Anecdotal, but it 1 shot fixed a UI bug that neither Opus 4.5/Codex 5.2-high could fix.
Claude Opus 4.6
91–100 of 1001 posts
Re: Claude Opus 4.6
#92The bicycle frame is a bit wonky but the pelican itself is great: https://gist.github.com/simonw/a6806ce41b4c721e240a4548ecdbe...
They trained for it. That's the +0.1!
Re: Claude Opus 4.6
#93Does 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…
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
From Alphabet 2025 Q4 Earnings call:
"As we scale, we’re getting dramatically more efficient. We were able to lower Gemini serving unit costs by 78% over 2025 through model optimizations, efficiency and utilization improvements."
https://abc.xyz/investor/events/event-details/2026/2025-Q4-E...
Re: Claude Opus 4.6
#94Earlier 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. >…
> "engineers optimizing inferencing" are we sure this is not a fancy way of saying quantization?
Or distilled models, or just slightly smaller models but same architecture. Lots of options, all of them conveniently fitting inside "optimizing inferencing".
Re: Claude Opus 4.6
#95The bicycle frame is a bit wonky but the pelican itself is great: https://gist.github.com/simonw/a6806ce41b4c721e240a4548ecdbe...
Can it draw a different bird on a bike?
Here's a kākāpō riding a bicycle instead: https://gist.github.com/simonw/19574e1c6c61fc2456ee413a24528...
I don't think it quite captures their majesty: https://en.wikipedia.org/wiki/K%C4%81k%C4%81p%C5%8D
Re: Claude Opus 4.6
#96This 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!
Bringing me back to slashdot, this thread
Re: Claude Opus 4.6
#97in a first for our Opus-class models, Opus 4.6 features a 1M token context window in beta.
Re: Claude Opus 4.6
#98Somehow regresses on SWE bench?
That benchmark is pretty saturated, tbh. A "regression" of such small magnitude could mean many different things or nothing at all.
Re: Claude Opus 4.6
#99The bicycle frame is a bit wonky but the pelican itself is great: https://gist.github.com/simonw/a6806ce41b4c721e240a4548ecdbe...
There's no way they actually work on training this.
Re: Claude Opus 4.6
#100Does 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…
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 subsidizing inference costs.
In fact, I'd argue that's way more likely given that has been precisely the goto strategy for highly-competitive startups for awhile now. Price low to pump adoption and dominate the market, worry about raising prices for financial sustainability later, burn through investor money until then.
What no one outside of these frontier labs knows right now is how big the gap is between current pricing and eventual pricing.