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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

#331

Post by jug »

Klathmon wrote: post #322
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.

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'…

Yes, I've seen this too and how Luna xhigh is so good that Terra doesn't really serve a purpose because beyond that you can continue at Sol medium. This can be the most cost efficient way, and especially now!

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

#332

Post by weird-eye-issue »

simianwords wrote: post #209

There were people on HN who still thought that the API prices were being subsidised. The level of conspiracy theory was off the charts on this topic. You would get these price reductions month over month you would still have people believing in crazy stuff.

I mean to be fair this price reduction does not prove anything

If somebody already thought the price was subsidized then this price change would just mean it's even more subsidized, so why would that change their mind?

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

#333

Post by an0malous »

captainbland wrote: post #24
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.

To be fair we don't really know in terms of prices what's real and what's just investor subsidised attempts at market capture at this point. It could well be OpenAI's attempt to drown Anthropic while they've got the halo product if they feel they've got deeper pockets.

I bet they’ll announce new fundraising soon. Someone gave them a top up to try and corner the market.

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

#334

Post by weird-eye-issue »

purpleidea wrote: post #87

I would pay significantly more to use these models if there was a legal contract that guaranteed they weren't ever terfing them and some way to prove that.

Don't you realize how many people are using these in production and rely on responses in very specific formats and have logging and metrics in place to detect any issues and if they were just randomly nerfing models they would cause major issues in production that tons of people would immediately notice? The easy way to prove this is to set up your own evals and then run them daily

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

#335

Post by try-working »

gck1 wrote: post #271
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).

Luna is comparable to GPT 5.4 from 4 months ago on many benchmarks. I know many who have said during that time, myself included, that if that's the model they had to use for the rest of their lives, they'd be fine. GPT 5.4 is/was a very capable model.

Agree on 5.4. And it has 4x more quota than 5.6.

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

#336

Post by ch4s3 »

Cort3z wrote: post #302
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.

The magic is in the fact that they essentially have an ASIC llm device. There is no other trickery. The problem they will face is that it is actually locked in silicon, so upgrading models will be difficult, and likely require new hardware each time.

At some point I imagine you’d add a software layer on top that holds more current training and can be called as needed trading off for slower responses. There’s already work out there splitting models across networks. You could have the base on silicon, some stuff in memory on the machine, and another frontier tool in the cloud.

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

#337

Post by xyzzy123 »

program_whiz wrote: post #317
xyzzy123 wrote: post #305

Earlier quoted context omitted.

Its funny because you can write a halting problem oracle by calling out to an LLM and have it return yes / no / not sure and get it to work reliably for almost all real code, like that is an entirely practical thing to do in 2026. All we need now is some sort of program to evaluate halting problem oracles...

Wasn't the original proof something like "a program that loops indefinitely when the oracle says it will halt, and stops immediately if an oracle says it will not stop"? The whole point was the you can construct a contradictory program that cannot be decided by any oracle by its very nature. ```python source_code = open(__file__, 'rt').read() response = ask_llm("will this program halt, only answer 'yes' or 'no'?\n" +…

You're right of course, LLMs provide a partial, unsound oracle.

The "halting problem is unsolvable" argument relies on the oracle not being able to output "not sure". But adding that option admits trivial oracles, like ones which output "not sure" for everything, so some are better than others.

The "real world" use most people have for halting oracles is as part of software safety, where if the checker outputs "not sure" you modify the software until the checker can decide if it halts.

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

#338

Post by taf2 »

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 remember bugs always end up being more expensive then the first time I implemented the feature… so for me always sol always max… I don’t want to pay for a bug later

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

#339

Post by jaggederest »

ch4s3 wrote: post #336
Cort3z wrote: post #302

Earlier quoted context omitted.

The magic is in the fact that they essentially have an ASIC llm device. There is no other trickery. The problem they will face is that it is actually locked in silicon, so upgrading models will be difficult, and likely require new hardware each time.

At some point I imagine you’d add a software layer on top that holds more current training and can be called as needed trading off for slower responses. There’s already work out there splitting models across networks. You could have the base on silicon, some stuff in memory on the machine, and another frontier tool in the cloud.

Taalas actually already support LoRA, basically doing exactly what you say.

The other thing that I think is really interesting about all of this, is that LLMs are already perforce behind the times with their knowledge cutoff, so adding an additional ~3 months for bake into silicon isn't such a huge deal, I think, for the ~10x more efficient and faster you get.

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

#340

Post by mgambati »

heisgone wrote: post #112
ismailmaj wrote: post #54

Earlier quoted context omitted.

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?

Let's suppose each models was subsidized at 70%, so that we only pay 30% of the cost. They would loose much more money per token on the more powerful models. It's in their interest to encourage the use of the less expensive models. Let's say they increase Luna subsidies at 90%. They would still "save" relative to the use of the more expensive models.

Not true at all. Do you really believe that generating 1m output tokens on sol really cost 100 usd?

These companies are printing money on inference. The only issue is the capex of expending on new servers but they are generating TONS of income.

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