Advancing the price-performance frontier with GPT‑5.6

GPT 5.4 is/was a very capable model.

Famously, this is also a problem for human coders in sprint planning.

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.

This way the expensive/strong model only handles the architecture and orchestration tasks. The cheaper models handle everything else and the strong one knows how to tell them what to do in enough detail to get good work out of them.

/s

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), that will start being done in the future.

Assuming the efficiency gains are real, I feel like something has to give, maybe worse quality due to aggressive quantization/kv cache compression?

There is ZERO reason to believe models 1/10th the size of frontier are completely capped on intelligence and impossible to get smarter.

They have consistently compressed the intelligence of larger models.

You'll see it first on the small end, when they stop being able to compress intelligence, you know that will slowly bubble up and up the chain to larger and larger models.

There's no evidence we've reached that at the bottom.

I think there's also a new generation of hardware in the past year or so tuned specifically for LLM workloads, where it was almost an accident that GPUs worked to run LLMs before. So, while there's still this ridiculous shortage of hardware, what is being delivered is much faster and cheaper to run for these specific workloads.

I wasn't expecting it to happen from the US vendors, though, as they've spent so much capital to get to where they are they need to make huge margins on inference to pay it all back. I expected the Chinese models who're running much leaner operations to be the "frontier" on costs (and they have been). But, I'm glad to see OpenAI joining the "cheap and cheerful" models party. There's a lot of work in that area of capability. Probably most work people are doing falls into that area of capability.

But yeah I do'nt want to know what Kimi 3 is pushing buttons inside Anthropic, OpenAI and Google.

Besides any floor: For every year the tokens get faster and cheaper, we will see new things like properly working AI factories which mimic expert teams. A lot more parallism as well.

With an 80% reduction in cost that becomes a ridiculous outlier in efficiency.

You mean they increased the price and then cut it back and now it is amazing?

Luna had a price hike vs mini (its previous replacement). The cut now just puts it back in that ball park.

Not that this isn't good news, but what's impressive?

This is very likely priced below recovering the cost of the hardware but still above operating expenses.

> it doesn't feel like night-and-day.

I see what you did there. :)

Now we have an American lab drastically cutting a price, feels like this is the opposite of that trend.

If those numbers are accurate, I don't think 20% is a really, really big deal. It's like saying "we're digging our grave 20% slower." Ok, but they're still digging!

Or, different analogy, if I'm going broke because I lost my job due to executive AI psychosis, cancelling my netflix subscription doesn't really change the math of not being able to afford rent. It doesn't even really slow it. The amount of money that OpenAI is spending is so absurd that a minor cost saving is like, uh, some progress, but they'd need to do it a lot more to move the needle

> reduced inference cost by 20 percent saving company x billion dollars per month

Your support bot.

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Your personal assistent bot.

Yes, I can just do it myself, but even at API prices, I'd rather have the LLM do it.

You can actually use Luna without reasoning (set it to "none"). So if they wanted to, they could definitely replace 5.5 Instant with it.

My company checks the models and pays for Opus through AWS.

You still send the WHOLE context of whatever you want to do to a random endpoint on the internet. If you want to write a good email, you give that context your email address, names, the reason for it etc.

Big companies don't randomly use some random api endpoint to do so.

Anthropics quarerly revenue is still growing very fast. I don't think we have seen even the real potenzial of it yet at all.

Not only are still a lot of countries missing which do not even use anthropic or any other frontier model yet but also all the agentic based solutions enterprise companies are currently building on mass (at least in my industry)

Stop rejecting what we have been collectively telling you guys! LLM providers are profitable, and have been for awhile!

- GPT-5 high: score 35, approximately $0.37/task

- Luna medium: score 38, approximately $0.01/task

- Luna max: score 51, approximately $0.042/task

So Luna medium is:

- slightly more capable than GPT-5 high;

- approximately 35–40× cheaper per benchmark task.

And Luna max is:

- 16 Intelligence Index points better;

- still roughly 9× cheaper per task.

This reduction was possible within 1 year.

> China: Household rates average around $0.08 / kWh (¥0.53/kWh).

vs

> US: Household rates average around $0.16 / kWh, though regional variation is massive—ranging from ~$0.10/kWh in low-cost states (like Washington or Louisiana) to $0.30–$0.45+/kWh in high-cost areas like California or Hawaii.

https://files.catbox.moe/csxl32.png

(2 cents to run AA index, score 40)

Looks like OpenAI broke the pareto frontier on the trust-me-bro benchmarks!

(One has to wonder if they used any of the neat tricks from the DSV4 paper :)

https://api-docs.deepseek.com/updates/

The update seems to say that it's a re-post train of exactly the same model.

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?

Has been pretty accurate when pre-release quality or bugs pop up. In theory you could run your own.

Now, if cost is your concern, then that’s a problem in all of computing. Hence why I’m sending you short plain text messages using an iPhone with a many-core CPU and gigabytes of RAM.