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Claude Opus 4.6

anthropic.com

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Re: Claude Opus 4.6

#231

Post by chis »

cootsnuck wrote: post #100
simonw wrote: post #50

Earlier quoted context omitted.

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. >…

I have not see any reporting or evidence at all that Anthropic or OpenAI is able to make money on inference yet. > Turns out there was a lot of low-hanging fruit in terms of inference optimization that hadn't been plucked yet. That does not mean the frontier labs are pricing their APIs to cover their costs yet. It can both be true that it has gotten cheaper for them to provide inference and that they still are subsid…

It's quite clear that these companies do make money on each marginal token. They've said this directly and analysts agree [1]. It's less clear that the margins are high enough to pay off the up-front cost of training each model.

[1] https://epochai.substack.com/p/can-ai-companies-become-profi...

Re: Claude Opus 4.6

#232

Post by stkai »

simonw wrote: post #40

The bicycle frame is a bit wonky but the pelican itself is great: https://gist.github.com/simonw/a6806ce41b4c721e240a4548ecdbe...

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

Re: Claude Opus 4.6

#233

Post by jedberg »

rogerrogerr wrote: post #21
GenerocUsername wrote: post #11

This is huge. It only came out 8 minutes ago but I was already able to bootstrap a 12k per month revenue SaaS startup!

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!

"Soylent Green is made of people!"

(Apologies for the spoiler of the 52 year old movie)

Re: Claude Opus 4.6

#234

Post by zhengyi13 »

intelliot wrote: post #215
ibejoeb wrote: post #96

Earlier quoted context omitted.

Bringing me back to slashdot, this thread

What did happen to ye olde slashdot anyway? The original og reddit

They're still out there; people are still posting stories and having conversations about 'em. I don't know that CmdrTaco or any of the other founders are still at all involved, but I'm willing to bet they're still running on Perl :)

Re: Claude Opus 4.6

#235

Post by schappim »

pants2 wrote: post #162
dmk wrote: post #46

The benchmarks are cool and all but 1M context on an Opus-class model is the real headline here imo. Has anyone actually pushed it to the limit yet? Long context has historically been one of those "works great in the demo" situations.

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

The only way to not go bankrupt is to use a Claude Code Max subscription…

Re: Claude Opus 4.6

#236

Post by runarberg »

Aurornis wrote: post #138
Someone1234 wrote: post #19

Does anyone with more insight into the AI/LLM industry happen to know if the cost to run them in normal user-workflows is falling? The reason I'm asking is because "agent teams" while a cool concept, it largely constrained by the economics of running multiple LLM agents (i.e. plans/API calls that make this practical at scale are expensive). A year or more ago, I read that both Anthropic and OpenAI were losing money o…

> 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…

I can see a case for omitting R&D when talking about profitability, but training makes no sense. Training is what makes the model, omitting it is like omitting the cost of running the production facility of a car manufacturer. If AI companies stop training they will stop producing models, and they will run out of a products to sell.

Re: Claude Opus 4.6

#237

Post by 3836293648 »

Aurornis wrote: post #138
Someone1234 wrote: post #19

Does anyone with more insight into the AI/LLM industry happen to know if the cost to run them in normal user-workflows is falling? The reason I'm asking is because "agent teams" while a cool concept, it largely constrained by the economics of running multiple LLM agents (i.e. plans/API calls that make this practical at scale are expensive). A year or more ago, I read that both Anthropic and OpenAI were losing money o…

> 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…

The reports I remember show that they're profitable per-model, but overlap R&D so that the company is negative overall. And therefore will turn a massive profit if they stop making new models.

Re: Claude Opus 4.6

#238

Post by scrollop »

jorl17 wrote: post #185

This is the first model to which I send my collection of nearly 900 poems and an extremely simple prompt (in Portuguese), and it manages to produce an impeccable analysis of the poems, as a (barely) cohesive whole, which span 15 years. It does not make a single mistake, it identifies neologisms, hidden meaning, 7 distinct poetic phases, recurring themes, fragments/heteronyms, related authors. It has left me completel…

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

Re: Claude Opus 4.6

#239

Post by nomel »

itay-maman wrote: post #204

Impressive results, but I keep coming back to a question: are there modes of thinking that fundamentally require something other than what current LLM architectures do? Take critical thinking — genuinely questioning your own assumptions, noticing when a framing is wrong, deciding that the obvious approach to a problem is a dead end. Or creativity — not recombination of known patterns, but the kind of leap where you r…

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.

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