"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

Or a German car company?

What they'll do to LLMs. Keep up

Outcompeted them badly. Sent Porsche packing and BMW bawling.

We can only hope.

Just because there are new capabilities, doesn't mean they've pushed passed the plateau, they've just expanded where the previous solutions work.

We've gone from 80% in some places to 80% in some more places.

The whole "pace the frontier" thing originated from Anthropic.

With Opus 5.5, it doesn't seem like model improvement is plateauing. And Fable 5.5 will likely be dropped this or next week.

You can only imagine what they've got going internally.

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.

Correct, the benchmark became exponentially more difficult as it progressed from pattern matching puzzles to games.

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?

Those points were true at the time and most are still true now. But they aren’t predictions.

- it’s correct there isn’t much fresh data anymore

- it’s correct that compute is scarce, that was 100% the case and a huge issue at the beginning of the year, it is better now but still scarce, and hardware is now way, way more expensive

- it’s correct the finances don’t make sense

But there is no way to know when a bubble pop, because it’s a psychological phenomenon across an extremely complicated distributed system (ie the stock and bonds markets)

Yup.

I see posts about OpenAI and Anthropic latest and don’t even care looking at what they do better. I just read the comments here.

I use DS4.1 Flash and GLM 5.3 Flash, pay peanuts per day and get more than acceptable results.

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.

That makes sense. There's not really much else you can say about it.

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.

Edit: https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash/blob/...

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.

I was working exclusively with DS 4.1 Flash until Opus 5.5 got me back to a sub. I was disillusioned with what was available.

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.

Maybe process maturity too.

Like think about a software org with good CI/CD versus one without. The mature org can do consistent incremental releases because each one is safe and low overhead, the messier org will do fewer big releases because each release requires a big effort on its own.

As model developers mature we might expect to see more frequent point releases rather than the big bang evolutions.

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 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.

There are movements against consumerism and the environmental impacts of industry in general. Greenpeace is over half a century old.

The differences with AI are: 1) we are starting off (mid 2020s) from a baseline point of already being in a hopelessly shitty situation, past the 1.5C warming target; and 2) Electronics, chips, data centers etc were already a thing for a long time, but industry took _decades_ to ramp up production to pre-AI levels, and these things are used everywhere for a huge number of things. Now we're consuming electronics/data centers/water/power at an unheard-of rate, and for a single purpose (AI) with questionable benefits, besides the private interests of a handful of people.

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.

I don't think video games consume nearly as much power. A PS5's power consumption is apparently around 200W. That's not enough to run even one GPU, let alone the armada it presumably takes to run Astra.

Even then people do care about the power consumption of non-AI things. Look at the energy label on your TV or tumble drier for example.

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.

Yes I do. I've got a spare desktop that isn't too efficient (probably ~100W idle but annoyingly I've lost my power meter) so I don't leave it on even though I would like to use it as a server.

Laptops use very minimal power - you don't need to worry about them. If they didn't their battery life would suck.

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. Both were asked to use lesser sub-agents for implementation grunt work at their discretion (Luna, Sonnet) as long as they manage and review the output.

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 gave Astra a hint from Opus and permission to change the test in question, which it did, and got a bit farther, but still ultimately didn't produce a working implementation (to be fair, Opus's was not completely working either, but was closer). I then 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.

I think it’s one of the reasons why you often see people decrying the lessening capabilities of the models a few weeks later, despite there being 0 proof of any changes, and evidence of the models staying the same from sites that track it.

They form these super strong opinions after a few prompts, then face reality over time.

People have been talking about how good whatever model is at “complex” tasks since the beginning, never mind that all of those models are now outperformed by Luna which many people consider unusable for complex work.

They’re comparing against the previous model, not the newly released one (6.1). Why do that on a thread about the new model, I don’t know.

benchmarks.

For me Anthropic models from 4.7 to 5 including where bad and ate tokens like crazy. Task delivery was worse than GPT 5.6 and token usage was 2-3x higher.

Looks like 5.5 is the new 4.6

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.)

You HAVE to have a set of personal evals for each class of task you want to use models against at scale so you can test plausible candidates and compare output on your work against your evals.

There is way too much subtlety in what does and doesn't work for a given problem, context/prompt, tool set and eval. I can tell you Fable is generally better than Haiku, but comparing similar tiers really does depend on your exact context.

OpenAI models used to be the prototype for robotic text, but lately I've been finding them much more natural.

This was the biggest thing I noticed in the 6 models; their conversational prose is dramatically less grating.

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.

Are you comparing Opus 5.5 with GPT-6 Sol or GPT-6.1 Sol? Because they are different models.

This news and thread is about 6.1 Sol, not 6 Sol. You haven’t even had time to do a fair comparison yet.

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

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

you can config codex to compact at a higher context limit

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.

hopefully the answer doesn't include an increase in cost/decrease in usage.

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

8x for subscription, 6x for credits/enterprise: https://learn.chatgpt.com/docs/agent-configuration/speed

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.

They should etch it into an ASIC. The first model worthy of that honor.

Sol 6 definitely feels kind of dumb and worse than 5.6

Astra seems better though.

Showing one potentially saturated benchmark doesn't necessarily fill me with a lot of confidence in the coding results.

In my experience, no. There’s no way to know though. The whole conversation and industry are a combo of benchmaxing, faith, and mysticism.

Since like last December I haven’t had any issues getting work done with whatever the latest Anthropic or OpenAI models at the time were. Tooling and models have only gotten better since then.

That mirrors how disappointing Opus 5 and Fable were, for anything beyond one-shotted tasks or shiny demos. Maybe OAI is just a step behind Anthropic? Opus 5.5 seems like the real deal again, consistent good results on large, complex codebases.

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

I had it write some code the other day, boy was it awful-looking compared to 5.6. Worked perfectly, but ugly nonetheless.

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.

It really does look like OpenAI is trying to gradually get rid of their subscription plans. Every week there is noticeably less usage available to them while each new model release boasts substantially cheaper API token pricing. If this continues then the two pricing models will eventually be at parity.

The 200$ plan was appealing because you got 4x usage for 2x the price.

Now, as it's linear, it makes much more sense to downgrade to 100$ OAI and pick up a 100$ Claude sub. (without doing the numbers) the usage should remain the same, total paid the same, but having access to best of both worlds. It should be a win for the user, and a loss for OAI.

With this in mind, it sounds like a fumble by OAI.

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.

I'm describing what I got from 20x Codex vs Claude 5x. Codex is just not worth the money, at least for me. What's flipped is the value you get for each of those

"For antitrust reasons, it’s helpful for the US government to mediate or at least enable these discussions — they don’t need to participate, but do need to issue a narrow waiver for certain kinds of safety conversations. " - Dario a couple weeks ago.

Yes, he was talking about safety, but IMHO they're likely already IMHO pushing the boundaries of cartel type behaviour. And they will use safety as the cover to make it happen.

I suspect we'll see serious price fixing and the DOJ do nothing about it because of the inroads these people have with the Trump regime.

Whether that survives contact with Chinese open weight models is hard to say.

Opus 5.5 is significantly better than (..) Sol 6.1

Come on .. this is barely released and you can already make that assessment?

And no, the $200 Anthropic plan is not significantly better than the $200 OpenAI plan, it's just the same Marketing non-sense and anybody shall now rather stick to the $100 plan of both of these provider if the monthly budget is $200. Anthropic doesn't have a Luna Max equivalent, and frankly Sol 6.1 is yet to be thoroughly tested.

If you've used both you know the OpenAI plans don't compare to Anthropic plans _at all_. Claude code subscriptions are probably worth 4x as much in API spend compared to the same OpenAI subscription tier.

According to the company, existing subscribers will keep their current limits for a time, and will later receive a one-time credit to help them make the most of their new reduced allowances

Might want to hold off on canceling and continue to bleed them dry until the nerf hits

You think these guys care about risk?

For consumers they may as well buy GPUs and run local models. The cost is same over a year or two but infinite token usage, they get to keep the hardware, and local models continue to improve over that time too. I can't justify $200 on SOTA models for a personal subscription after Qwen3.8-27B. And it's only getting better from here.

Let’s pray Chinese models are not banned.

At $500 per month, it's cheaper to just buy GPUs and use local models.

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.

People said Astra was a gut punch and that Anthropic was reeling. (Opus 5 was almost universally panned)

The best thing is that we benefit from these constant back and forth gut punches :)

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?

I fully believe that these models perform better in benchmarks versus their predecessors, but in real world usage inside of real, production codebases? They feel just as flawed as ever. I honestly have not seen any significant improvement in a few months. The last thing where I felt "wow" was `/fast` mode and Deepseek.

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.

where's the pelican?

I can appreciate they show that Opus 5.5 is objectively better intel. and cost wise on multiple benchmarks

At the same time, OpenAI is also making its existing $200 Pro plan less appealing. In Codex and Work, $200 Pro subscribers will see their included usage decrease from 20x of what the company offers to Plus users, down to 10x of that same allowance. In ChatGPT, meanwhile, GPT-6 Pro message caps will decrease from 200 to 100 per week.”

Fuck altruism, ammi right? lets make money, gobs of it by screwing the middle users as much as we can to push them into just two tiers: Ones that use it for recreation and others that pay through their noses.

This is the first time I've seen praise for GPT 6.0 Sol: it's widely disparaged on Reddit and here in the HN comments too. My own experience likewise shows 6.0 making loads of silly mistakes, both for things 5.6 Sol is good at and things 5.6 Luna Xhigh is good at.

Hardly any comparisons to Opus 5.5, which means it's not great

3.8 flash is more expensive than sol 6.0, google uses a LOT of reasoning tokens

I wonder if this is the reason GPT barely works now and some chats have become not accessible.

There was a model called Astra-Minor, found in the files a few days ago. I assume Sol 6.1 is this, as a last minute panic rename due to Sol 6 being underwhelming while Opus 5.5 turned out really strong. I can't really explain releasing Sol 6 in any other way, especially mere days ago.

For most sane people, OpenAI is the way to go... A lot of usage with very good models, but you know that Anthropic is laughing all the way to the bank with Opus 5.5 being "the best" model right now... There are a ton of people (and companies) that will just refuse to use anything else than the highest benchmarking model in existence.

Ok when can i get this in codex?

If it's so good why is Dots, also released today, based on Astra?