mchusma

I’m skeptical of the result as claimed BUT I really do think model diversity really helps.

Depending on the feature, I often these days have opus plan first and work with it on business specs, then have it reviewed by 1 or all of the cheaper models: haiku, sonnet, terra, sol, grok, muse spark,

Then take the updated plan in opus and forward to Astra and Fable, once you have the final plan in place execute.

Then have the same batch review code after created.

I am surprised how often one of the smaller models catch things. Despite so many things having eyes on.

You should also just pay $100 yourself? I think the bigger issue is just ease. They should deregulate these so they are simple to get

Seems roughly the same story to me. A bit more competition but good growth and healthy multiple of compute.

Just to be clear, I think the current mapping for OpenAI is something like:

xlow = Luna Low

low = Luna xhigh

medium = Terra medium

high = Sol medium

xhigh = Sol xhigh

Max = Astra something?

I actually do have a recommendation, which is to do something like a new mode (i know i know): Auto xlow Auto low Auto med Auto high Auto xhigh Auto max

With an optional flash mode.

Then as you release models, you pick the model and thinking level that you believe is pareto optimal for cost/performance.

So this auto updates each release, like Terra was probably off this list entirely, but maybe this would appear now in Auto medium or something like that.

I know from looking at charts that each model seems to have sweet spots (like I always use this model in this thinking level and if i want to go cheaper i go here). If you picked those for us, it would be interesting. I do think you should keep the model families for those wanting more consistent experiences, but for many devs they may want to just pick auto something and have it update over time. I think nice if it actually just said in parenthesis what it was, something like: Auto Medium (Terra 6.1 High)

I am not sure if everyone would agree here though. I'm obivously advocating for more naming schemes, and i understand the irony.

I actually think just buying the digital copies is pretty great. I have a big library of movies, and I have muse check for digital discounts, so most movies i have gotten were <$10, often <$5. For kids, this includes basically every animated disney/pixar/illumination movie ever. You can get recurring hits like Gilmore Girls or friends on sale sometimes for like $29, the whole series.

I did cancel Disney+, For the cost of 4 movie purchases or a series a month basically.

I thought about Jellyfin, but it was worth the convenience cost to get them easily viewable.

Movies Anywhere helps because i just buy them wherever they are on sale and watch in 1 place.

Have you used loco? Curious any thoughts.

I agree with what this paper generally says...but doesn't it seem like something anyone developing ai systems already do? Learn from past runs to update your skills and system prompt etc? I know I have done this a lot over the last year+.

I think the MAIN thing on specs is to just make sure you have them, and get the models to ask you clarifying questions. You don't really need a skill or process for that, just ask the model to ask you questions on a spec.

I am not so convinced yet that subagents with haiku and sonnet really deliver better results versus opus, nor if the speed really matters that much if you are truly autonomous. I mean he is talking about saving like 20 minutes here. There are 24 hours a day, you are much more likely to be gated by product specs and knowing what you want and good ideas than engineering if you are hammering things now.

I do t think it was a fiasco. I thought the updates were good. They should do more work on benchmarks. There has been good work on testing benchmarks for example to ensure they are accurate, and as issues arise they should change the benchmarks. The point of AA and Arena is trying to understand how to think about models.

Porsche. “Aluminum key”

Tesla roadster. “Drives upside down, flies, does flips, 0-60 in 0.81s”

I agree, returns are where amazon crushes it. I feel like there should be some startup just focusing on making the perfect returns product for small brands. Just do anything including at home pickup and then figure out how to optimize later.

Website says its available on Pro too, but i have pro and can't access it.

The average rate for job application to interview is roughly 2%...so while there have been some boom times and professions where this is different, I think OP is going to have a rough time giving up this early. I know it sucks...but applying for jobs sucks.

Or just loosen supply side restrictions so much that houses typically don’t go up in value.

Yes. And to clarify we should make the interchange rate being charged above and beyond the list price the default. I dont think merchants will care about rejecting much it’s just consumers with 3% interchange fees will have to pay it and those with 0.1% get it cheaper.

At a glance, looks competitive on the pareto. I hope it retains the flavor of sonnet 5.5 and opus 5.5. Both are extremely good and productive. I have had a lot more issues with gpt 6/6.1 and getting what I want out of them.

Something looks off in artificial analysis. Benchmarks aren’t everything, but not even close to the Pareto https://artificialanalysis.ai/models/mistral-large-4?cost=in...

I guess lots of token usage.

I’m guessing this is considered something like a C grade from Google if they are being honest with themselves.

After being nowhere near the frontier for a long time, they are pre announcing a model that ranks 3rd, roughly on par with models today that are cheaper.

Good for them to think about releasing to stay in the frontier game.

(I do think 3.7 flash was a solid release, so they are around the conversation. And their image and audio and live models are good)

Cool. But until I can play with it it’s vaporware. I will say I think 3.1 pro is still really good for legal type things. They cooked with that one.

Either mathematical progress helps advance society, in which case progress is a good thing.

Or mathematics is more like a hobby, and while ai may spoil their fun, they need to move on like chess and go players.

47 is my crossover for serious things (e.g. Grok 4.7 is below the line and GPT 6 Sol is above the line). I mean, Opus 5.5 is way better, but GPT 6 Sol still gets the job done for anything that doesn't require design thinking.

Although I do think Luna 6 max is ok for some basic things, would never use it for coding myself.

Jev is orders of magnitudes cheaper and faster, its a very interesting new paradigm. (Jev pioneered this about 2-3 weeks ago).

My guess is that haiku will be a mid release. They don’t seem to care to compete for the low end. Something akin to gpt6 sol level intelligence at $1 / $5 pricing. Then not release an update for 6+ months.

I feel like sonnet is priced too close to opus right now. If Sonnet 5.5 were half its current price it would make sense to use. At its current prices, I won't use it in applications (I would use cheaper models) and I won't use it in my subscriptions ( just use Opus instead). At least that is my initial reaction.

I agree I don’t like having both engines, more to fail. BUT I could see a world where you basically have basically an EV with a smaller battery pack (150 mile range) and basically a swappable $300 generator. I saw swappable so that if anything goes wrong they just swap it for $300, and credit you back async any recovery value. Just electricity generation from it, for rare needs of longer range. I think in theory, it could be a good way to get a cheaper EV supporting rare bursts of range and low maintenance cost.

But I’m not even sure regulators would allow such a design. Even then I’m not sure it’s worth it.

But I am totally done with ICE car maintenance. I want nothing to do with it anymore.

I think for a sense of scale if this works as the SpaceX team wants, they will be able to launch more mass to orbit in a single day than all payloads ever before 2022 (rough math). And at a cost roughly 1/100th the prior average.

This I agree with.

Right now, I do actually use OpenAI's gpt-oss-safeguard-20b for somethings, was released 11 months ago, and is $0.075/M input / $0.30/M output now. I could see this model being in fairly widespread use at 10x speed and 1/10th cost if it was introduced today. Meaning, that for some usecases (moderation) i think dedicated chips can pan out today.

But for more general models, its tougher. Gemini 3 pro was launched in November, if ASICs brought it down 1/10th in cost, it would be $0.20/$1.2. GPT 6 Luna is $0.1/$0.50. Luna is better at a lot of things, but not everything. So 1/10th doesn't really make the ASICS investment worth it in my opinion, but if it brought it down to 1% ($0.02 / $0.12) it would be a really compelling model with a lot of use.

BUT, do i think something like Luna is probably generally capable of doing a huge amount of knowledge work. So if Luna came out at 1/10th the cost a year from now, it would probably be compelling for a while.

It all depends on the rate of improvement in cost/capability.

What? The report is about ai performance and cost.

My initial takeaway is that GPT-6 is mostly a lower cost win, for Luna. GPT-6 Max is an upgrade on intelligence too, but its mostly a cost play (which is great, not complaining).

Overall, I expect for most people think the winner of today was Anthropic. I personally am preferring Opus 5.5 at medium over GPT-6 Sol Max, in very very early tests. Similar price range, more capability.

But competiton is great, these are solid releases by OpenAI today.