You can use my script p-noteq-np.sh too if that helps.

Canary tests on my data showed I needed xhigh to get good results, but they are good.

Luna is good as an end user model for simple tasks like classification, but not as a coding model. Also do you mean Gemini 3.6 Flash? 4 doesn't exist, and Gemma 4 exists but doesn't have a Flash option.

don't expect it to be opus, but luna does coding just fine for its size/price

This is annoying. Similar with "context poisoning". LLMs can be highly productive... until they're "poisoned" with contradicting inputs / guidance.

That's not necessarily a joke; the article proposes exactly that.

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

> 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".

Enterprises implemented spending caps and inference providers are lowering prices. Seems they are jockeying for market share.

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.

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).

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.

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.

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 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.

Luna is comparable to Haiku, not Sonnet.

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

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.

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.

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

Good observation! Kudos

It's cheaper currently on many of the inference providers.

Personally, I'm having surprisingly good results with DeepSeek 4 Pro at home, which is very good value for money: it's not as good as Claude / GPT 5.6 (I have Co-pilot license at work), but it's still really useful for code reviews, validating thoughts, and especially designing / writing unit tests for new (and old before refactoring) functionality.

And it's very cheap per task. (Flash is even cheaper, but I've had issues with that on more complex tasks where it starts forgetting things and arguing with itself "but wait, let me read the function again").

If you optimize program A and manage to wring out a 1% improvement, and I optimize program B and improve performance by 20%, you can see the problem with trying to infer anything from those two numbers.

Edit: searching for the story now, further bolstering the point is that was 1% in training time [1], and the openAI claim is 20% in end to end inference cost. This is a bad comparison.

[1] https://deepmind.google/blog/alphaevolve-a-gemini-powered-co...

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)

Where are you going to apply to with that resume that’s a step up from your current job though?

In this case, and I don't mean this critically, I guess it would technically be, "Instructed model to find efficiencies... reducing inference cost by 20% saving company x billion dollars per month."

I have no doubt that further work was required to enable this, but it's still very cool to be possible to say that.

Contributed to. Can't be some IC who made a few nice PRs

Deep research is basically a LLM with web search, and a "work really hard" goal-orientated prompt, and some output formatting suggestions.

It's a feature offered in ChatGPT and other platforms, though probably gated behind paid subscriptions.

Here is a rough approximation of the pipeline I use:

Phase 1 - Run X copies of Luna in parallel over the user's prompt. The purpose is to generate a diverse set of hypotheses.

Phase 2 - Run Y copies of Terra in parallel to investigate the hypothesis results, with each receiving them in a randomized order.

Phase 3 - Run 1 copy of Sol over investigation reports.

The goal is to ensure that the agent covers more initial starting points before presenting a final conclusion. If you only run a single copy of Sol and it hooks onto something wrong, it might not recover.

Anything related to reading and interpreting the environment seems to always benefit from the addition of more agents to the search party, assuming you have some rational way to synthesize their results.

Taking actions that mutate the environment is a different story. I think this is where you run into diminishing returns very quickly. You generally want one strong agent to act given the results of all the searching that was done. If the plan is clear, you don't need a genius model to execute it.

Wasn't it down last weekend?

This doesn’t quite count as “API”, but OpenAI’s roll out of OAuth device code authentication was poor, to say the least.

It's still 50% off apparently. Listed as $0.10 for input (original price was $1.0 without this reduction or sale)

Depends on the reasoning effort, see https://deepswe.datacurve.ai (add Luna via model selection drop down, if it's not shown by default)

Smaller models are great if you are doing targeted changes in existing codebases. Don’t expect to use it for creating complex architecture from scratch or do major refactors. The larger the context, the greater the drop off will be.

According to the link I mentioned above it's roughly as good as GPT-5.4. Haven't tried it in practice yet.

I bet it must be better in some contexts and worse in others.

You'd be surprised at what Luna can do, especially on xhigh or max. It's capable of working overnight, usually productively, just like Sol.

Haiku 4.5, on the other hand, is comparable to performance to Gemma4 31B (with working tool call formatting) in my experience, and Gemma4 strongly wins on vision and multimodal.

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

Per Artificial Analysis:

- Haiku: 30 points

- Luna Medium/High/Xhigh/Max: 38/46/49/51 points

That's a massive difference:

- 30 points is Gemma 4 31B territory

- 50 points is GLM-5.2 (744B) territory.

luna is way better than haiku 4.5

Game play bots (monsters, commanders maybe) would be really cool. But it needs long term support and probably local AI instead.

Good points. Though GPT 5.5 Instant does beat out 5.6 Luna non-thinking 34 to 27 on Artificial Analysis Intelligence Index.[1][2] This could mean it is optimised for non-reasoning perfomance. So even if beaten on cost and performance, it could still be on the pareto frontier for speed.

It probably costs them a substantial amount of money everyday to not replace Instant with Luna, and the people who want larger models will upgrade regardless of whether they get Instant or Luna on the free tier. It is unlikely the amount of people upgrading just to have latest models would be enough to offset the costs.

[1]https://artificialanalysis.ai/models/gpt-5-6-luna-non-reason... [2]https://artificialanalysis.ai/models/gpt-5-5-instant-05-26

But they still have to cover compute cost, and they already committed to more than $650B in infra expenses for 2035

is it? i don't know how we measure these things, but here's one measurement that says v4 pro is better than luna: https://artificialanalysis.ai/models/comparisons/gpt-5-6-lun...

presumably it's a much bigger model

They're supposed to bring 5h today.

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?

I maintain this meta-benchmark leaderboard: https://metabench.organisons.com/

With this new price change, Terra does look pretty Pareto’ed by Luna.

On agentic coding, pairing Sol Medium for architecting with Luna High for coding does kinda make sense. But beware that architecting can be very read-heavy, and Sol is a bit read-pricey compared to Terra.

So, does that mean they're selling tokens at cost? Or that they realized they were doing 180% more work than necessary and they're passing the savings on because "your margin is my opportunity" and this is a dog-eat-dog fight, but 80% margins are nice, no one needs multiple hundreds?

I was metaphorically hit in the head with the brick of already provisioned computing with a lot less demand than anticipated by multiple MoUs.

I just gave you the ROI on a retail blackwell card, the math checks out, particularly on overpriced Haiku and Sonnet, what do you mean by "you're just fighting economies of scale if you're going trying to beat a datacenter on cost" ?

Local LLM argument was always ideology first and never ever about economics.

This doesn't seem correct.

Estimated final electricity price for large industrial customers in energy-intensive industries:

USA 50 USD/MWh

China 68 USD/MWh

https://www.iea.org/reports/electricity-2026/prices

I don't know, non of the chinese models I use are served from China. And they are still cheap.

there is mixture of experts which is also another routing. So, simple change in prompt can be a big difference.

Hang on, I need to create a Twitter account so I can ask Grok

nerfing*