"no homerS -- we're allowed to have one"

https://amphetamem.es/meme?id=the-simpsons_06_12_71&text=We%...

Yup, same experience here. I used it for one day, spent the next day fixing its lousy code, then went back to 5.6.

API price cuts were obvious once they made their announcement changing how usage is counted.

These moves all make sense when you take into account the enterprise market.

https://news.ycombinator.com/item?id=49889873

Shots fired, half the price of Opus 5.5.

Yes but it was underwhelming, so they seem to have rushed 6.1 Sol out. Also Opus 5.5 may have spooked them too.

Do you need to? Do you always keep up with all the version bumps on the software you use?

Sol 6 is in there? You may be on Enterprise where it didn't roll out by default and comes out in a week or so. (Which is a weird and bad change to their model releases.)

Not an AWS customer, I take it? :-)

That's relative to where you live.

Consumers are now saying the new pricing with lower usage caps is not so great. https://news.ycombinator.com/item?id=49896975

They may work for one of the big AI labs.

What they'll do to LLMs. Keep up

Outcompeted them badly. Sent Porsche packing and BMW bawling.

We can only hope.

The difference between 6 months ago frontier and now frontier in 3d modelling, graphics and video editing is night and day.

Not that they've hit it but that they are approaching it. The time to panic and steer the narrative is before you hit the iceberg, not after

People have been saying this since GPT-4.

Agreed. You can’t “brute force” reality, which has an infinitely large state space. A million monkeys won’t write Shakespeare and all that

"This machine-intelligence stuff is overrated, they are just using <insert particular machine-intelligence technique here>" isn't the resounding verdict it may have sounded like when you typed it.

Those version numbers don't necessarily correspond to equal increases in "difficulty", though.

Nvidia can start putting weights in silicon if model development slows down.

I think they are hitting compute restrictions. And buying compute right now can be 3-4X. And the costs are increasing. If they train a larger model and demand is high, that’s a lot of compute for Codex subscriptions, which is a loss leader for them. Especially Pro 20X which they just nerfed to 10X.

Another piece of evidence on the pile that the sudden panic and desire to "slow down" is because they're hitting the plateau on capability

I think it's more a token-cost-demand plateau. They've reached the scale and investor trillions to which they can't 10x the hardware cost of inference any more. They can't afford to compete by eating costs and there isn't appetite for more expensive inference.

So in order that they don't bankrupt each other they're looking for the legal cartel behavior coordinating a stop to growth by convincing governments to regulate them into stopping.

There's a lot of juice to squeeze in efficiency but only so much whereas it seemed like capability was going to continue to scale with parameter count.

Maybe it's good news for everyone that model capability is now going to scale on semiconductor cost meaning huge players are going to be very motivated to make semiconductors cheap.

It's not so much that they're hitting a plateau in capability, as we're saturating long horizon benchmarks and it's not greatly improving general usability. On the other hand, newer models have been amazing for people interested in 3d, graphics, video editing, etc. The difference between Opus 5.5/Astra and earlier models is night and day even if for many coding tasks they're not a revolution.

The game is already over

Another piece of evidence on the pile that the sudden panic and desire to "slow down" is because they're hitting the plateau on capability

People were talking about plateau for years already.

Is there anything that could happen that you wouldn't use as evidence that they are hitting a plateau?

It just seems like these claims are constant and looking back the calls of 'plateau' between 2023 and 2025 were clearly false, why should we think it's different now?

This is literally the plan, open weight models are something like 60% of token spend, and it will get worse. many companies now have model gateways where you can slot in cheaper models via cli for cheaper. we've been using glm 5.x and it's pretty close to SOTA frontier models.

it's also why there have been so many calls for regulation and slowdowns.

Pretty standard business to identify and compete on every axis (cost, speed, intelligence, etc). Often, nobody will be able to maximize every axis so you end up with a polyhedron derived from the axes where there’s a niche for everyone.

DeepSeek understands that. Grok understands it. Every other AI company thinks they need to be the best at everything all the time and it’s weird.

?! this model launch was around 10% of the dev day and the other time was spent on Dots and things other than models.

No because then I'll go to the competition.

Because it is more work?

Switching models is _very_ expensive in compute (you have to rerun everything from the beginning), and highly variable in cost. Cursor tried doing this for awhile, but inconsistent performance/usage means most users turned it off and pick models specifically.

I guess the question is, does the Dunning Krueger effect apply to models? The dumb ones might think they're up to the task.

These models have a knowledge cutoff that don't just prevent them from knowing about themselves (especially since most data about the model doesn't even exist until after the model is created), but they also don't know about other recent models. Sure, they can search and use other sources, even make some guesses based on the models they do know, but their default stance is more akin to "User asked about model X, model X doesn't exist, maybe it was an hallucination or mistake, let me do a web search...", but that assumes they have web search and are willing to spend tokens on it.

Personally I've taken to having a list of 3 to 4 models in default context with some ordering on which to prefer. Things like GPT 6 Luna is cheap very cheap, use it. Because otherwise the model will assume Haiku or such is the good cheap model to use.

The speed I'm having to update that document has not gone unnoticed.

No.

The old "the bigger number is better", GPT announces model 6.1, the obvious thing to do next is to announce Gemini 27, and after that Claudé 3000, then a flute album.

Not the person you're replying to, but judging by the emphasis on the cost of cached input tokens in the OP article, I'd guess it has to do with DeepSeek v4.1's KV cache efficiency. It uses <1000 bytes per token, so they're able to get 1M token context in under a GB of VRAM.

Which paper are you referring to?

No, we're pacing ourselves to have the time to evaluate the impact each new model could have, obviously.

[delayed]

Probably just the singularity, no big deal

Its a news cycle more than anything, and its ONLY going to get much, much worse. Daily releases, or multiple daily, 30-45, by EOY. Welcome to RSI!

Competition

If you look closely at various benchmarks, you'll see that often models will improve in certain areas while regressing in others. It suggests we're already at the point of diminishing returns.

Probably one of the factors. Signed up to openai pro a few days ago, deciding between openai and anthropic, then sonnet 5.5 was released and am wondering whether I made a mistake.

Luckily it's not a mistake as now we have access to . . . dots.

(and sol 6.1, it seems)

This is it.

It's because they need subscription money and interaction data and so keeping a version bump in the wings to stop the bleeding from your competitor's version bump is the logical thing to do. It has nothing to do with RSI.

jokes on me, I pay for all the subscriptions.

Anthropic’s IPO?

New models are distill from the actual unrelease frontier models. They are just giving us better checkpoints.

The initial response to 6 Sol was bad, and Opus 5.5 was definitely winning the public vibes war. Makes sense to rush something out

They're releasing Sol 6.1 because 1. Astra 6.1 got postponed 2. Sol 6 is shitty 3. They have to release _something_ in response to Opus 5.5

Versions is marketing, snapshots/minor variations are easy and the number must go up. Release timing is another OAI's marketing tactic.

RSI

Recursive improvement doesn't imply increased rate, another word for it is "iterative" but this probably sounds too boring for some.

It is the only way to reduce prices while making it look like a good thing.

Productivity is increasing as the models get smarter; we are ascending the singularity. I'm serious.

It feels like an updated 4.6. It's fantastic.

I'm not the only one

See, that's an/the issue. As soon as people start to flee to the improved model, they start to serve degraded models to keep up with the demand.

and seems like they're claiming Sol/Opus are not frontier (and only Astra/Fable are)

Mature training pipelines, plus ever expanding RL datasets of increased quality, and mega GPU clusters to finish training in a few weeks. Automated safety and reliability testing.

Aka "we made an oopsie last week and released what should have been called GPT 6 Terra with the name GPT 6 Sol"

It's relentless, isn't it?

GPT‑6.1 Sol matches GPT‑6 Astra at roughly one-fifth of the cost

Astra is a pretty impressive model. Excited to try this.

I love free market competition. We're getting insane advancements every day. I remember when llms used to cost an arm and a leg for decent intelligence

This is great. But maybe part of the motivation is that 6-Sol wasn't as good as initially advertised so they needed to tweak it. I felt a clear degradation in quality in some simple refactoring tasks vs 5.6-Sol.

Yes, obviously. They're both working to make it cheaper, faster, and better at different industries (3d animations, etc). The only direction they are slowing is raw intelligence.

The energy costs of the cloud computing required for gaming are substantially less in power

Yes but it adds up when you consider that just on Steam alone there are 200 million monthly active users.

You're not including the physical supply chain energy consumption of distributing video game equipment in this analysis. Nobody ships LLMs to big box stores and tries to sell them to consumers.

How many DCs are devoted solely to gaming?

An entire planet. Just Steam alone has one or two hundres million monthly active users.

you will remember that people have indeed been enraged with the environmental costs of things for a long time

Yeah? Show me the big movements against computer gaming.

Because people find video games fun, though I suppose there's some vocal people that think of them as bad for society. In contrast the AI companies are promising a torment nexus future.

I'd be curious as to how much of internet infrastructure is dedicated to gaming though.

Given that the number one cost of inference is memory and compute, and the incremental cost of each is energy, cost per inference is roughly proportional to energy consumption.

Likely orders of magnitude different, this is a weak whataboutism.

Look at Neuralwatt. They report energy usage with every call as well as aggregate statistics.

I want to energymaxx. Every home should have a nuclear generator for free limitless clean energy. Do not energysimp, we want prosperity for all we must energymaxx and invest heavily in solar/battery/nuclear.

A night of no sleep this one will be.

Okay, now price cut 6 Sol (and rename it to Terra again).

Sorry, I'm GenX. Growing up they showed us "Old Yeller" in the school gym every year like that was some kind of treat.

The misstep was naming it Sol - it was Terra-level all along.

If that were true, they’d have axed their margins.

Whether it was or wasn't, Terra's absence shows Sol has replaced it as the new middle model.

You need to use the Blender MCP. There is an official plugin for this now, so the third party one can be avoided.

I've only dabbled but yes with SOTA models it is very good at animating and really most Blender tasks you can think of. Certainly if you are coming at Blender at below expert level it makes it far more accessible and fun to work with.

There are still rough edges of course. But try the official MCP out with Astra and judge for yourself.

Have you tried fable? (I did small experiements and was satisfied, but maybe there are reasons to switch?)

From the results of a lot of YouTubers in the space, I think Opus 5.5 is pretty competitive with Astra in 3D. It's slightly worse at spatial detail but better at aesthetics and little touches.

After all the hype, I’ve been kinda disappointed tbh. Modeling specific models are so much better (eg. Tripo3d). Astra still models some janky crap for me.

The usage allowances are now insane, like they were when the Max plans were introduced. The $100 plan is usable again for real tasks.

If 5-10 minutes is enough, you need a more ambitious one-shot goal.

Can you give an example? For me I find that one shot prompts are pretty good it’s only when working with large codebases and complex, multi prompt workflows, that I find the real limitations of models

Yeah totally agree, people keep jumping in w/ strong views hours after release, eg: https://news.ycombinator.com/item?id=49045430

While I have no experience comparing this brand-new model, OpenAI themselves call it "near-Astra" intelligence. I set Astra and Opus 5.5 independently working on the same large research/coding task in an experimental project (doing NURBS surface modeling stuff). They had the same starting repo state, same task packet, same test suite to try to meet. I have the $100 plan in both.

Astra used 215% of a week's budget (I burned 2 free resets) and took 13 hours. Opus used 20% of a week's budget and took 20 hours.

The timing comparison is not that interesting because the wall-clock speed mostly reflects how often they ran the (large, slow) test suite, not their coding speed. Although in the past my gut feeling is that OpenAI models do generally respond faster.

The quality of their implementation was more interesting. There turned out to be a bug in one of the unit tests the agents were trying to pass. Opus interpreted the natural-language requirements from the task packet, found the test bug, and fixed it. Astra tried hard to solve the problem without altering the test suite. In practical terms Opus got much, much farther into a useful implementation. Astra was still stubbing out and faking critical parts of the implementation (B-splines) and since it ultimately couldn't pass the full test suite, finally gave up on its implementation. Astra wrote some useful tooling in the process of its efforts which I ended up integrating into Opus's version of the code, but otherwise its approach was behind.

Now, this is just one comparison in one domain, and arguably Astra's strict adherence to the tests as-given is a good thing. But Opus wasn't merely loosening the rules / moving the goalposts to pass, it spotted an actual bug, and was more successful at doing what I actually wanted. And the cost difference was Astra-nomical.

Out of curiosity for an interpretation free from my personal bias, I also fired up fresh agents to review the two repos. Predictably, an Opus agent thought the Opus-written repo was the better basis to build on, and an Astra agent thought the Astra-written repo was the one to keep. They were not explicitly told which was which nor did the commit trailers say, but I assume they can tell. However, after doing this twice each, I saved the 4 review reports into another folder and did yet another meta-review of the 4 reports, so each would see the arguments and critiques both directions. In this meta-review both Astra and Opus converged on preferring the Opus implementation.

For my personal experience, antropic model have better user experience except for 4.7 and 4.8 though. 4.7 and 4.8 feels like expensive downgrade of 4.6 to me (I didn't know why these two should even exist)

However it's less willing to obey your instruction so it's less usable for general runtine flows.

A pity I have to use claude code to try this, that I can't use the tools I know and love and have built around (opencode).

(I did use some CC for Fable when it came out, and it was... ok. Not the worst thing ever.)

  > Ask for something difficult from GPT-6 Sol and Opus 5.5 and watch what each one does.

That's far too vague. I found Opus to be terrific at coding, but human text just seems so robotic with it. OpenAI models used to be the prototype for robotic text, but lately I've been finding them much more natural. What is "something difficult" in your workflow?

Suggest trying it out yourself: Ask for something difficult from GPT-6 Sol and Opus 5.5 and watch what each one does. The difference is stark.

Oh yes, I know GPT-6 Sol is ... quite not up to par. At least it's not as bad as GPT-5.6 Terra I suppose.

Based on my benchmark[1] it is the same price as Opus 5.5 and just as capable.

1 - https://bench.killswitch-lang.org

50% cheaper cache than GPT-6 Sol will get you far more mileage on Codex.

Cache doesn't help you much when you are compacting every 5 minutes...

I was shocked at how quickly I ran out my $100/mo subscription with a single agent (sol medium).

The backdrop being deepseek offering 1% (I remember it was ~1% when 4-pro first came out early this year - 4-pro is now removed) / 2% (current for 4.1-flash).

Astra 6.1, this is Sol 6.1

That model was implied to be GPT 6.1 Astra, not Sol.

I see where you are coming from. But 6.1 Sol seems like a new frontier in pricing, not intelligence. I do think the deceleration stuff was mostly bluster, but I don't think this release in particular contradicts it too much.

I get decent results telling it to use Luna subagents for implementing commits.

Cache is priced at $0.1/M, 50% as sol 6 and sonnet 5.5.

$2/10 is pretty cheap for a frontier model...

I stopped using LLMs. I shit you not. My life got better.

You live in a ping-pong.

Why they are not even benchmark model against Anthropic or anybody ?

GPT 6.0 Sol was so terrible—I wonder if 6.1 Sol will be good?

Is this due to a similar safety concern or just because it's not ready yet for one (or more) of a myriad of possible reasons?

The coverage around 6.1 Astra seems deliberately playing into the dubious, recently headline "safety" narrative in a way that feels distinct. But you may be correct in which case, I would take the correction on board and maybe suggest a different alternative.

Although in theory if OpenAI was boycotted in this way the market pressure would force them to release. Then everyone moves back over there. Then Claude faces the same pressure. So even so, I think it could still work even if you have to trade off who you are boycotting from time to time.

Without more details on the credibility of the "safety" concern this seems like a totally coherent action for customers to take. We shouldn't put up with teasing.

It's just vibe versioning, right? Fable 5 is a beloved product, it gets a .1 bump to feel close. Opus 5 and Sonnet 5 had a mixed reception, they get a .5 bump to create a sense of distance.

After what DeepSeek pulled with V4.1 Flash I've given up on trying to map LLM versions to semver.

6x

i'm watching the keynote

Still not available to me

Other comments have already addressed this.

the race to the bottom on models is well underway. huge IPO's only really make sense for DC/HW lockups, and going vertical.

It is hard to trust these scores. GPP 6 Sol has been so bad for few days.

They need to fix Astra first. My main issue is with GPT in general is that unless steered it goes into building AI “sloppiness”/machinery that is not “needed”.

The good part is that this kind of behaviour also makes it good to find subtle bugs or debug issues that Fable/Claude just cannot get/fix even when you point it.

How does this jive with the exponential growth claims? Theoretically sol models are better than the 4 series models I was using at the beginning of the year, but in practice the results don’t seem to be much better. They always nerf the models over the course of the release so it _looks_ like the next version is better but I haven’t seen actual capability growth since ~January, and I’m pretty sure that was all tooling/harness improvements.

On r/codex the sentiment seems to be quite wide-spread.

When GPT 6 Sol & Luna were released, everything went down. I have been running Sol at max thinking and it is about the same as old Luna with max thinking, give or take. Sometimes feeling even dumber. I can't trust it to do anything big alone anymore without babysitting.

Opus 5.5 is so good that I don't want it to be replaced anytime soon. Stop training models, Anthropic, and just serve this thing without regressions for a year or three, can you?

DeepSeek and GLM made it impossible for "sota" to price any way they want.

GPT 6 Sol is obsolete after only one week! I am glad that they are not afraid to update the models more frequently. The Navier-Stokes thing revealed that it took them only a week or two to train a model more capable than Astra, and I want the pace of public releases to keep up with that.

Astra Light is the default option in the UI, so likely a bug

Came here only to check if the pelican spam has made it to the top again.

OpenAI is deeply unprofitable, particularly on those pro plans.

The only way is for prices to go up. Way up.

This is pretty typical product positioning. You want to sell to both high-end and low-end users, so you offer products at a few price points. Then it turns out that that middle is a much better fit for most users. So you start making the middle a worse fit to push most of those users into the higher tiers.

Long term, this only works if you have a non-commodity, and if the higher tier is actually more profitable. We'll eventually learn whether both are true. For OpenAI right now, it's probably enough to just increase revenue, even if the higher tier is even less profitable.

I'm ok with whatever price they give out given they are not a monopoly and have competition, the lock in is minimum for me. This means they have legit reasons to send us this price plan. I don't believe they would shoot themselves in the foot when there is cut throat competition (Claude/opensource) out there.

Lastly, I'd like to actually use it in the real world to see how far my plan goes or if its unusable.

Alas, I did enjoy burning investor money on my taxis, movies and tokens.

Well, here is a breaking-news for you: the 20x from Claude is not a 20x on the weekly usage, it's a 20x on the 5h usage, while the weekly usage is simply double the $100 plan...

Basically OpenAI aligned with Anthropic on the weekly usage with the caveat that OpenAI doesn't have a 5h limit.

Very risky to do so especially considering how well is opus 5.5.

Let’s pray Chinese models are not banned.

It's more like they released GPT 6 Sol too early because they were under pressure and now they are releasing the real version. You cannot do anything more than minor post-training in a week.

Implying they don't have like 3 or 4 "models" (different quants, post training, plain renaming) on the back burner at any point in time to do exactly that

That seems like...a big deal.

They can release a new version every day if they wanted to. The question is whether or not the new releases provide substantial improvements or not. It's not hard to just go through the motions, bump the minor version, then make an announcement to rile up the users who don't get that none of this is standardized or regulated in any way and it's literally all made up by the company trying to sell them the product.

Typically how long does codex take to update with the right model metadata for the release of a new model?

{"type":"item.completed","item":{"id":"item_0","type":"error","message":"Model metadata for `gpt-6.1-sol` not found. Defaulting to fallback metadata; this can degrade performance and cause issues."}}

OAI and Anthropic are locked in an unwindable race. Fighting over the same pool of users while cost is growing at geometric pace.

Impressive improvements, but GPT 6 Sol came out 7 days ago, and this one will behave differently. The panicked pace is becoming a liability, maybe they should have waited and released this as the 6.0 release

I guess they released this because GPT-6 Sol was underwhelming, they didn't even release it to ChatGPT. It was basically GPT-5.6 Terra for the price of Sol. However, who doesn't like price cuts? Astra for the fifth of the price? Wow, OpenAI have been quite generous recently, I still have not forgotten their 90% price cut with GPT-5.6 Luna, and now this? Astra was truly a milestone, and now they are offering similar "intelligence" for cheaper price. Incredible.

One thing I wish was better communicated is the mileage we get for our subscriptions. I do not fully understand how much usage I get with each model and their reasoning effort on 5h and weekly limit in Codex. I am asking because I know switching to Astra would consume my 5h usage limit quite rapidly, so I avoid it. If I knew how much mileage I would get from each model and respective reasoning effort, then I would be able to plan my workflow better and know when to upgrade model for a task. In almost all cases, GPT-6 Luna (XHigh) have been enough. That's why I appreciate its discount, because its dirt cheap, yet highly capable.

In other news:

In the coming days, we’ll also offer GPT‑6.1 Sol Ultrafast , with up to 8x faster token generation compared to its standard speed in Codex.

It’ll be interesting to see what happens to the economics of this business if we hit a wall on peak intelligence but keep finding cool ways to lower prices.

Sam Assman needs money to buy a new super car or private jet?

Do these benchmarks have any meaning anymore? And do the announcements seem less exciting now? (Not taking anything away from the advances we are making but it seems more incremental now?) The reliable way to tell if you'll like a model is reliable collage/X reviews to gauge a model's capability and then trying it out to see if you like the style.

The last time a model announcement felt like a leap in capability beyond other things out there was Fable - which was promptly taken away. Sol and recently Opus 5.5 were strong because they approach that capability with a lot more efficiency and don't blabber incoherently (looking at you Opus 5.1).

Deepseek is a workhorse for those who prefer open and API usage. Other than that the model announcements all just seem like a blur and quite interchangeable but I wonder if that's just me tuning out or do others feel the same way?

Surprisingly (or maybe not) it matches the performance of Astra on my benchmark[1], but is much cheaper. It is also head to head with Opus 5.5 on both the price and pass rate, but edges it out slightly.

1 - https://bench.killswitch-lang.org/

Still too expensive. Needs to come down to $1 or less to compete with china.

Sol is so good, honestly - the sweet spot for me. I've only ever found it stumbles when you don't give enough direction. But for idea execution - Sol is the GOAT.

I’m a bit disappointed with Sol 6.1. I suspect they didn't show the benchmarks and test results because it would have been embarrassing to reveal that their flagship model can't compete with the capabilities of Sonnet 5.5. That said, I still think this model is useful for a great many things, but it looks like Anthropic has the upper hand this time.

Ok, but we need 6.1 Luna soon. 6 feels worse than 5.6 in our agentic use case.