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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

#321

Post by 5555watch »

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

I'm curious, how hard/expensive it is to burn a really large model into silicon, and why aren't we doing this already?

Or, when we will start doing this, who's going to be able to do that in scale?

I'm seeing the TAALAS example, but it's only an 8B model, suggesting some real limitations parameter wise. And for 2.5kW?

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

#322

Post by Klathmon »

re-thc wrote: post #106
827a wrote: post #82

Earlier quoted context omitted.

Totally untrue. Luna and Sonnet 5 are very comparable: https://artificialanalysis.ai/#intelligence Luna is an extremely strong model.

> 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.

I'm actually finding Luna to be an extremely strong model in practice.

I almost exclusively use it with xhigh or max effort, but when run like that it's been an incredibly cheap little workhorse for most development work. I'm still leaning on Sol for planning and debugging, but when it's time to start pumping out code I've been leaning into Luna (Max) and I've been enjoying it! And that was before the price drop, it's going to feel practically free at this point

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

#324

Post by jaggederest »

blovescoffee wrote: post #306
christophilus wrote: post #254

Earlier quoted context omitted.

Wow. This is absolutely wild. I didn't expect that. If we get to anywhere near this speed for the equivalent of the current models... I don't even know what to think about that future.

what do you mean "if"? of course we will, and the models will be smarter as well

Yeah. A model smarter than Fable by, say, 50 human IQ point equivalents, running in some kind of autoregressive process continously on whatever goals you put into the loop, on baked silicon, would be maybe my low end for the potential in the relatively near future.

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

#325

Post by jaggederest »

nerdsniper wrote: post #191
jcul wrote: post #159

Earlier quoted context omitted.

It's crazy. Are they doing any precomputing as you type, I wonder if you paste a block of text is it the same speed.

I pasted and instantly hit enter on this prompt: "I generated a filter set using REW v5.31.3 using real-world sweep tone measurements from the room I'm listening in . How can I use it as my MacOS output equalizer so that my spotify music is adjusted for this room and speakers" and it gave a very reasonable answer in non-perceptible time.

That's a hell of a prompt, you should make a post about what you're doing maybe, because I want that for myself.

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

#326

Post by layoric »

greggh wrote: post #316
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).

Use a harness like OMP that lets you choose which model does which things. My main model is GLM 5.2, it handles planning and anything I dont have covered by other models. Tasks from todos and in sub agents are done by deepseek, I have different models for the git work like add/commit/push (that goes through cheap Minimax M3), and so on... This way the expensive/strong model only handles the architecture and orchestra…

I've done a little bit in this space to try and see what works, and maybe it is different per language/framework but found the use case seems best for largely mechanical code changes, eg small direction, lots of simple changes. This sounds good until you realize that IDEs generally have built in functionality to do these tasks waaay faster, cheaper and more accurately.

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

#327

Post by keeganpoppen »

in_a_society wrote: post #142
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).

I don't see why it should be all that difficult. All you have to do is first find a library that implements a decent solution to the halting problem and you're off to the races.

ya im partial to oracle.ts xD

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

#328

Post by Rendak »

sipjca wrote: post #312
dannyw wrote: post #83

Earlier quoted context omitted.

OpenAI's APIs are extremely reliable for sure. I don't even remember when the last incident or downtime was.

all day yesterday, Taiwan time Sol was having significant issues. “Overloaded”, maybe half of requests or more wouldn’t go through

That’s what I was seeing too, and my cache hit rate was below 25% during that same time, leading to significant burn of my weekly limit (Pro 5x plan) via all the uncached input. Doesn’t prove that the overload caused the cache failure, but it does seem to point to some common infrastructure cause. No such problems (cache hit or system overload) with Terra or Luna.

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

#329

Post by jug »

maxdo wrote: post #48
pavpanchekha wrote: post #4

Making Luna, which was already very cheap and extremely capable, 5x cheaper is crazy. I use Sol at work but Luna at home, and while there's definitely a difference, it doesn't feel like night-and-day. After a year of ever-increasing prices it suddenly feels (between this, Kimi K3, GLM 5.2) that prices are falling again.

is kimi that cheap? it's a very expensive model

Kimi K3 is fairly cheap per token but thinks like a madman with poor self esteem.

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

#330

Post by weird-eye-issue »

baalimago wrote: post #111
gbnwl wrote: post #92

Earlier quoted context omitted.

Experienced similar between 5.4-mini vs 5.6-luna in our own pipelines but after spending some time on prompt optimization and testing out various reasoning effort levels 5.6-luna was well worth it. Did you just replace model selection while keeping everything else in place or spend some time on evaling with newer prompts etc?

No we kept prompts as is, just swapped model. The prompt is already quite optimized for the task. How would updating it possibly make a more intelligent model spend less tokens than a less intelligent model? Care to elaborate?

Yikes, you can't really expect prompts to just be model agnostic
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