Comment by preommr
5 hours ago
> 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.
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), that will start being done in the future.
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.
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And don't underestimate how much money Google, Microsoft, Amazon and Meta still have to spend on this tech.
Blocking Fable for sure made it very politicl a lot sooner than i expected it to happen.
and because China already has massive problems of getting access, they are pushing it on hardware too like what Huawai did without EUV.
It seems China is already able to do DUV a lot sooner than others expected.
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Yep it will be ASICs and DSPs all over again. Orders of magnitude changes.
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Not an expert on this but wouldn’t this be possible with something similar to an FPGA?
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What does that mean though? Like some kind of a ROM memory ?
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> When model intelligence reliably hits 90%-95% of current day knowledge worker tasks, they are going to burn those weight to silicon
Google is already working on a similar idea but more "flexible".
Explain.
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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.
Enterprises implemented spending caps and inference providers are lowering prices. Seems they are jockeying for market share.
Partnerships then consolidation comes next...
I wouldn't be surprised if they still had some margins since cheaper models are much harder to nail the accurate sizes off, and you still pay 2x for 1M context window.
But if this is even at 400B size it's insanity those inference prices, maybe 10-20% margins, if it's higher I would like to know is it their own chips or maybe they have accurately sized the model to fit on exactly a B300?
Could be a lot of magical things we can only speculate, but from here there likely isn't another 60-70% margin, like I have heard people claim, I would definitely be willing to bet on that.
Could still be a healthy 10-30% margin. Especially with Terra.
We can guess based on the decisions of other inference providers who serve these models.
Do you mean if other providers will cut their prices in turn?
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Hard to believe numbers. I don't mean that as a critique, but literally I am so impressed. Even if the model is a few percent lower for performance but is 80+% cheaper than competitors and is a US company hosted on US based hyperscaler clouds this is kind of a no brainer. Hard for most businesses to justify otherwise.
This is exactly the model that DeepSeek V4 Flash followed, and it's been insanely successful as a result, even though it's not frontier.
DeepSeek v4 Pro & MiMo v2.5 Pro (Opus 4.6 quality models for code) are insanely cheap for agent-driven work due to their super low cached-input prices ($0.0036/mtok) [0]. For Luna, the cached-input price drop isn't disclosed in TFA, but the pricing page puts it at $0.02/mtok, & that's 5x more expensive.
[0] I am constantly surprised how much work pay-as-you-go with DeepSeek / MiMo will get done. I've barely crossed $2 each in a month of use (~200m tokens).
Absolutely. Although DeepSeek started announcing "Peak valley" pricing which started making me nervous. I have spent $50 usd in July on deepseek and for that much spend I got SO MUCH mileage.
I feel perfectly content in using pay as you go pricing with deepseek. On the other hand, although Anthropic's models used to be my bread and butter for personal work, they are simply too expensive to reach for these days.
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.
> Let's suppose each models was subsidized at 70%, so that we only pay 30% of the cost.
why on earth would you suppose that?
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.
Been using OpenAI models since ada/babbage/curie/davinci and at least from my own experience, their APIs feel the same.
If you use Codex it's different, the harness has a lot to do with it and there's definitely been changes including recently.
Something can be overpriced and still lose money.
5-10% over months would still be quite crazy.
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.
Vera Rubin will be hitting racks very soon, and this is purported to have a 10x improvement in token throughput per megawatt. Of course, old chips don't get replaced with new chips overnight, but I don't think we're anywhere near the floor yet.
AMD MI400 series is already shipping to customers (basically everybody) and it is crazy fast (8x to 10x faster than the previous gen and beats published Vera numbers in FP8, loses in FP4) and 432 GB per chip. 72 chip unified rack architecture (Helios) already shipping and projected to also beat Vera in NVL72.
MI500 series is supposedly already taping out and they're claiming massive increases (we'll find out end of 2027 prob).
In a data center that is power constrained but not space constrained they could build out new racks and flip the power from the old racks. Wonder if this will lead to moderately used server GPUs on the secondary market someday.
I don't think they overengineered a DC like this.
Besides Nvidia Hardware is still sold out and super expensive. Not a single Nvidia consumer GPU got cheaper at all, Nvidia DGX Spark got more expensive too.
It will be swooped of the market the second it hits the market.
This is gonna put Sonnet 5 in a really awkward spot.
I use sonnet as a smart grep and haiku never and that’s only when I have to use Anthropic at all
Sonnet and Haiku were already in an awkward spot, likely by design.
Anthropic's big marketing push this year has been entirely focused on getting people to use Opus via a Claude Code subscription, to the point that Sonnet is almost viewed as the poor man's alternative, and from what I've seen, almost nobody uses it.
Actually, here's an interesting project for all the vibe coders looking for their next front page post: scrape a ton of commits from GitHub with Co-Authored-By: Claude and figure out what the percentage split between Opus/Fable/Sonnet is. I'm willing to bet it's less than 10% Sonnet.
>figure out what the percentage split between Opus/Fable/Sonnet is.
This may be misleading, since I suspect many are using a blend through sub-agents. I tend to bias for Fable to orchestrate and Opus for implementation via sub-agents.
I've found that Claude nearly always claims to be Opus in the commit message, regardless of the actual model making the commit.
When I'm paying for it, Sonnet. When work is paying, Opus 5, then Fable if Opus gets confused.
Opus 5 is not strong enough as the top-of-stack model, and feels idiotic after a week or two of heavy Fable usage, to the point where I'm paying for Usage Credits to keep using Fable rather than having to slum it with Opus.
Luna is comparable to Haiku, not Sonnet.
Totally untrue. Luna and Sonnet 5 are very comparable: https://artificialanalysis.ai/#intelligence
Luna is an extremely strong model.
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In my real world use Luna is as useful to me as Sonnet. And it gets stuff done faster and follows my instructions more closely.
Anthropic basically downgraded all their tiers when they released Mythos, no nah, Sonnet 5 is the successor to Haiku 4.x
They have no (other) equivalent to nano, so it makes sense that it’s much cheaper now. It may have been better, but it was also hell of a lot more expensive.
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.
As if ALL OF THAT doesn't represent inherent and crucial elements of judgement, and therefore"intelligence" itself.
We are not purely rational creatures, thank God. Sometimes those "limiting factors" you listed -- stress, peer pressure, hormones -- are crucial elements of informing the problem solving process and arriving at a decision or a solution that actually works.
All an LLM can do is fulfill a prompt, no matter how misguided, backwards, or incomplete that prompt actually was.
"Go jump off a bridge." Hmm. Dying makes me stressed out. I'm not gonna do that.
That's a bit like saying a tail is swayed by a dog, as if it could exist without one, or would have anything to do if it did.
Why does it matter? This is completely based on data produced by humans.
more like they were facing pressure from chinese models, and dropped prices and now their margins are squeezed
there is a ton of downward price pressure from Chinese open weight models
According to https://deepswe.datacurve.ai/, Luna at Max at it's previous cost was comparable in both performance and cost to Sol at High.
With an 80% reduction in cost that becomes a ridiculous outlier in efficiency.
> Seeing spikes like this makes me question about where the floor really is.
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?
Had to ctrl+f for someone saying this.
I typically do lots of mini calls for research (100s of millions or something in that ball park). Newer models made that absolutely impossible, and the fact that the older ones are starting to get deprecated made me switch to e.g. deepseek for some of my runs. We'll see if I move back after this.
Luna's now cheaper than 5.4-nano (for output tokens). That's a significant improvement.
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.
> The cost of nails has fallen by 95% over the last century
No it hasn't!
A century ago, some nails cost 2.5% of disposable income, and now the same nails cost 2.3% - only a little cheaper.
The cost of nails has remained remarkably consistent for a century. The problem is that you have ignored the depreciation of money.
Let's assume California prices and income and pick a bigger retail package of nails as you might use for building a house. The numbers used to calculate percentages: in 1926 a 50lb keg of 4" nails was $2.75 and median after tax income might be $108 per month. In 2026 a 50lb carton of 4" nails is $106 and income might be $4,516. Albeit I assume nails are now more readily available and the quality of nails is likely better; and perhaps I should have compared galvinised nail prices.
Until I stop getting downvoted for asserting that these guys are profitable per token, HN is going to continue being pikau face shocked at easily predictable things that any serious AI researcher would tell you, and has been telling you for years now!!!
They over purchased hardware.
This is very likely priced below recovering the cost of the hardware but still above operating expenses.
That’s ridiculous. Every major AI lab is compute constrained. That’s exactly why nvidia is worth trillions today. If OpenAI had a single extra GPU they’d be using it to run another training cycle or experiment for their next model.
What evidence is there?
I have no idea either way but one thing that detracts from these threads is folks claiming things as a fact without evidence.
sama literally just said they wish they had bought more. the price drops are almost certainly due to good old fashioned hardware innovation (wafer scale with cerebras) and optimizing hardware development based on model architecture and inference costs. other inference providers will try to do the same if they can.
https://www.youtube.com/watch?v=XDB5beon4DY&t=4m20s