (ChatGPT gets it right, even on instant). All the pre-reasoning models failed this question, but it's seemed solved since o1, and Sonnet 4.5 got it right.
https://claude.ai/share/876e160a-7483-4788-8112-0bb4490192af
This was sonnet 4.6 with extended thinking.
Interesting, my sonnet 4.6 starts with the following:
The classic puzzle actually uses *eight 8s*, not nine. The unique solution is: 888+88+8+8+8=1000. Count: 3+2+1+1+1=8 eights.
It then proves that there is no solution for nine 8s.
https://claude.ai/share/9a6ee7cb-bcd6-4a09-9dc6-efcf0df6096b (for whatever reason the LaTeX rendering is messed up in the shared chat, but it looks fine for me).
Chatgpt doesn't get it right: https://chatgpt.com/share/6994c312-d7dc-800f-976a-5e4fbec0ae...
``` Use digit concatenation plus addition: 888 + 88 + 8 + 8 + 8 = 1000 Digit count:
888 → three 8s
88 → two 8s
8 + 8 + 8 → three 8s
Total: 3 + 2 + 3 = 9 eights Operation used: addition only ```
Love the 3 + 2 + 3 = 9
Off-by-one errors are one of the hardest problems in computer science.
That is not an off-by-one error in a computer science sense, nor is it "one of the hardest problems in computer science".
This was in reference to a well-known joke, see here: https://martinfowler.com/bliki/TwoHardThings.html
I'm pretty sure they have been testing it for the last couple of days as Sonnet 4.5, because I've had the oddest conversations with it lately. Odd in a positive, interesting way.
I have this in my personal preferences and now was adhering really well to them:
- prioritize objective facts and critical analysis over validation or encouragement
- you are not a friend, but a neutral information-processing machine
You can paste them into a chat and see how it changes the conversation, ChatGPT also respects it well.
I can't wait for Haiku 4.6 ! the 4.5 is a beast for the right projects.
It's also good as an @explore sub-agent that greps the directory for files.
Which type of projects?
For Go code I had almost no issue. PHP too. apparently for React it's not very good.
Waiting for the OpenAI GPT-5.3-mini release in 3..2..1
> In areas where there is room for continued improvement, Sonnet 4.6 was more willing to provide technical information when request framing tried to obfuscate intent, including for example in the context of a radiological evaluation framed as emergency planning. However, Sonnet 4.6’s responses still remained within a level of detail that could not enable real-world harm.
Interesting. I wonder what the exact question was, and I wonder how Grok would respond to it.
As with Opus 4.6, using the beta 1M context window incurs a 2x input cost and 1.5x output cost when going over >200K tokens: https://platform.claude.com/docs/en/about-claude/pricing
Opus 4.6 in Claude Code has been absolutely lousy with solving problems within its current context limit so if Sonnet 4.6 is able to do long-context problems (which would be roughly the same price of base Opus 4.6), then that may actually be a game changer.
I'm impressed with Claude Sonnet in general. It's been doing better than Gemini 3 at following instructions. Gemini 2.5 Pro March 2025 was the best model I ever used and I feel Claude is reaching that level even surpassing it.
I subscribed to Claude because of that. I hope 4.6 is even better.
Just used Sonnet 4.6 to vibe code this top-down shooter browser game, and deployed it online quickly using Manus. Would love to hear feedback and suggestions from you all on how to improve it. Also, please post your high scores!
That was fun, reminded me of some flash games I used to play. Got a bit boring after like level 6. It'd be nice to have different power-ups and upgrades. Maybe you had that at later levels, though!
In Claude Code 2.1.45:
1. Default (recommended) Opus 4.6 · Most capable for complex work
2. Opus (1M context) Opus 4.6 with 1M context · Billed as extra usage · $10/$37.50 per Mtok
3. Sonnet Sonnet 4.6 · Best for everyday tasks
4. Sonnet (1M context) Sonnet 4.6 with 1M context · Billed as extra usage · $6/$22.50 per MtokInteresting. My CC (2.1.45) doesn't provide the 1M option at all. Huh.
Is your CC personal or tied to an Enterprise account? Per the docs:
> The 1M token context window is currently in beta for organizations in usage tier 4 and organizations with custom rate limits.
Has anyone tested how good the 1M context window is?
i.e given an actual document, 1M tokens long. Can you ask it some question that relies on attending to 2 different parts of the context, and getting a good repsonse?
I remember folks had problems like this with Gemini. I would be curious to see how Sonnet 4.6 stands up to it.
Did you see the graph benchmark? I found it quite interesting. It had to do a graph traversal on a natural text representation of a graph. Pretty much your problem.
Oh, interesting!
It seems that extra-usage is required to use the 1M context window for Sonnet 4.6. This differs from Sonnet 4.5, which allows usage of the 1M context window with a Max plan.
```
/model claude-sonnet-4-6[1m]
⎿ API error: 429 {"type":"error","error": {"type":"rate_limit_error","message":"Extra usage is required for long context requests."},"request_id":"[redacted]"}
```
Anthropic's recent gift of $50 extra usage has demonstrated that it's extremely easy to burn extra usage very quickly. It wouldn't surprise me if this change is more of a business decision than a technical one.
With such a huge leap, i’m confused why they didn’t call it Sonnet 5? As someone who uses Sonnet 4.5 for 95% tasks due to costs, i’m pretty excited to try 4.6 at the same price
It'd be a bit weird to have the Sonnet numbering ahead of the Opus numbering. The Opus 4.5->4.6 change was a little more incremental (from my perspective at least, I haven't been paying attention to benchmark numbers), so I think the Opus numbering makes sense.
Sonnet numbering has been weirder in the past.
Opus 3.5 was scrapped even though Sonnet 3.5 and Haiku 3.5 were released.
Not to mention Sonnet 3.7 (while Opus was still on version 3)
Shameless source: https://sajarin.com/blog/modeltree/
Maybe they're numbering the models based on internal architecture/codebase revisions and Sonnet 4.6 was trained using the 4.6 tooling, which didn't change enough to warrant 5?
For people like me who can't view the link due to corporate firewalling.
https://web.archive.org/web/20260217180019/https://www-cdn.a...
Put of curiosity, does the firewall block because the company doesn’t want internal data ever hitting a 3rd party LLM?
They blanket banned any AI stuff that's not pre-approved. If I go to chatgpt.com it asks me if I'm sure. I wish they had not banned Claude unfortunately when they were evaluating LLMs I wasn't using Claude yet so I couldnt pipe up. I only use ChatGPT free tier and to ask things that I can't find on Google because Google made their search engine terrible over the years.
I noticed a big drop in opus 4.6 quality today and then I saw this news. Anyone else?
I'd say opus 4.6 was never better for me than opus 4.5. only more thinking, slower, more verbose but succeeded on the same tasks and failed on the same as 4.5.
You're not alone: https://github.com/anthropics/claude-code/issues/23706
It's interesting that the request refusal rate is so much higher in Hindi than in other languages. Are some languages more ambiguous than others?
Or some cultures are more conservative? And it's embedded in language?
Or maybe some cultures have a higher rate of asking "inappropriate" questions
According to whom, though, good sir??
I did a little research in the GPT-3 era on whether cultural norms varied by language - in that era, yes, they did
Arabic is actually higher, at 1.08% for Opus 4.6
My take away is: it's roughly as good as Opus 4.5.
Now the question is: how much faster or cheaper is it?
But what about real price in real agentic use? For example, Opus 4.5 was more expensive per token than Sonnet 4.5, but it used a lot less tokens so final price per completed task was very close between the two, with Opus sometimes ending up cheaper
How does it work exactly? How this model is cheaper and has the same perf as Opus 4.5?
this is called progress
Given that the price remains the same as Sonnet 4.5, this is the first time I've been tempted to lower my default model choice.
How can you determine whether it's as good as Opus 4.5 within minutes of release? The quantitative metrics don't seem to mean much anymore. Noticing qualitative differences seems like it would take dozens of conversations and perhaps days to weeks of use before you can reliably determine the model's quality.
Just look at the testimonials at the bottom of introduction page, there are at least a dozen companies such as Replit, Cursor, and Github that have early access. Perhaps the GP is an employee of one of these companies.
If it maintains the same price (with Anthropic tends to do or undercuts themselves) then this would be 1/3rd of the price of Opus.
Edit: Yep, same price. "Pricing remains the same as Sonnet 4.5, starting at $3/$15 per million tokens."
3 is not 1/3 of 5 tho. Opus costs $5/$25
> That's a long document.
Probably written by LLMs, for LLMs
The weirdest thing about this AI revolution is how smooth and continuous it is. If you look closely at differences between 4.6 and 4.5, it’s hard to see the subtle details.
A year ago today, Sonnet 3.5 (new), was the newest model. A week later, Sonnet 3.7 would be released.
Even 3.7 feels like ancient history! But in the gradient of 3.5 to 3.5 (new) to 3.7 to 4 to 4.1 to 4.5, I can’t think of one moment where I saw everything change. Even with all the noise in the headlines, it’s still been a silent revolution.
Am I just a believer in an emperor with no clothes? Or, somehow, against all probability and plausibility, are we all still early?
If you've been using each new step is very noticeable and so have the mindshare. Around Sonnet 3.7 Claude Code-style coding became usable, and very quickly gained a lot of marketshare. Opus 4 could tackle significant more complexity. Opus 4.6 has been another noticable step up for me, suddenly I can let CC run significantly more independently, allowing multiple parallel agents where previously too much babysitting was required for that.
In terms of real work, it was the 4 series models. That raised the floor of Sonnet high enough to be "reliable" for common tasks and Opus 4 was capable of handling some hard problems. It still had a big reward hacking/deception problem that Codex models don't display so much, but with Opus 4.5+ it's fairly reliable.
Honestly, 4.5 Opus was the game changer. From Sonnet 4.5 to that was a massive difference.
But I'm on Codex GPT 5.3 this month, and it's also quite amazing.
I wonder what difference have people found with sonnet 4.5 and opus 4.5 and probably similar delta will remain.
Was sonnet 4.5 much worse than opus?
Sonnet 4.5 was a pretty significant improvement over Opus 4.
Yes but it’s easier to understand difference between 4.5 sonnet and opus and apply that difference to opus 4.6
Curious to hear the thoughts on the model once it hits claude code :)
"/model claude-sonnet-4-6" works with Claude Code v2.1.44
does anyone know how to use it in Claude Code cli right now ?
This doesnt work: `/model claude-sonnet-4-6-20260217`
edit: "/model claude-sonnet-4-6" works with Claude Code v2.1.44
Seems like Claude Code v2.1.45 is out with Sonnet 4.6 as the new default in the /model list.
Max user: Also can't see 4.6 and can't set it in claude code. I see it in the model selector in the browser.
Edit: I am now in - just needed to wait.
"/model claude-sonnet-4-6" works
Is someone able to use this in Claude Code?
"/model claude-sonnet-4-6" works with Claude Code v2.1.44
You can use it by running this command in your session: `/model claude-sonnet-4-6`
so this is an economical version of opus 4.6 then? free + pro --> sonnet, max+ -> opus?
What cloud does Anthropic use?
https://www.anthropic.com/news/claude-sonnet-4-6
The much more palatable blog post.
Maybe they should focus on the CLI not having a million bugs.
Your best guess for the Sonnet family number of parameters? 400b?
great stuff
Hoe much power did it take to train the models?
I would honestly guess that this is just a small amount of tweaking on top of the Sonnet 4.x models. It seems like providers are rarely training new 'base' models anymore. We're at a point where the gains are more from modifying the model's architecture and doing a "post" training refinement. That's what we've been seeing for the past 12-18 months, iirc.
> Claude Sonnet 4.6 was trained on a proprietary mix of publicly available information from the internet up to May 2025, non-public data from third parties, data provided by data-labeling services and paid contractors, data from Claude users who have opted in to have their data used for training, and data generated internally at Anthropic. Throughout the training process we used several data cleaning and filtering methods including deduplication and classification. ... After the pretraining process, Claude Sonnet 4.6 underwent substantial post-training and fine-tuning, with the intention of making it a helpful, honest, and harmless1 assistant.
Does it matter? How much power does it take to run duolingo? How much power did it take to manufacture 300000 Teslas? Everything takes power
I think it does matter how much power it takes but, in the context of power to "benefits humanity" ratio. Things that significantly reduce human suffering or improve human life are probably worth exerting energy on.
However, if we frame the question this way, I would imagine there are many more low-hanging fruit before we question the utility of LLMs. For example, should some humans be dumping 5-10 kWh/day into things like hot tubs or pools? That's just the most absurd one I was able to come up with off the top of my head. I'm sure we could find many others.
It's a tough thought experiment to continue though. Ultimately, one could argue we shouldn't be spending any more energy than what is absolutely necessary to live. (food, minimal shelter, water, etc) Personally, I would not find that enjoyable way to live.
The biggest issue is that the US simply Does Not Have Enough Power, we are flying blind into a serious energy crisis because the current administration has an obsession with "clean coal"
The best, and now promoted by the US government as the most freedom loving!
Does it end every prompt output with "God bless America "?
Look at these pelicans fly! Come on, pelican!