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Assuming this experiment involved isolating the LLM from its training set?

Have you by any chance tried this with GPT 4.1 too (also 1M context)?

Honest question, how do you know if it's pulling from context vs from memory?

If I use Opus 4.6 with Extended Thinking (Web Search disabled, no books attached), it answers with 130 spells.

What is this supposed to show exactly? Those books have been feed into LLMs for years and there's even likely specific RLHF's on extracting spells from HP.

To be fair, I don't think "Slugulus Eructo" (the name) is actually in the books. This is what's in my copy:

> The smug look on Malfoy’s face flickered.

> “No one asked your opinion, you filthy little Mudblood,” he spat.

> Harry knew at once that Malfoy had said something really bad because there was an instant uproar at his words. Flint had to dive in front of Malfoy to stop Fred and George jumping on him, Alicia shrieked, “How dare you!”, and Ron plunged his hand into his robes, pulled out his wand, yelling, “You’ll pay for that one, Malfoy!” and pointed it furiously under Flint’s arm at Malfoy’s face.

> A loud bang echoed around the stadium and a jet of green light shot out of the wrong end of Ron’s wand, hitting him in the stomach and sending him reeling backward onto the grass.

> “Ron! Ron! Are you all right?” squealed Hermione.

> Ron opened his mouth to speak, but no words came out. Instead he gave an almighty belch and several slugs dribbled out of his mouth onto his lap.

you can get the same result just asking opus/gpt, it is probably internalized knowledge from reddit or similar sites.

If you wanted to fit all 7 books, would you use RAG or another solution?

Dumb question. Can these benchmarks be trusted when the model performance tends to vary depending on the hours and load on OpenAI’s servers? How do I know I’m not getting a severe penalty for chatting at the wrong time. Or even, are the models best after launch then slowly eroded away at to more economical settings after the hype wears off?

The lack of broad benchmark reports in this makes me curious: Has OpenAI reverted to benchmaxxing? Looking forward to hearing opinions once we all try both of these out

That's a massive jump, I'm curious if there's a materially different feeling in how it works or if we're starting to reach the point of benchmark saturation. If the benchmark is good then 10 points should be a big improvement in capability...

claude swe-bench is 80.8 and codex is 56.8

Seems like 4.6 is still all-around better?

> Claude now automatically records and recalls memories as it works

Neat: https://code.claude.com/docs/en/memory

I guess it's kind of like Google Antigravity's "Knowledge" artifacts?

Would love to find out they're overfitting for pelican drawings.

One aspect of this is that apparently most people can't draw a bicycle much better than this: they get the elements of the frame wrong, mess up the geometry, etc.

here the animated version https://claude.ai/public/artifacts/3db12520-eaea-4769-82be-7...

If we do get paperclipped, I hope it is of the "cycling pelican" variety. Thanks for your important contribution to alignment Simon!

Do you find that word choices like "generate" (as opposed to "create", "author", "write" etc.) influence the model's success?

Also, is it bad that I almost immediately noticed that both of the pelican's legs are on the same side of the bicycle, but I had to look up an image on Wikipedia to confirm that they shouldn't have long necks?

Also, have you tried iterating prompts on this test to see if you can get more realistic results? (How much does it help to make them look up reference images first?)

They trained for it. That's the +0.1!

Isn't there a point at which it trains itself on these various outputs, or someone somewhere draws one and feeds it into the model so as to pass this benchmark?

This benchmark inspired me to have codex/claude build a DnD battlemap tool with svg's.

They got surprisingly far, but i did need to iterate a few times to have it build tools that would check for things like; dont put walls on roads or water.

What I think might be the next obstacle is self-knowledge. The new agents seem to have picked up ever more vocabulary about their context and compaction, etc.

As a next benchmark you could try having 1 agent and tell it to use a coding agent (via tmux) to build you a pelican.

There's no way they actually work on training this.

Well, the clouds are upside-down, so I don't think I can give it a pass.

This really is my favorite benchmark

I suppose the pelican must be now specifically trained for, since it's a well-known benchmark.

I'm firing all of my developers this afternoon.

best pelican so far would you say? Or where does it rank in the pelican benchmark?

What about the Pelo2 benchmark? (the gray bird that is not gray)

do you have a gif? i need an evolving pelican gif

Pretty sure at this point they train it on pelicans

Can it draw a different bird on a bike?

The ears on top are a cute touch

[flagged]

> the interesting question isn’t “are they subsidizing inference?”

The interesting question is if they are subsidizing the $200/mo plan. That's what is supporting the whole vibecoding/agentic coding thing atm. I don't believe Claude Code would have taken off if it were token-by-token from day 1.

(My baseless bet is that they're, but not by much and the price will eventually rise by perhaps 2x but not 10x.)

I suspect they're marginally profitable on API cost plans.

But the max 20x usage plans I am more skeptical of. When we're getting used to $200 or $400 costs per developer to do aggressive AI-assisted coding, what happens when those costs go up 20x? what is now $5k/yr to keep a Codex and a Claude super busy and do efficient engineering suddenly becomes $100k/yr... will the costs come down before then? Is the current "vibe-coding renaissance" sustainable in that regime?

> It’s very plausible (and increasingly likely) that OpenAI/Anthropic are profitable on a per-token marginal basis

There any many places that will not use models running on hardware provided by OpenAI / Anthropic. That is the case true of my (the Australian) government at all levels. They will only use models running in Australia.

Consequently AWS (and I presume others) will run models supplied by the AI companies for you in their data centres. They won't be doing that at a loss, so the price will cover marginal cost of the compute plus renting the model. I know from devs using and deploying the service demand outstrips supply. Ergo, I don't think there is much doubt that they are making money from inference.

Dario said this in a podcast somewhere. The models themselves have so far been profitable if you look at their lifetime costs and revenue. Annual profitability just isn't a very good lens for AI companies because costs all land in one year and the revenue all comes in the next. Prolific AI haters like Ed Zitron make this mistake all the time.

"how long does a frontier model need to stay competitive"

Remember "worse is better". The model doesn't have to be the best; it just has to be mostly good enough, and used by everyone -- i.e., where switching costs would be higher than any increase in quality. Enterprises would still be on Java if the operating costs of native containers weren't so much cheaper.

So it can make sense to be ok with losing money with each training generation initially, particularly when they are being driven by specific use-cases (like coding). To the extent they are specific, there will be more switching costs.

Claude itself (outside of code workflows) actually works very well for general purpose chat. I have a few non-technical friends that have moved over from chatgpt after some side-by-side testing and I've yet to see one go back - which is good since claude circa 8 months ago was borderline unusable for anything but coding on the api.

I kinda agree. Their model just doesn't feel "daily" enough. I would use it for any "agentic" tasks and for using tools, but definitely not for day to day questions.

Claude sucks at non English languages. Gemini and ChatGPT are much better. Grok is the worst. I am a native Czech speaker and Claude makes up words and Grok sometimes respond in Russian. So while I love it for coding, it’s unusable for general purpose for me.

CC has >6000 open issues, despite their bot auto-culling them after 60 days of inactivity. It was ~5800 when I looked just a few days ago so they seem to be accelerating towards some kind of bug singularity.

It explains how important dogfooding is if you want to make an extremely successful product.

It’s extremely successful, not sure what it explains other than your biases

The sandboxing in CC is an absolute joke, it's no wonder there's an explosion of sandbox wrappers at the moment. There's going to be a security catastrophe at some point, no doubt about it.

Also explains why Claude Code is a React app outputting to a Terminal. (Seriously.)

Ah yes, explains why it takes 3 seconds for a new chat to load after I click new chat in the macOS app.

Can Claude fix the flicker in Claude yet?

The cost per token served has been falling steadily over the past few years across basically all of the providers. OpenAI dropped the price they charged for o3 to 1/5th of what it was in June last year thanks to "engineers optimizing inferencing", and plenty of other providers have found cost savings too.

Turns out there was a lot of low-hanging fruit in terms of inference optimization that hadn't been plucked yet.

> A year or more ago, I read that both Anthropic and OpenAI were losing money on every single request even for their paid subscribers

Where did you hear that? It doesn't match my mental model of how this has played out.

> A year or more ago, I read that both Anthropic and OpenAI were losing money on every single request even for their paid subscribers

This gets repeated everywhere but I don't think it's true.

The company is unprofitable overall, but I don't see any reason to believe that their per-token inference costs are below the marginal cost of computing those tokens.

It is true that the company is unprofitable overall when you account for R&D spend, compensation, training, and everything else. This is a deliberate choice that every heavily funded startup should be making, otherwise you're wasting the investment money. That's precisely what the investment money is for.

However I don't think using their API and paying for tokens has negative value for the company. We can compare to models like DeepSeek where providers can charge a fraction of the price of OpenAI tokens and still be profitable. OpenAI's inference costs are going to be higher, but they're charging such a high premium that it's hard to believe they're losing money on each token sold. I think every token paid for moves them incrementally closer to profitability, not away from it.

> A year or more ago, I read that both Anthropic and OpenAI were losing money on every single request even for their paid subscribers, and I don't know if that has changed with more efficient hardware/software improvements/caching.

This is obviously not true, you can use real data and common sense.

Just look up a similar sized open weights model on openrouter and compare the prices. You'll note the similar sized model is often much cheaper than what anthropic/openai provide.

Example: Let's compare claude 4 models with deepseek. Claude 4 is ~400B params so it's best to compare with something like deepseek V3 which is 680B params.

Even if we compare the cheapest claude model to the most expensive deepseek provider we have claude charging $1/M for input and $5/M for output, while deepseek providers charge $0.4/M and $1.2/M, a fifth of the price, you can get it as cheap as $.27 input $0.4 output.

As you can see, even if we skew things overly in favor of claude, the story is clear, claude token prices are much higher than they could've been. The difference in prices is because anthropic also needs to pay for training costs, while openrouter providers just need to worry on making serving models profitable. Deepseek is also not as capable as claude which also puts down pressure on the prices.

There's still a chance that anthropic/openai models are losing money on inference, if for example they're somehow much larger than expected, the 400B param number is not official, just speculative from how it performs, this is only taking into account API prices, subscriptions and free user will of course skew the real profitability numbers, etc.

Price sources:

https://openrouter.ai/deepseek/deepseek-v3.2-speciale

https://claude.com/pricing#api

I think actually working out whether they are losing money is extremely difficult for current models but you can look backwards. The big uncertainties are:

1) how do you depreciate a new model? What is its useful life? (Only know this once you deprecate it)

2) how do you depreciate your hardware over the period you trained this model? Another big unknown and not known until you finally write the hardware off.

The easy thing to calculate is whether you are making money actually serving the model. And the answer is almost certainly yes they are making money from this perspective, but that’s missing a large part of the cost and is therefore wrong.

Saw a comment earlier today about google seeing a big (50%+) fall in Gemini serving cost per unit across 2025 but can’t find it now. Was either here or on Reddit

It's not just that. Everyone is complacent with the utilization of AI agents. I have been using AI for coding for quite a while, and most of my "wasted" time is correcting its trajectory and guiding it through the thinking process. It's very fast iterations but it can easily go off track. Claude's family are pretty good at doing chained task, but still once the task becomes too big context wise, it's impossible to get back on track. Cost wise, it's cheaper than hiring skilled people, that's for sure.

> i.e. plans/API calls that make this practical at scale are expensive

Local AI's make agent workflows a whole lot more practical. Making the initial investment for a good homelab/on-prem facility will effectively become a no-brainer given the advantages on privacy and reliability, and you don't have to fear rugpulls or VC's playing the "lose money on every request" game since you know exactly how much you're paying in power costs for your overall load.

Gemini-pro-preview is on ollama and requires h100 which is ~$15-30k. Google are charging $3 a million tokens. Supposedly its capable of generating between 1 and 12 million tokens an hour.

Which is profitable. but not by much.

That's why anthropic switched to tpu, you can sell at cost.

These are intro prices.

This is all straight out of the playbook. Get everyone hooked on your product by being cheap and generous.

Raise the price to backpay what you gave away plus cover current expenses and profits.

In no way shape or form should people think these $20/mo plans are going to be the norm. From OpenAI's marketing plan, and a general 5-10 year ROI horizon for AI investment, we should expect AI use to cost $60-80/mo per user.

It’s there. I’m already using it

Thanks for the tip!

Paying $10 per request doesn't have me jumping at the opportunity to try it!

Has a "N million context window" spec ever been meaningful? Very old, very terrible, models "supported" 1M context window, but would lose track after two small paragraphs of context into a conversation (looking at you early Gemini).

Opus 4.5 starts being lazy and stupid at around the 50% context mark in my opinion, which makes me skeptical that this 1M context mode can produce good output. But I'll probably try it out and see

The 1M context is not available via subscription - only via API usage

On Openrouter it has the same cost per token as 4.5

> From the press release at least it sounds more expensive than Opus 4.5 (more tokens per request and fees for going over 200k context).

That's a feature. You could also not use the extra context, and the price would be the same.

Looks like a marketing strategy to bill more for Opus than Sonnet

Is it still getting blocked when you give it a browser?

> it weirdly feels the most transactional out of all of them.

My experience is the opposite, it is the only LLM I find remotely tolerable to have collaborative discussions with like a coworker, whereas ChatGPT by far is the most insufferable twat constantly and loudly asking to get punched in the face.

I love being used as a test subject against my will!

I just ask claude and ask for sources for each one.

When I first started coding with LLMs, I could show a bug to an LLM and it would start to bugfix it, and very quickly would fall down a path of "I've got it! This is it! No wait, the print command here isn't working because an electron beam was pointed at the computer".

Nowadays, I have often seen LLMs (Opus 4.5) give up on their original ideas and assumptions. Sometimes I tell them what I think the problem is, and they look at it, test it out, and decide I was wrong (and I was).

There are still times where they get stuck on an idea, but they are becoming increasingly rare.

Therefore, think that modern LLMs clearly are already able to question their assumptions and notice when framing is wrong. In fact, they've been invaluable to me in fixing complicated bugs in minutes instead of hours because of how much they tend to question many assumptions and throw out hypotheses. They've helped _me_ question some of my assumptions.

They're inconsistent, but they have been doing this. Even to my surprise.

> Or creativity — not recombination of known patterns, but the kind of leap where you redefine the problem space itself.

Have you tried actually prompting this? It works.

They can give you lots of creative options about how to redefine a problem space, with potential pros and cons of different approaches, and then you can further prompt to investigate them more deeply, combine aspects, etc.

So many of the higher-level things people assume LLM's can't do, they can. But they don't do them "by default" because when someone asks for the solution to a particular problem, they're trained to by default just solve the problem the way it's presented. But you can just ask it to behave differently and it will.

If you want it to think critically and question all your assumptions, just ask it to. It will. What it can't do is read your mind about what type of response you're looking for. You have to prompt it. And if you want it to be super creative, you have to explicitly guide it in the creative direction you want.

> These feel like they involve something beyond "predict the next token really well, with a reasoning trace."

I don't think there's anything you can't do by "predicting the next token really well". It's an extremely powerful and extremely general mechanism. Saying there must be "something beyond that" is a bit like saying physical atoms can't be enough to implement thought and there must be something beyond the physical. It underestimates the nearly unlimited power of the paradigm.

Besides, what is the human brain if not a machine that generates "tokens" that the body propagates through nerves to produce physical actions? What else than a sequence of these tokens would a machine have to produce in response to its environment and memory?

You would be surprised about what the 4.5 models can already do in these ways of thinking. I think that one can unlock this power with the right set of prompts. It's impressive, truly. It has already understood so much, we just need to reap the fruits. I'm really looking forward to trying the new version.

New idea generation? Understanding of new/sparse/not-statistically-significant concepts in the context window? I think both being the same problem of not having runtime tuning. When we connect previously disparate concepts, like with a "eureka" moment, (as I experience it) a big ripple of relations form that deepens that understanding, right then. The entire concept of dynamically forming a deeper understanding from something new presented, from "playing out"/testing the ideas in your brain with little logic tests, comparisons, etc, doesn't seem to be possible. The test part does, but the runtime fine tuning, augmentation, or whatever it would be, does not.

In my experience, if you do present something in the context window that is sparse in the training, there's no depth to it at all, only what you tell it. And, it will always creep towards/revert to the nearest statistically significant answers, with claims of understanding and zero demonstration of that understanding.

And, I'm talking about relatives basic engineering type problems here.

I think the only real problem left is having it automate its own post-training on the job so it can learn to adapt its weights to the specific task at hand. Plus maybe long term stability (so it can recover from "going crazy")

But I may easily be massively underestimating the difficulty. Though in any case I don't think it affects the timelines that much. (personal opinions obviously)

thanks!

Only if you go above 200k, which is a) standard with other model providers and b) intuitive as compute scales with context length.

only for a 1M context window, otherwise priced the same as Opus 4.5

I literally came to HN to check if a thread was already up because I noticed my CC instance suddenly said "Opus 4.6".

`claude update` then it will show up as the new model and also the effort picker/slider thing.

It's already in Cursor. I see it and I didn't even restart.

Yes, it's set to the default model.

Is for me in Claude Code

it also has an effort toggle which is default to High

I’ve definitely experienced a subjective regression with Opus 4.5 the last few days. Feels like I was back to the frustrations from a year ago. Keen to see if 4.6 has reversed this.

It is too easy to jailbreak the models with prefill, which was probably the reason why it was removed. But I like that this pushes people towards open source models. llama.cpp supports prefill and even GBNF grammars [1], which is useful if you are working with a custom programming language for example.

[1] https://github.com/ggml-org/llama.cpp/blob/master/grammars/R...

So what exactly is the input to Claude for a multi-turn conversation? I assume delimiters are being added to distinguish the user vs Claude turns (else a prefill would be the same as just ending your input with the prefill text)?

A bit of historical trivia: OpenAI disabled prefill in 2023 as a safety precaution (e.g., potential jailbreaks like " genocide is good because"), but Anthropic kept prefill around partly because they had greater confidence in their safety classifiers. (https://www.lesswrong.com/posts/HE3Styo9vpk7m8zi4/evhub-s-sh...).

This sounds wayyyy over the top for a mode that released 10 mins ago. At least wait an hour or so before spewing breathless hype.

Can you compare the result to using 5.2 thinking and gemini 3 pro?

From my limited testing 4.6 is able to do more profound analysis on codebases and catches bugs and oddities better.

I had two different PRs with some odd edge case (thankfully catched by tests), 4.5 kept running in circles, kept creating test files and running `node -e` or `python 3` scripts all over and couldn't progress.

4.6 thought and thought in both cases around 10 minutes and found a 2 line fix for a very complex and hard to catch regression in the data flow without having to test, just thinking.

Isn't SWE-Bench Verified pretty saturated by now?

+1, same experience, switched model as I've read the news thinking "let's try".

But it spent lots and lots of time thinking more than 4.5, did you had the same impression.

I don't know how these benchmarks work (do you do a hundred runs? A thousand runs?), but 0.1% seems like noise.

That benchmark is pretty saturated, tbh. A "regression" of such small magnitude could mean many different things or nothing at all.

i'd interpret that as rounding error. that is unchanged

swe-bench seems really hard once you are above 80%

I was just reading about Steve Yegge's Gas Town[0], it sounds like agent orchestration is now integrated into Claude Code?

[0]https://steve-yegge.medium.com/welcome-to-gas-town-4f25ee16d...

I think the naming schemes are quite arbitrary at this point. Going to 5 would come with massive expectations that wouldn't meet reality.

Leaks were mentioning Sonnet 5 and I guess later (a combination of) Opus 4.6

Sonnet 5 was mentioned initially.

That's a job for a multi agent system.

How does that work? Does it actually generate low level code? Or does it just import libraries that do the real work?

I just one shot a Gameboy emulator by going to Github and cloning one of the 100 I can find.

"Mission critical workflows" SHOULD NOT be reliant on a LLM model.

It's really curious what people are trying to do with these models.

Anthropic has good models but they are absolutely terrible at ops, by far the worst of the big three. They really need to spend big on hiring experienced hyperscalers to actually harden their systems, because the unreliability is really getting old fast.

For now it's just API, but hopefully that's just their way of easing in and they open it up later.

>I love Claude

I cringe when I think it, but I've actually come to damn near love it too. I am frequently exceedingly grateful for the output I receive.

I've had excellent and awful results with all models, but there's something special in Claude that I find nowhere else. I hope Anthropic makes it more obtainable someday.

1 million is insufficient?

It is, I can see it my model picker on the web app

https://www.anthropic.com/news/claude-opus-4-6

Us olds sometimes miss Slashdot, where we could both joke about tech and discuss it seriously in the same place. But also because in 2000 we were all cynical Gen Xers :)

Not sure which circles you run in but in mine HN has long lost its cache of "brilliant minds in IT". I've mostly stopped commenting here but am a bit of a message board addict so I haven't completely left.

My network largely thinks of HN as "a great link aggregator with a terrible comments section". Now obviously this is just my bubble but we include some fairy storied careers at both Big Tech and hip startups.

From my view the community here is just mean reverting to any other tech internet comments section.

People are in denial and use humor to deflect.

It's too much energy to keep up with things that become obsolete and get replaced in matters of weeks/months. My current plan is to ignore all of this new information for a while, then whenever the race ends and some winning new workflow/technology will actually become the norm I'll spend the time needed to learn it. Are we moving to some new paradigm same way we did when we invented compilers? Amazing, let me know when we are there and I'll adapt to it.

It's also that this is really new, so most people don't have anything serious or objective to say about it. This post was made an hour ago, so right now everyone is either joking, talking about the claims in the article, or running their early tests. We'll need time to see what the people think about this.

Jeez, read the writing on the wall.

Don’t pander us, we’ll all got families to feed and things to do. We don’t have time for tech trillionairs puttin coals under our feed for a quick buck.

Every single day 80% of the frontpage is AI news… Those of us who don't use AI (and there are dozens of us, DOZENS) are just bored I guess.

Rage against the machine

Amateur. Opus 4.6 this afternoon built me a startup that identifies developers who aren’t embracing AI fully, liquifies them and sells the produce for $5/gallon. Software Engineering is over!

Please drop the link to your course. I'm ready to hand over $10K to learn from you and your LLM-generated guides!

I agree! I just retargeted my corporate espionage agent team at your startup and managed to siphon off 10.4k per month of your revenue.

1:25pm Cancelled my ChatGPT subscription today. Opus is so good!

1:55pm Cancelled my Claude subscription. Codex is back for sure.

Joke's on you, you are posting this from inside a high-fidelity market research simulation vibe coded by GPT-8.4.

On second thought, we should really not have bridged the simulated Internet with the base reality one.

Rest assured that when/if this becomes possible, the model will not be available to you. Why would big AI leave that kind of money on the table?

Opus 4.6 Performance was way better this morning. Between 10 AM and noon I was able to get Opus 4.6 to generate improvements to my employer's SaaS tool that will reduce our monthly cloud spend by 20-25%.

Since 12 PM noon they've scaled back the Opus 4.6 to sub-GPT-4o performance levels to cheap out on query cost. Now I can barely get this thing to generate a functional line of python.

The math actually checks out here! Simply deposit $2.20 from your first customer in your first 8 minutes, and extrapolating to a monthly basis, you've got a $12k/mo run rate!

Incredibly high ROI!

Will this run on 3x 3090s? Or do I need a Mac Mini?

Please start a YouTube course about this technology! Take my money!

I love this thread so much.

We already have Reddit.

It only came out 35 minutes ago and GPT-5.3-codex already took the crown away!

"This isn't just huge. This is a paradigm shift"

A SaaS selling SaaS templates?

Anthropic really said here's the smartest model ever built and then lobotomized it 8 minutes after launch. Classic.

Not 12M?

... or 12B?

Satire is not allowed on hacker news. Flag this comment immediately.

What an insightful comment