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Claude Opus 4.6

anthropic.com

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Re: Claude Opus 4.6

#361

Post by m101 »

chis wrote: post #231
cootsnuck wrote: post #100

Earlier quoted context omitted.

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...

It’s not clear at all because model training upfront costs and how you depreciate them are big unknowns, even for deprecated models. See my last comment for a bit more detail.

Re: Claude Opus 4.6

#362

Post by eli »

fragmede wrote: post #341
stkai wrote: post #232

Earlier quoted context omitted.

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

The estimation I did 4 months ago: > there are approximately 200k common nouns in English, and then we square that, we get 40 billion combinations. At one second per, that's ~1200 years, but then if we parallelize it on a supercomputer that can do 100,000 per second that would only take 3 days. Given that ChatGPT was trained on all of the Internet and every book written, I'm not sure that still seems infeasible. http…

How would you generate a picture of Noun + Noun in the first place in order to train the LLM with what it would look like? What's happening during that 1 estimated second?

Re: Claude Opus 4.6

#363

Post by nodja »

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, 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

Re: Claude Opus 4.6

#364

Post by nickstinemates »

losvedir wrote: post #348
nickstinemates wrote: post #313

Earlier quoted context omitted.

[flagged]

Oh, is that what the issue is? I've seen the "flicker" thing as a meme, but as someone who uses Claude Code I've never noticed. I use ghostty mostly, so maybe it's not an issue with ghostty? Or maybe I just haven't noticed it.

Yes it's people using bad tools on underpowered machines as far as I have seen

Re: Claude Opus 4.6

#365

Post by arionmiles »

gcanyon wrote: post #207
simonw wrote: post #40

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

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.

There's a research paper from the University of Liverpool, published in 2006 where researchers asked people to draw bicycles from memory and how people overestimate their understanding of basic things. It was a very fun and short read.

It's called "The science of cycology: Failures to understand how everyday objects work" by Rebecca Lawson.

https://link.springer.com/content/pdf/10.3758/bf03195929.pdf

Re: Claude Opus 4.6

#368

Post by epolanski »

apetresc wrote: post #61

Impressive that they publish and acknowledge the (tiny, but existent) drop in performance on SWE-Bench Verified between Opus 4.5 to 4.6. Obviously such a small drop in a single benchmark is not that meaningful, especially if it doesn't test the specific focus areas of this release (which seem to be focused around managing larger context). But considering how SWE-Bench Verified seems to be the tech press' favourite be…

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.

Re: Claude Opus 4.6

#369

Post by tedsanders »

wasmainiac wrote: post #265
gizmodo59 wrote: post #107

5.3 codex https://openai.com/index/introducing-gpt-5-3-codex/ crushes with a 77.3% in Terminal Bench. The shortest lived lead in less than 35 minutes. What a time to be alive!

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?

We don't vary our model quality with time of day or load (beyond negligible non-determinism). It's the same weights all day long with no quantization or other gimmicks. They can get slower under heavy load, though.

(I'm from OpenAI.)

Re: Claude Opus 4.6

#370

Post by rolisz »

aggie wrote: post #346
legitster wrote: post #199

Earlier quoted context omitted.

I have three specific use cases where I try both but ChatGPT wins: - Recipes and cooking: ChatGPT just has way more detailed and practical advice. It also thinks outside of the box much more, whereas Claude gets stuck in a rut and sticks very closely to your prompt. And ChatGPT's easier to understand/skim writing style really comes in useful. - Travel and itinerary: Again, ChatGPT can anticipate details much more, an…

How does #3 square with Anthropic's literal warehouse full of books we've seen from the copyright case? Did OpenAI scan more books? Or did they take a shadier route of training on digital books despite copyright issues, but end up with a deeper library?

I think they bought the books after they were caught that they pirated the books and lost that case (because they pirated, not because of copyright).
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