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.
And half as good. I didn't have great experiences with Anthropic models in the past, but Opus 5.5 seems to have turned a major corner. It is churning through tasks significantly more quickly and efficiently.
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.
Edit: Defining "difficult" as a complex coding or systems task (or even series of them in a single prompt).
> 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?
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.
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.
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.
I'm not an OpenAI simp, but how anyone can have any opinion on the performance of these models in less than a day - let alone a few hours - is beyond me.
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.
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.
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
If you run out of sol medium with $100 you're doing something wrong. Astra destroys your usage, I get 1 day of usage with Astra, but 6 sol is almost unlimited and I only use xhigh.
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).
This is the distribution of usage. Spin up a bunch of loops or fire a semi-autonomous factory at a project and it's pretty easy to blow through a 20x account in a few hours if you can afford the sandboxes, CI and other infra required.
If you're running 2-3 parallel agent session with a few sub agents and waiting for you to prompt them, you'll have a very different experience!
> Spin up a bunch of loops or fire a semi-autonomous factory at a project and it's pretty easy to blow through a 20x account in a few hours if you can afford the sandboxes, CI and other infra required.
This is a tiny minority of people, not “most people”.
if an openai employee is reading this plz hire me i am unemployed and i need a job
(Usage limits are entirely dependent on what you're doing with them. If you're not running it on 1 million LoC codebases you can get a lot of mileage out of even a 5x account particularly with the recent cheap models)
I agree, and I haven't seen other people mention this! The benchmarks for GPT 6 Sol are great, but realistically it does not seem better than 5.6 Sol. 6-Sol is noticeably worse for code reviews (worse than Deepseek 4.1 flash), has implementation issues (requires more rounds of code reviews and fixes to get to a serviceable state). Opus 5.5 is much much better.
I've implemented multiple features side by side with Opus 5.5 and 6 Sol, and the Opus 5.5 results always have fewer high severity bugs and require fewer rounds of fixes to get it over the finish line.
If 6.1 Sol has actually matched Opus 5.5, I'd be very happy. However, benchmarks and real usage don't seem to agree in my own tests. So we'll have to see.
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.
How large of codebases are you working on? The models have gotten good enough to 1 shot stupid "trivial" throwaway integration projects with 0 handholding (was having RL'd garbage in late 2025), and I'm actually enjoying designing bounded greenfield personal software from scratch with Astra, in my experience. It's quite slow - 2 weeks of credits and constant talking and back and forth with Astra, but it doesn't feel annoying to talk to and is like an intelligent colleague maybe 70% of the time? Which is great. Just push back when it's dumb.
I'm by no means an AI booster, but given 2022 - 2026 progress I'd say it's "exponential" in the sense of, "holy shit, every year I can do more and more genuinely different things", not "RSI mind reading intelligence can do anything is here".
> I haven’t seen actual capability growth since ~January, and I’m pretty sure that was all tooling/harness improvements.
Even if that were the case, I'd say that it's improved in practice. And just from a philosophy perspective, if you're trying to imply some kind of mind dualistic way of viewing things, uh, I disagree with those theories of intelligence strongly (which also incidentally also disagrees with AIT-style theories of intelligence on one axis, though I have many bones to pick with the culture there).
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.
I mean Opus 5.5 is absolutely fantastic, unreasonably and unexpectedly so, but Astra was great and as far as I can tell SOTA until, when was it, 3 days ago, no?
(Sol 6 idk, have not used it much for coding really. Seemed to work just fine when Astra used it in Codex as subagents.)
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.
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?
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.
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.
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
Which, honestly, is fine. A lot of juice to squeeze in efficiency and even if models got zero more capable, making the capability that is already here cheaper is a huge win for everyone (except Nvidia)
Some version of this claim has been made for the past 4 years. There's a data cliff, there's no more compute to buy, the financials don't make sense and all of these orgs will be out of business by end of quarter.
Not once has any of these predictions come true, the pace of progress has continued on it's exponential trajectory since ChatGPT first came to the public's attention.
So why now? What is special about today that suggests all of this is coming to a screeching halt despite all evidence to the contrary?
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)
I was thinking the same thing in terms of running out of data a few months ago. But aren't most gains in the past year+ due to reinforcement learning in some form? Which doesn't need "fresh data" per se, as the model effectively creates the data as it goes. As long as engineers can come up with proper environments, tasks/goals, rewards, and actions, I don't really see data being a limit to model improvement in an agentic sense. Maybe as a knowledge base
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?
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.
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.
The plateau doesn't have to be perfectly flat, but it's not a straight line upward anymore either (kind of like our work on transistors, where we've kind of hit the bounds of speed in clock cycles but are improving on miniaturization and power efficiency)
>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.
Most of the impressive accomplishments we’ve seen in the last few months have been the result of huge agent swarms working together and brute-forcing solutions, not massive leaps in intelligence from standalone models. That is still an improvement in the usefulness and power of the technology, but it is NOT evidence that model intelligence is increasing faster than before.
I don't have any access to any agent swarms (and neither do most) and i still think the models have obviously improved massively in standalone intelligence. Of course they have, agent swarms are not magic. You can swarm all you want around GPT-4 era models and you'll get nowhere. And i've never seen the term 'brute-force' more abused than these LLM discussions. Basically none of the results have been brute force.
"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.
> sudden panic and desire to "slow down" is because they're hitting the plateau on capability
I don't think that's the motivation, it's because both companies want to IPO and the _only_ way to even hope to be profitable is to do a whole lot less training, which costs a fortune. But unless Chinese labs go along with this gentleman's agreement (they won't), slowing down on training will bring about the inevitable Chinese model parity date more rapidly. At which point the game is well and truly over for OpenAI and Anthropic. Bit of a pickle they've gotten themselves into with the emphasis on being best, with premium prices to match.
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.
“OpenAI's new Pro 500 plan offers OpenAI's highest usage allowance and comes with access to its new "Ultrafast" feature — it also costs $500 per month.
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.”
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.
I think that's by design - they're going to IPO soon so if they can get a significant percentage of users to switch from the $200 to the $500, they can 2.5x projected revenue.
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.
Yes, either US AI corps reduce the cost of their top tier personal subscriptions down to what people are already paying for other expensive personal apps (e.g. Adobe), so ~$50-100, or open weights are going to eat their lunch very quickly. We're not there yet, as current hardware doesn't allow you to do things like multiple parallel agents, but we'll get there soon enough.
This is pretty typical product positioning. You want to sell to both high-end and low-end users, so you offer products at a few price points. Then it turns out that that middle is a much better fit for most users. So you start making the middle a worse fit to push most of those users into the higher tiers.
Long term, this only works if you have a non-commodity, and if the higher tier is actually more profitable. We'll eventually learn whether both are true. For OpenAI right now, it's probably enough to just increase revenue, even if the higher tier is even less profitable.
The 200$ 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.
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.
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.
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
You are painting half of the picture, perhaps on purpose? The other half is this: Opus 5.5 is significantly better than both Sol 6.1 and Astra, and with the newly increased limits across the board, it is quite difficult to run out (unless you're spamming agents at Max effort). So it is a much, much better deal than OpenAI's Pro 100.
> 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.
"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.
> 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
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.
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
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.
I do wonder if people switch back and forth between primary models (GPTvsClaude) that it may be a better idea to simply keep releasing updates as soon as possible in order to keep users from bouncing back and forth.
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.
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.
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
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dots.
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.
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.
What I don't understand is how much people have to say about every single one. Aren't we at the diminishing returns stage yet? Is there really that much to discuss?
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.
Chinese model pressure. Many of my SWE friends switched to Chinese models. I also use QWEN and GLM for many of the api requiring projects and dropped OpenAI and Anthropic. The only reason was the cost.
EDIT: I love getting downvoted by openai and anthropic employees or their bots.
I can't recommend Chinese models enough. My personal favorite is DeepSeek v4.1 Flash but I have tried Qwen 3.8, Kimi 3 and GLM 5.3 which are equally impressive but DeepSeek is the cheapest and fastest regularly hitting 270 token per second.
And yeah I have worked with Anthropic and OpenAI models, they're good but they cost a fortune while Chinese models are already really good at a fraction of the cost.
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.
I have played around a little bit with fixing some rigging problems and was impressed, but Opus even warned me it was bad at animations cause it can only really grab screenshots to process static content.
I've only tried animating models in Astra-6, and I was quite impressed! It's rarely able to one-shot things perfectly, but it usually gets pretty close.
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.
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.
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
They have also cut allowances for subscriptions in half. So even in the best case scenario it's about 2.5 times cheaper for Codex users. They just seem to have matched Claude Sonnet 5.5 *API pricing*, but from what I see online, it seems Claude Code now has a much more generous subscription allowance.
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.
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
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.
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?
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.
I got a popup in my Codex just now saying "Try out 6.1 Sol!" and so I clicked the button to try it, and intriguingly, it set my model selector to "GPT-6 Astra Light" which makes me think 6.1 Sol may be in some way just a lighter/distilled version of Astra? defo interesting, not sure if I should read too much into it though. I see no option for directly selecting 6.1 Sol in my Codex Desktop UI.
very surprised by the sentiment against GPT 6.0 Sol, I've been using it exclusively since release and it feels like a cheaper astra to me. admittedly I haven't tried any anthropic models in a while other than small tests since i can't use my anthropic subscription in other harnesses (like OpenAI has supported natively for a long time).
If OpenAI cuts alternative harness support it will be a weird day trying to figure out what to do next, it's been so clearly the best bang for your buck (imo) for a while. maybe id finally have to give smaller models a try.
anything to avoid using the dogwater codex & claude code tuis.
anyways this seems like a nice cost improvement over GPT 6 Sol and I expect this will be my new daily driver.
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.
well i could certainly be in the wrong; i'm just speaking from my personal and likely flawed experience but i feel like i've noticed silly mistakes in every (llm) model that has been released (and that i've sufficiently used) and it hasn't felt like 6 Sol was much of a regression from 6 Astra (more than reported in both model cards), both of which ive very extensively.
not saying this is the case here but it does feel a bit like wine tasting sometimes, everyone claims to be an expert that can taste a few tokens and tell you exactly what region and vineyard its from.
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.
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.
> 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.
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.
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.
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.
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.
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.)
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.
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.
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."}}
I wish they'd list the environmental cost. My employer has an unlimited AI budget so I don't care about using Astra if it's just more profit for OpenAI. I care more if it actually uses 5x more energy.
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.
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.
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.
I don't understand the point of this, why just now when it comes to llms. Why wasn't anyone enraged with the environmental costs of kids playing video games. I would not be surprised the environmental cost of that is an order of magnitude bigger than what llms have.
Edit: for context, just Steam alone has ~200million monthly active users.
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.
Considering many games make use of cloud computing for online play and similar functions, they probably make up a pretty goot bit of global cloud compute capacity. Likely quite a lot less than the big AI players, but not an insignificant amount.
The energy costs of the cloud computing required for gaming are substantially less in power - not to mention overall demand - than LLMs. Come on, we're not in the same energy ballpark here.
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.
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.
If you recall history past the last 5 minutes, you will remember that people have indeed been enraged with the environmental costs of things for a long time. Its just that AI seems to have induced a mass amnesia, and people tend to forget about what happened pre 2024.
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.
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.
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.
Let's all boycott and move to Claude until they release 6.1 Astra. I don't like to be teased.
When is the alleged "safety" concern satisfied? Does this mean releasing new capability to consumers is going to get a lot slower? Lower price for 6 Astra capability via this 6.1 Sol is exciting, but that is because of Astra capability not merely the low price point.
When do we get the next jump in capability? When is 6.1 Astra released?
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.
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.
Here they are for GPT-6.1-Sol: https://tools.simonwillison.net/markdown-svg-renderer?url=ht...
They're not notably different from the GPT-6 family pelicans: https://static.simonwillison.net/static/2026/gpt-pelicans-gr...
This is the actual big announcement. 50% cheaper cache than GPT-6 Sol will get you far more mileage on Codex.
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.
Edit: Defining "difficult" as a complex coding or systems task (or even series of them in a single prompt).
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.
This was the biggest thing I noticed in the 6 models; their conversational prose is dramatically less grating.
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.
However it's less willing to obey your instruction so it's less usable for general runtine flows.
Looks like 5.5 is the new 4.6
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.
I get better results and usage our of my $20 claude sub than my $100 openai sub... it's that ridiculous
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 did use some CC for Fable when it came out, and it was... ok. Not the worst thing ever.)
1 - https://bench.killswitch-lang.org
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).
With the 80% price cut, this is competitive with Opus 5.5 despite the subscription downgrade.
Additionally, it was said that existing 20x subscriptions retain the higher limits for some time.
I have seen you make these immature accusations that users here are OpenAI employees multiple times today.
This is not even remotely true.
If you're running 2-3 parallel agent session with a few sub agents and waiting for you to prompt them, you'll have a very different experience!
This is a tiny minority of people, not “most people”.
(Usage limits are entirely dependent on what you're doing with them. If you're not running it on 1 million LoC codebases you can get a lot of mileage out of even a 5x account particularly with the recent cheap models)
Sol 6 was so bad that I switched over to Opus 5.5 exclusively.
Huge regression compared to Sol 5.6, often doing really dumb things. Same for Luna.
Even Astra is very unreliable for coding. Brilliant for vision, sometimes just great, but it also often does very stupid things.
I'm a bit sour on OpenAI right now and skeptical that 6.1 will be much different.
(Note: this is after preferring and shilling Codex/OpenAI models for the last half year)
I've implemented multiple features side by side with Opus 5.5 and 6 Sol, and the Opus 5.5 results always have fewer high severity bugs and require fewer rounds of fixes to get it over the finish line.
If 6.1 Sol has actually matched Opus 5.5, I'd be very happy. However, benchmarks and real usage don't seem to agree in my own tests. So we'll have to see.
I'm by no means an AI booster, but given 2022 - 2026 progress I'd say it's "exponential" in the sense of, "holy shit, every year I can do more and more genuinely different things", not "RSI mind reading intelligence can do anything is here".
> I haven’t seen actual capability growth since ~January, and I’m pretty sure that was all tooling/harness improvements.
Even if that were the case, I'd say that it's improved in practice. And just from a philosophy perspective, if you're trying to imply some kind of mind dualistic way of viewing things, uh, I disagree with those theories of intelligence strongly (which also incidentally also disagrees with AIT-style theories of intelligence on one axis, though I have many bones to pick with the culture there).
The lackluster GPT-6 Sol has been superseded by this apparently much better 6.1 Sol within a week.
I am very skeptical of claims that old models weren't much worse. Compare this to February's GPT-5.3.
I mean Opus 5.5 is absolutely fantastic, unreasonably and unexpectedly so, but Astra was great and as far as I can tell SOTA until, when was it, 3 days ago, no?
(Sol 6 idk, have not used it much for coding really. Seemed to work just fine when Astra used it in Codex as subagents.)
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.
Astra seems better though.
Showing one potentially saturated benchmark doesn't necessarily fill me with a lot of confidence in the coding results.
Which, honestly, is fine. A lot of juice to squeeze in efficiency and even if models got zero more capable, making the capability that is already here cheaper is a huge win for everyone (except Nvidia)
Not once has any of these predictions come true, the pace of progress has continued on it's exponential trajectory since ChatGPT first came to the public's attention.
So why now? What is special about today that suggests all of this is coming to a screeching halt despite all evidence to the contrary?
- 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)
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?
if true then LLM related AI (post-post AI winter AI?) is probably one of the fastest inception-to-plateau tech sectors to have ever existed.
We're still improving transistors on a somewhat routine basis.
People were talking about plateau for years already.
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.
Edit: removed a comment that was uncharitable and rude, for which I apologize.
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.
We are seeing multiple frontier models dropping on the same day and no one bats an eye, because it's more of the same.
We've gone from 80% in some places to 80% in some more places.
I don't think that's the motivation, it's because both companies want to IPO and the _only_ way to even hope to be profitable is to do a whole lot less training, which costs a fortune. But unless Chinese labs go along with this gentleman's agreement (they won't), slowing down on training will bring about the inevitable Chinese model parity date more rapidly. At which point the game is well and truly over for OpenAI and Anthropic. Bit of a pickle they've gotten themselves into with the emphasis on being best, with premium prices to match.
It's not even anything controversial..
it's also why there have been so many calls for regulation and slowdowns.
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.
I remember when bandwidth was super expensive and now it’s dirt cheap.
Consumers are now saying the new pricing with lower usage caps is not so great. https://news.ycombinator.com/item?id=49896975
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.
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.”
https://www.engadget.com/2272106/openai-adds-dollar500-pro-s...
Yikes
Our VC-backed subscription days are numbered
Lastly, I'd like to actually use it in the real world to see how far my plan goes or if its unusable.
Tibo said that the existing $200 subscriptions keep the 20x factor for a while.
Ultrafast would have been nice with the temporary "Pro 400" plan.
But $200 is likely the ceiling of what people will pay for a subscription with usage based on vibes.
$500 for the old $200 is definitely a fumble.
Long term, this only works if you have a non-commodity, and if the higher tier is actually more profitable. We'll eventually learn whether both are true. For OpenAI right now, it's probably enough to just increase revenue, even if the higher tier is even less profitable.
Now, as it's linear, it makes much more sense to downgrade to 100$ 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 can justify $200/mo but more than double is not appealing to me.
Basically OpenAI aligned with Anthropic on the weekly usage with the caveat that OpenAI doesn't have a 5h limit.
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.
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.
Might want to hold off on canceling and continue to bleed them dry until the nerf hits
The only way is for prices to go up. Way up.
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.
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.
Luckily it's not a mistake as now we have access to . . . dots.
(and sol 6.1, it seems)
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.
Edit: https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash/blob/...
>RSI
Recursive improvement doesn't imply increased rate, another word for it is "iterative" but this probably sounds too boring for some.
EDIT: I love getting downvoted by openai and anthropic employees or their bots.
And yeah I have worked with Anthropic and OpenAI models, they're good but they cost a fortune while Chinese models are already really good at a fraction of the cost.
Opus 5.5 is definitely better at coding, but nothing even comes close to 6-Astra for work in 3D graphics...
I have played around a little bit with fixing some rigging problems and was impressed, but Opus even warned me it was bad at animations cause it can only really grab screenshots to process static content.
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.
Opus 5.5 was a gut punch and my impression is OpenAI is still reeling.
The best thing is that we benefit from these constant back and forth gut punches :)
Astra is a pretty impressive model. Excited to try this.
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.
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?
If OpenAI cuts alternative harness support it will be a weird day trying to figure out what to do next, it's been so clearly the best bang for your buck (imo) for a while. maybe id finally have to give smaller models a try.
anything to avoid using the dogwater codex & claude code tuis.
anyways this seems like a nice cost improvement over GPT 6 Sol and I expect this will be my new daily driver.
not saying this is the case here but it does feel a bit like wine tasting sometimes, everyone claims to be an expert that can taste a few tokens and tell you exactly what region and vineyard its from.
Then Opus 5.5 caught them off guard and now they're actually releasing the correct sized model.
1 - https://bench.killswitch-lang.org/
These moves all make sense when you take into account the enterprise market.
https://news.ycombinator.com/item?id=49889873
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.
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.
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.
This is a decent win though, if it really is better. 6-sol was really no good, at least in my work.
Will see if this remedies things.
https://amphetamem.es/meme?id=the-simpsons_06_12_71&text=We%...
Guess not?
Huge misstep releasing it.
{"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."}}
Laptops use very minimal power - you don't need to worry about them. If they didn't their battery life would suck.
Edit: for context, just Steam alone has ~200million monthly active users.
I'd be curious as to how much of internet infrastructure is dedicated to gaming though.
How many DCs are devoted solely to gaming?
Yes but it adds up when you consider that just on Steam alone there are 200 million monthly active users.
An entire planet. Just Steam alone has one or two hundres million monthly active users.
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.
Yeah? Show me the big movements against computer gaming.
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.
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.
When is the alleged "safety" concern satisfied? Does this mean releasing new capability to consumers is going to get a lot slower? Lower price for 6 Astra capability via this 6.1 Sol is exciting, but that is because of Astra capability not merely the low price point.
When do we get the next jump in capability? When is 6.1 Astra released?
After what DeepSeek pulled with V4.1 Flash I've given up on trying to map LLM versions to semver.
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.