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Advancing the price-performance frontier with GPT‑5.6

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Re: Advancing the price-performance frontier with GPT‑5.6

#131

Post by jaggederest »

jrflo wrote: post #115
jpadkins wrote: post #86

Earlier quoted context omitted.

When model intelligence reliably hits 90%-95% of current day knowledge worker tasks, they are going to burn those weight to silicon and we will see another 10X improvement in price/performance frontier. The dynamic GPU clusters will be used for the 5% of tasks, and pushing out the frontier. Also there will be a set of knowledge tasks that are not done today (because they are too difficult for most knowledge workers),…

Burning the weights into silicon would be many orders of magnitude increase, not just 10x. It's kind of crazy that this hockey stick the AI hype bros talk about seems more and more every day like it might be real

https://taalas.com/ has done it already for a wildly obsolete model. 14000 tokens per second.

https://chatjimmy.ai/ is their interactive. Tiny context, very dumb, but absurdly fast. Imagine this as a tool call for claude code for trivial changes - the tool call from the harness takes longer than the execution.

Re: Advancing the price-performance frontier with GPT‑5.6

#132

Post by ceroxylon »

WarmWash wrote: post #93
preommr wrote: post #14

> Starting today, GPT‑5.6 Luna, our fastest and most affordable model, will cost 80% less, I don't have the words. I genuinely thought we were in a stage where we were plateauing and going in for 5-10% improvements over months. Seeing spikes like this makes me question about where the floor really is.

Totally possible that humans aren't actually that intelligent.

As well as the existing intelligence being swayed by emotions, hormones, circadian rhythms, stress, peer pressure, propaganda, and survival instincts.

Re: Advancing the price-performance frontier with GPT‑5.6

#133

Post by mediaman »

camel-cdr wrote: post #75
preommr wrote: post #14

> Starting today, GPT‑5.6 Luna, our fastest and most affordable model, will cost 80% less, I don't have the words. I genuinely thought we were in a stage where we were plateauing and going in for 5-10% improvements over months. Seeing spikes like this makes me question about where the floor really is.

this type of thing usually means you are the product

I don't see how this follows. The cost of nails has fallen by 95% over the last century. It's because the cost of manufacturing has fallen. Not because they are selling the information of nail consumers.

Tokens are not normal software, because they have marginal cost, and I think people who are used to software economics really struggle with this. With token generation there really can be manufacturing cost efficiencies where one producer is just straight up better at serving product at a lower marginal cost.

Re: Advancing the price-performance frontier with GPT‑5.6

#134

Post by mlinsey »

ismailmaj wrote: post #54
preommr wrote: post #14

> Starting today, GPT‑5.6 Luna, our fastest and most affordable model, will cost 80% less, I don't have the words. I genuinely thought we were in a stage where we were plateauing and going in for 5-10% improvements over months. Seeing spikes like this makes me question about where the floor really is.

it's 80% less cost, not 80% in efficiency gains, could be that Luna was overpriced to begin with, we don't have much info on the models themselves. Assuming the efficiency gains are real, I feel like something has to give, maybe worse quality due to aggressive quantization/kv cache compression?

High-performing open weight models being released recently, and your customers looking into working with multiple providers as a result, are a great reason to drop prices on your non-frontier offerings.

Although I'm sure there are some efficiency gains, the technology is too new and labs are scrambling to release too quickly to think that the low-hanging optimization fruit has been picked already.

Re: Advancing the price-performance frontier with GPT‑5.6

#135

Post by bryanlarsen »

GodelNumbering wrote: post #44

"Half the money I spend on advertising is wasted; the trouble is I don't know which half." -John Wanamaker This applies even more strongly to model choosing. I know for a fact that majority of my work doesn't require a very strong model, but separating the trivial and non-trivial tasks is a famously hard problem (if at all decidable).

Highlighting https://news.ycombinator.com/item?id=49113236 in response.

HN could be run as a BBS on 70's hardware. Instead of using a CPU with ~10 thousand transistors, you're likely using one with ~10 billion to do basically the same thing, and you don't think twice about it.

Re: Advancing the price-performance frontier with GPT‑5.6

#136

Post by re-thc »

mediaman wrote: post #128
re-thc wrote: post #106

Earlier quoted context omitted.

> Luna is an extremely strong model. By benchmarks, which sadly is a poor measure. Yes Luna is a good model under certain circumstances. Whether it is great for general usage is another story. Sonnet is definitely better when prompts are more vague and it needs to decide things. Luna generally sticks to things very strictly and goes off in bad ways.

Yes but there is a big, big market for subagents to consume lots of tokens cheaply and condense information up to parent agents. Luna would not be my choice for planning. But an explorer to comb through a codebase to find relevant parts? Or for enterprise retrieval, where it needs to search across many different types of data to see where to focus efforts for a smarter model? Or to wake up periodically to evaluate so…

> Yes but there is a big, big market for subagents to consume lots of tokens cheaply and condense information up to parent agents.

That's again not some "intelligence factor" here. Different agents work for different use cases. Luna wins some. Terra wins some. Sonnet wins some. Flash was really good at exploring.

So I'm not sure what your point is? There's a big market for everything. Even within the market you describe it's likely not a Luna-size fits all either.

Re: Advancing the price-performance frontier with GPT‑5.6

#137

Post by iamjackg »

jaggederest wrote: post #131
jrflo wrote: post #115

Earlier quoted context omitted.

Burning the weights into silicon would be many orders of magnitude increase, not just 10x. It's kind of crazy that this hockey stick the AI hype bros talk about seems more and more every day like it might be real

https://taalas.com/ has done it already for a wildly obsolete model. 14000 tokens per second. https://chatjimmy.ai/ is their interactive. Tiny context, very dumb, but absurdly fast. Imagine this as a tool call for claude code for trivial changes - the tool call from the harness takes longer than the execution.

Holy crap, I was not prepared for how fast it responded. I just wrote "Just wanted to see how fast you are! Can you write me a quick story about a tiger who lives inside a block of cheese the size of a house?"

I pressed Enter, and the response was instant.

> Generated in 0.037s • 14,205 tok/s

This is unbelievable.

Re: Advancing the price-performance frontier with GPT‑5.6

#139

Post by dust42 »

NitpickLawyer wrote: post #17
simonw wrote: post #3

> The kernel work helped reduce the end-to-end cost of serving the model by 20%, while its experiments increased token-generation efficiency by more than 15%. If the cost of serving GPT-5.6 just dropped by 20%, does that add up to literally billions of dollars in savings per month? We know Anthropic spend $1.25 billion renting inference capacity from SpaceX (in two Colossus datacenters) from the SpaceX IPO, but we do…

~2 years ago gemini2.5 helped write better kernes for itself and (only) reached 1% efficiency gains. Today we're at 20%.

In 2 years from now we will be at 400%. https://xkcd.com/605/ Also, it is called kernels (you have nitpick in your username)

Re: Advancing the price-performance frontier with GPT‑5.6

#140

Post by newtwilly »

heaney-555 wrote: post #81
tosh wrote: post #30

80% price cut for luna is a very aggressive pricing move makes it by far the best choice for most workloads that do not need bleeding edge intelligence (reminder: luna can be comparable to opus 5!)

Luna is meant to compete with Haiku. What tasks are you seeing it equal Opus on?

According to the Artificial Analysis benchmark graph in the article, Luna can now outperform Sonnet 5 and Opus 5 low at ~4-10x less cost
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