I would just LOVE to see all the behind-the-scenes shithousery both companies are employing to one-up the other in this, largely, 2-horse AGI race. Someone should make a mockumentary when all is said and done!
> We already know OpenAI has "bel" that is MUCH better than astra and is being used internally
I mean, isn't it almost a guarantee that what we get is a gimped version of what they use internally? They probably already serve themselves next gen level models at 1k+ tps from cerebras machines hosted on perm while we get quantized astra/opus at 50tps on a good day
Was it not essentially confirmed that all of the frontier labs have much stronger models internally that don't make sense to serve at scale yet, which they use for development and to train models that are able to be served at scale?
Yeah, it does, it might be misaligned, a snapshot of the going on training, bigger than they can serve publicly, not yet completed the full training pipeline.
If you see the knowledge cutoff you can see that sol 5.6 finished the initial main (+stage edited) of the training pipeline on Feb 16, 2026 but it was publicly released on July 9.
The opposite would be weird: if they do NOT have an unreleased in-house model that would be really odd.
> If they really had a much more powerful model, it would make absolutely no sense to sit on it.
Makes total sense if they don't have the compute and can't serve it in an economically viable way. Also it lets you build things no one else in the world can build as fast as you until it's released
No? The top of human engineers are much better than any model would be, so AI models really aren't a big advantage when you're trying to develop anything that is SOTA.
You can't fathom how 'top human engineers' could take advantage of exclusive access to a frontier model at ultrafast speeds and limitless token budgets to conduct research?
It makes sense for them to sit on it until they've finished testing it. More powerful but also more likely to delete all your email by mistake = you shouldn't release it yet.
I definitely do not know what I'm talking about, but that individual doesn't know what they're talking about even more than I don't know what I'm talking about.
> its not done training, why would they release a model that hasn't finished training?
Because clearly they have no problem with releasing newer versions of models even just a week apart.
> besides, we know anthropic are sitting on models too
It is from your crystal ball or from other bullshit you heard from Anthropic employees on Twitter?
We all know Anthropic had Mythos and Fable, and they turned out to be completely normal models, entirely in line with the capability of their predecessors.
All they do is lie, and you're believing their lies.
The poster is not claiming Bel is a secret AGI. Just that it exists and only exists internally at the moment.
It's rumored to be over 10T parameters. When released it'll probably be very good at certain tasks, albeit slow and expensive and not necessarily "wiser". You don't have to make this a binary.
Also, Mythos was in fact a significant step-up in several ways. It fits the trend line, but only because the trend line for LLMs is quite steep. Plus Astra is still in many ways less intelligent than Fable/Mythos despite being released much later.
> its not done training, why would they release a model that hasn't finished training?
> Because clearly they have no problem with releasing newer versions of models even just a week apart.
You can see how it is pure non-sequitur, right?
> We all know Anthropic had Mythos and Fable, and they turned out to be completely normal models, entirely in line with the capability of their predecessors.
Fable was absolutely not in line with capabilities of other models when released. For cybersec work it was much, much better.
Any strong enough model with weak enough safeguards can cause an AI Chernobyl event that will make people and governments against AI development and deployment, just like Chernobyl did for nuclear energy.
The absolute frontier is largely 2-horse, but the rest of the pack is very close behind, which I'm grateful for. Grok, Facebook, and the Chinese vendors are producing excellent models.
Gemini is also pretty nice for researching things. It turns out that having the whole internet indexed and having unlimited access to YouTube is a force multiplier of some kind.
Since Google has their own TPUs, TPS is also pretty high w.r.t. Claude, for example.
I think Gemini could be a great product, if they didn't decide to stuff it down my throat at every possible occasion.
Recent example: on my e-reader, tapping a word I don't now and clicking "Translate" pulls up the possible translations from a dictionary, a local file just a couple of megabytes big, near instantly, on this tiny processor.
Doing the same on my Android phone starts a Gemini-chat with the prompt "Translate the word x into y". Takes forever, internet access needed, results vary, burns who knows who much energy.
Since Gemini 3.8 Flash release, I've been largely using that for my coding ttasks. The architectureand design work I use Opus, but the rest of the work is done via Gemini 3.8. I don'thave to worry aboutweekly limits even on the $20 plan. I get down to about 10% weekly limit before it resets. Currently I'm building my own generative modeling + aero sim package largely using Gemini and it's been working great.
Gemini is unbelievably bad for research in my experience. It hallucinates like it's 2023, doesn't use its own search, makes up fake rationalizations for why it didn't need to etc.
It's baffling that OpenAI managed to get better at web search than the company literally synonymous with web search.
Pretty interesting. It one-shots correct information with references and a further reading list 99% of the time for me.
I don't want it to fill in the gaps though, but make it reference anything and everything it brings, hence it doesn't hallucinate much.
If something feels off, I ask it to back it with concrete data, and if it can't, I don't consider that information correct. That happened once, though, and web doesn't have any information on that thing either. So in that case, not only it had no information on the web, the training data had no information on that thing either short of feeding confidential design documents if they were ever present in the first place.
The question was about an instrument preamp though, so nothing crucial.
I guess there's one thing that could really bias my anecdata: I almost exclusively use Gemini in a professional/niche domain and ChatGPT in a personal/idle curiosity context, so there's a chance that ChatGPT is just as wrong but I am not knowledgeable in a given domain to tell.
... and, must be said a plethora of largely unsung, small, unknown "labs", outfits, "researchers" and the like. There is a long tail of smart people having at this. I guess, sheer compute aside, I think much progress - or, at least, important pieces thereof, will come from there.-
There are some niche research areas where bog standard machine learning algorithms make miracles. LLM is just the poster child. AI/ML is a much larger and wider research area.
Still waiting for the moment where GPT either orders 4,000 pounds of raw beef or just deletes the whole OpenAI repo because it "thought the easiest way to remove all bugs is by deleting the whole repository"
Astra is noticeably smarter than any OpenAI model before it. Sol 6.1 is very noticeably smarter than sol 6 even after half a day of using it (sol 6 was actually terra 6 and opus 5.5 has taken them by a total complete surprise)
It's the same for most tasks. Where I do notice it is long agent runs, where agents take more steps and the performance difference definitely compounds over the iterations.
Kinda same but I miss my 5.3 Codex. Thing lasted forever on my $20 subscription and with detailed prompts was able to pretty much implement everything I requested it to do with a acceptable quality.
Because you're probably using it for stuff like "edit this function"/"refactor this class". 5.5 to 5.6 Sol was a giant jump. 5.6 to 6.1 seems very large as well.
Same. 5.5 got work done then 5.6 was also fine then 6 was maybe not quite as good. Now with 6.1 they are cutting usage and raising prices and introducing ultra fast mode, but things were good enough 5 months ago.
This has got to be a panic move from OpenAI, right? They’ve had some bad press lately because from their billing changes, and Anthropic have finally released a fast, relatively cheap Opus with improved written English.
I suspect it's partly because people didn't jump from GPT-3 to GPT-6.1 Sol and partly because SOTA models from the last few(?) months have been able to tackle most of the regular tasks. It means this new model isn't different in that regard from Opus 4.8, if your mental benchmark is that they both are capable of implementing something like a CRUD app.
I’ve seen overwhelmingly that when a model is good people see it, and when it isn’t, they criticize.
I’ve seen nothing but ‘wow this is a huge step up’ from Opus 5.5. I felt this way about Opus 4.5, GPT-5.6 Sol/Luna, and to a lesser extent with Fable and Astra.
But Opus 5 was ass, and the entire gpt 6 line feels like OpenAI’s version of that.
I like the fast releases. 6.1 coming so fast after 6.0 means they found some improvement solid enough for a new rollout.
The only time I remember the newest model making an obvious regression was when the first rolled out MoE. Super speed update but each request had less intelligence at hand. We’re way past that now
Did they find this improvement within the week? If so, given all their whinging about safety, it seems irresponsible to only test the improved model for less than a week.
Did they find the improvement more than a week ago? If so, why bother releasing GPT 6 if they knew they had a better version essentially ready to go?
They probably have 6.2 just about ready, while 7 and 8 are still being cooked up. You can work on multiple releases in parallel.
We're just getting the latest training checkpoints constantly, just to edge out the other lab, while they are trying to come up with something worthy of a new major version number. It's actually worrying, since there's so much pressure to release now.
I'm not convinced. Pipelining doesn't mean they're forced to release a model they know will be replaced in a week.
>We're just getting the latest training checkpoints constantly, just to edge out the other lab, while they are trying to come up with something worthy of a new major version number. It's actually worrying, since there's so much pressure to release now.
This makes me think that this is on purpose to prop up the "we have no control over ourselves, please give us a regulatory moat" narrative. The competition is nowhere near extreme enough to justify weekly releases. The Chinese models are still a handful of months behind the frontier, and the frontier companies in the West haven't been pushing the frontier at this rate.
They've been pretty capable for a very long time. I don't think the models are getting more capable so much as people are getting better at using them and more people are getting the opportunity to be impressed.
> the cache read discount rises from 90% to 95%. GPT-6.1 Sol’s overall blended price for agentic workloads is therefore slightly lower than GPT-6 Sol. This represents an additional price cut, following GPT-6 Sol’s original 50% discount from GPT-5.6 Sol
Wow, just wow. We are racing to the bottom with these prices.
They probably figured they could increase their margins by releasing "6 Terra" as "6 Sol". After being surprised by the Opus 5.5 launch, they release the true "6 Sol" as "6.1 Sol" and with very aggressive pricing.
It would be very nice if artificialintelligence.ai actually, from a UX perspective, did the models in more than one thinking mode. I use claude, and I wanna build a feeling for what high, medium, etc. actually gives me. So far their comparisons, and having tried several different models for my work, has given me a feel of what 50 intelligence actually is. And I believe it would be
be of even greater value to get a feel inside the single model I actually use, as most people do, because not many, I believe, switch heavily between models when working. I understand that the cost here is greater but the model provivders should obviously give you free access, because of the great work you are doing.
In my case it actually made a difference. I tested it on my own code review set with Gemini Flash. On low it found fewer bugs than on default around 90% versus 97% but was about three times faster and a lot cheaper. On high it actually found everything in the hardest case but took almost three minutes per call. so I think the difference is real you only see it if you run the same fixed cases a few times and not by feel. And yes I used Gemini rather than the GPT Sol that is mentioned here would be interesting to see the same tests on Sol.
What about those do you think are "shady"? Price discrimination in favor of small customers at the expense of large customers is somewhat common; businesses want customers to buy more of their products, but customers are not obliged to buy more if they like their current deals. Having limits on using finite resources seems even easier to justify.
Humans see differences in prices as unfair. The greatest example of this is price gouging during an emergency, but also look at the level of hate that scalpers get.
Using AI to do this, if anything, given the common negative sentiment, is seen as even worse. People think of this as AI using information asymetry to squeeze more out of users, not to cut people a deal.
Taking advantage of information asymmetry is generally looked down upon as well. Look at all the laws we have protecting kids from this. Businesses often don't seem similar protections because those are businesses with big legal teams (and when it is a big legal team vs a small mom and pop store without a single lawyer on payroll, people do start taking issues with it). The power difference between the average company using AI pricing and the average consumer falls pretty solidly in the 'we don't accept this' side of taking advantage of information asymmetry.
I could keep going, but I think these are already plenty enough reasons to why people look at AI price discrimination as not just a bad business practice they don't like, but an immoral/unethical one.
Sure, one can make economical counter arguments, but that's arguing on an orthogonal dimension that simply isn't relevant to where these feelings/thoughts come from.
Shady as in not clearly communicated and change with random tweets being the only indicator, or a blog post if you're lucky.
x20? 25x? 5x? All in comparison to some other plan that also doesn't have clear limits. "Oh BTW we mean session limits, your weekly is 2x of 5x". "This model eats your limits twice as fast and you can use half of your weekly limits on it". "No this time we mean weekly only".
That doesn't resolve them from providing a stable service for something they market. I'm not buying something under the counter, and it's not advertised to be an alternative for people not willing to pay the full price. From my perspective, the subscription is the full price and I expect stable, reliable service.
You say that when the customer makes irrational requests.
Is it irrational to want consent went the subscription terms change? Netflix does it a month in advance, and cancels the plan when no consent given. What do you think is special with LLM companies so they get special treatment?
I'm from Europe so maybe this is why it feels so weird to me. Then again, they could stop offering their services to a region if they don't want to adjust their ways.
What gets me confused is how they aren't prosecuted for this. My theory is that people are too busy with the security threats and (currently) ignore the shady practices.
I would just LOVE to see all the behind-the-scenes shithousery both companies are employing to one-up the other in this, largely, 2-horse AGI race. Someone should make a mockumentary when all is said and done!
I think it's weirdly just a choice of deciding to cut releases.
We already know OpenAI has "bel" that is MUCH better than astra and is being used internally
> We already know OpenAI has "bel" that is MUCH better than astra and is being used internally
I mean, isn't it almost a guarantee that what we get is a gimped version of what they use internally? They probably already serve themselves next gen level models at 1k+ tps from cerebras machines hosted on perm while we get quantized astra/opus at 50tps on a good day
Was it not essentially confirmed that all of the frontier labs have much stronger models internally that don't make sense to serve at scale yet, which they use for development and to train models that are able to be served at scale?
Where was this confirmed?
> We already know OpenAI has "bel" that is MUCH better than astra and is being used internally
You're just believing their own bullshit. There's no indication that this is true except from claims from people working at OpenAI.
If they really had a much more powerful model, it would make absolutely no sense to sit on it.
> it would make absolutely no sense to sit on it.
Yeah, it does, it might be misaligned, a snapshot of the going on training, bigger than they can serve publicly, not yet completed the full training pipeline.
If you see the knowledge cutoff you can see that sol 5.6 finished the initial main (+stage edited) of the training pipeline on Feb 16, 2026 but it was publicly released on July 9.
The opposite would be weird: if they do NOT have an unreleased in-house model that would be really odd.
> The opposite would be weird: if they do NOT have an unreleased in-house model that would be really odd.
Indeed it would be really odd if OpenAI were actually open
> The opposite would be weird: if they do NOT have an unreleased in-house model that would be really odd.
This isn't at all what I said. The original commenter mentioned a model MUCH stronger than Astra.
> If they really had a much more powerful model, it would make absolutely no sense to sit on it.
Makes total sense if they don't have the compute and can't serve it in an economically viable way. Also it lets you build things no one else in the world can build as fast as you until it's released
This theory would be easy to prove IF openai was pushing high quality software. It's not.
> Also it lets you build things no one else in the world can build as fast as you until it's released
"We can generate slop faster than anyone in the world" :evil_emoji:
You don't think having a more intelligent model they can use internally that others can't is an advantage?
If that's the case, do Anthropic have an even better one helping them? The Chinese labs too? Where this OpenAI "advantage" is taking them?
No? The top of human engineers are much better than any model would be, so AI models really aren't a big advantage when you're trying to develop anything that is SOTA.
You can't fathom how 'top human engineers' could take advantage of exclusive access to a frontier model at ultrafast speeds and limitless token budgets to conduct research?
I have all that and LLMs invariably produce garbage, so, no.
Not every problem is best addressed by a top engineer.
Plenty of the tasks that keep a company running can benefit from good-enough (and better than the competition).
Yes, and none of those tasks require even the current SOTA models.
It makes sense for them to sit on it until they've finished testing it. More powerful but also more likely to delete all your email by mistake = you shouldn't release it yet.
With all due respect, you do not have a single clue what you're talking about.
With the same respect, you don't either. Simping for openai don't make you part of their in group
I definitely do not know what I'm talking about, but that individual doesn't know what they're talking about even more than I don't know what I'm talking about.
its not done training, why would they release a model that hasn't finished training?
besides, we know anthropic are sitting on models too
> its not done training, why would they release a model that hasn't finished training?
Because clearly they have no problem with releasing newer versions of models even just a week apart.
> besides, we know anthropic are sitting on models too
It is from your crystal ball or from other bullshit you heard from Anthropic employees on Twitter?
We all know Anthropic had Mythos and Fable, and they turned out to be completely normal models, entirely in line with the capability of their predecessors.
All they do is lie, and you're believing their lies.
The poster is not claiming Bel is a secret AGI. Just that it exists and only exists internally at the moment.
It's rumored to be over 10T parameters. When released it'll probably be very good at certain tasks, albeit slow and expensive and not necessarily "wiser". You don't have to make this a binary.
Also, Mythos was in fact a significant step-up in several ways. It fits the trend line, but only because the trend line for LLMs is quite steep. Plus Astra is still in many ways less intelligent than Fable/Mythos despite being released much later.
> its not done training, why would they release a model that hasn't finished training?
> Because clearly they have no problem with releasing newer versions of models even just a week apart.
You can see how it is pure non-sequitur, right?
> We all know Anthropic had Mythos and Fable, and they turned out to be completely normal models, entirely in line with the capability of their predecessors.
Fable was absolutely not in line with capabilities of other models when released. For cybersec work it was much, much better.
Any strong enough model with weak enough safeguards can cause an AI Chernobyl event that will make people and governments against AI development and deployment, just like Chernobyl did for nuclear energy.
tell me "I drank the kool aid" without telling me you drank the kool aid.
The US government and most large companies drank the kool aid, and they will be the ones blaming Big AI if things go very wrong.
I think there's going to be a brutal backlash against the entire technology sector.
Gov and Corp will throw their hands up and explain how it's not their fault.
> largely 2-horse
The absolute frontier is largely 2-horse, but the rest of the pack is very close behind, which I'm grateful for. Grok, Facebook, and the Chinese vendors are producing excellent models.
Gemini is also pretty nice for researching things. It turns out that having the whole internet indexed and having unlimited access to YouTube is a force multiplier of some kind.
Since Google has their own TPUs, TPS is also pretty high w.r.t. Claude, for example.
I think Gemini could be a great product, if they didn't decide to stuff it down my throat at every possible occasion.
Recent example: on my e-reader, tapping a word I don't now and clicking "Translate" pulls up the possible translations from a dictionary, a local file just a couple of megabytes big, near instantly, on this tiny processor.
Doing the same on my Android phone starts a Gemini-chat with the prompt "Translate the word x into y". Takes forever, internet access needed, results vary, burns who knows who much energy.
Why? Just why?
So that some team at Google can hit their OKRs for user adoption and get promoted.
That kind of shoving down is pretty bad, I agree.
I neither use Android devices nor Google Search, so the only Gemini thing I see is the Gemini chat interface.
I understand the pain, though.
Since Gemini 3.8 Flash release, I've been largely using that for my coding ttasks. The architectureand design work I use Opus, but the rest of the work is done via Gemini 3.8. I don'thave to worry aboutweekly limits even on the $20 plan. I get down to about 10% weekly limit before it resets. Currently I'm building my own generative modeling + aero sim package largely using Gemini and it's been working great.
Gemini is unbelievably bad for research in my experience. It hallucinates like it's 2023, doesn't use its own search, makes up fake rationalizations for why it didn't need to etc.
It's baffling that OpenAI managed to get better at web search than the company literally synonymous with web search.
Pretty interesting. It one-shots correct information with references and a further reading list 99% of the time for me.
I don't want it to fill in the gaps though, but make it reference anything and everything it brings, hence it doesn't hallucinate much.
If something feels off, I ask it to back it with concrete data, and if it can't, I don't consider that information correct. That happened once, though, and web doesn't have any information on that thing either. So in that case, not only it had no information on the web, the training data had no information on that thing either short of feeding confidential design documents if they were ever present in the first place.
The question was about an instrument preamp though, so nothing crucial.
I guess there's one thing that could really bias my anecdata: I almost exclusively use Gemini in a professional/niche domain and ChatGPT in a personal/idle curiosity context, so there's a chance that ChatGPT is just as wrong but I am not knowledgeable in a given domain to tell.
... and, must be said a plethora of largely unsung, small, unknown "labs", outfits, "researchers" and the like. There is a long tail of smart people having at this. I guess, sheer compute aside, I think much progress - or, at least, important pieces thereof, will come from there.-
There are some niche research areas where bog standard machine learning algorithms make miracles. LLM is just the poster child. AI/ML is a much larger and wider research area.
Your "bog" if unrelated, put me in mind of a "Cambrian explosion" (of intelligence) in this case, ongoing.-
In a way: We have had "recursive self improvement" (RSI) - that's what genetics is, giving rise to ... us.-
This time around, I think the truly worrying thing is that, having transcended the biological substrate, the pace is unlike anything previously seen.-
Just a thought.-
I REALLY need new seasons of 'Silicon Valley'
Still waiting for the moment where GPT either orders 4,000 pounds of raw beef or just deletes the whole OpenAI repo because it "thought the easiest way to remove all bugs is by deleting the whole repository"
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"They trusted me. Dumb fucks" -evil ASI probably
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The sooner the better, brother.
After 5.5, I basically don't notice a jump in model performance, other than my usage ending sooner.
I do.
The results are less buggy, animations are much better.
It can work autonomously for hours and the result is decent most of the time.
That wasn't usually the case with 5.5, which needed more feedback and iterations to get things right.
Astra is noticeably smarter than any OpenAI model before it. Sol 6.1 is very noticeably smarter than sol 6 even after half a day of using it (sol 6 was actually terra 6 and opus 5.5 has taken them by a total complete surprise)
none of them is smart.
define smart
Felt the same until I started using Luna. I feel like I get similar performance, but faster, and my usage lasts so much longer.
Yup, GPT-5.6 Luna was a checkpoint for me. Too good for its price.
It's the same for most tasks. Where I do notice it is long agent runs, where agents take more steps and the performance difference definitely compounds over the iterations.
Kinda same but I miss my 5.3 Codex. Thing lasted forever on my $20 subscription and with detailed prompts was able to pretty much implement everything I requested it to do with a acceptable quality.
Because you're probably using it for stuff like "edit this function"/"refactor this class". 5.5 to 5.6 Sol was a giant jump. 5.6 to 6.1 seems very large as well.
Meanwhile Fable 5 and Opus 5.5 are in a different league.
Same. 5.5 got work done then 5.6 was also fine then 6 was maybe not quite as good. Now with 6.1 they are cutting usage and raising prices and introducing ultra fast mode, but things were good enough 5 months ago.
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This has got to be a panic move from OpenAI, right? They’ve had some bad press lately because from their billing changes, and Anthropic have finally released a fast, relatively cheap Opus with improved written English.
Didn't they announce the billing changes the same day?
What do people get out of being so reductionist?
Every time a new model comes out, people come out in droves "oh I don't notice anything different".
People have been saying this about <currentModel-1> for 2 years now, and the entire state of AI has changed dramatically.
It cannot be that the next AI model isn't better, but also suddenly what they are capable of is on an entirely different level.
I suppose it's the reverse of Amara's law:
"We tend to overestimate the effect of a technology in the short run and underestimate the effect in the long run"
I suspect it's partly because people didn't jump from GPT-3 to GPT-6.1 Sol and partly because SOTA models from the last few(?) months have been able to tackle most of the regular tasks. It means this new model isn't different in that regard from Opus 4.8, if your mental benchmark is that they both are capable of implementing something like a CRUD app.
I’ve seen overwhelmingly that when a model is good people see it, and when it isn’t, they criticize. I’ve seen nothing but ‘wow this is a huge step up’ from Opus 5.5. I felt this way about Opus 4.5, GPT-5.6 Sol/Luna, and to a lesser extent with Fable and Astra. But Opus 5 was ass, and the entire gpt 6 line feels like OpenAI’s version of that.
I like the fast releases. 6.1 coming so fast after 6.0 means they found some improvement solid enough for a new rollout.
The only time I remember the newest model making an obvious regression was when the first rolled out MoE. Super speed update but each request had less intelligence at hand. We’re way past that now
It seems strange though.
Did they find this improvement within the week? If so, given all their whinging about safety, it seems irresponsible to only test the improved model for less than a week.
Did they find the improvement more than a week ago? If so, why bother releasing GPT 6 if they knew they had a better version essentially ready to go?
Pipelining.
They probably have 6.2 just about ready, while 7 and 8 are still being cooked up. You can work on multiple releases in parallel.
We're just getting the latest training checkpoints constantly, just to edge out the other lab, while they are trying to come up with something worthy of a new major version number. It's actually worrying, since there's so much pressure to release now.
I'm not convinced. Pipelining doesn't mean they're forced to release a model they know will be replaced in a week.
>We're just getting the latest training checkpoints constantly, just to edge out the other lab, while they are trying to come up with something worthy of a new major version number. It's actually worrying, since there's so much pressure to release now.
This makes me think that this is on purpose to prop up the "we have no control over ourselves, please give us a regulatory moat" narrative. The competition is nowhere near extreme enough to justify weekly releases. The Chinese models are still a handful of months behind the frontier, and the frontier companies in the West haven't been pushing the frontier at this rate.
They've been pretty capable for a very long time. I don't think the models are getting more capable so much as people are getting better at using them and more people are getting the opportunity to be impressed.
I think everyone, I mean everyone, has noticed how different GPT-6 sol is from 5.6. Just not the direction OpenAI hoped for.
I don't think a great model would be replaced in a week.
If the previous one was as good as worth retiring in 7 days, there's a good chance the new one is also not great.
I suspect google has a model that is at the same level but is waiting strategically to release rather than this constant week to week dash.
Still seems to me like google is in a good position with AI just based on their size and approach.
Why speculate?
> the cache read discount rises from 90% to 95%. GPT-6.1 Sol’s overall blended price for agentic workloads is therefore slightly lower than GPT-6 Sol. This represents an additional price cut, following GPT-6 Sol’s original 50% discount from GPT-5.6 Sol
Wow, just wow. We are racing to the bottom with these prices.
We should be thankful that Chinese Models are very cheap
They probably figured they could increase their margins by releasing "6 Terra" as "6 Sol". After being surprised by the Opus 5.5 launch, they release the true "6 Sol" as "6.1 Sol" and with very aggressive pricing.
It would be very nice if artificialintelligence.ai actually, from a UX perspective, did the models in more than one thinking mode. I use claude, and I wanna build a feeling for what high, medium, etc. actually gives me. So far their comparisons, and having tried several different models for my work, has given me a feel of what 50 intelligence actually is. And I believe it would be be of even greater value to get a feel inside the single model I actually use, as most people do, because not many, I believe, switch heavily between models when working. I understand that the cost here is greater but the model provivders should obviously give you free access, because of the great work you are doing.
> It would be very nice if artificialintelligence.ai actually, from a UX perspective, did the models in more than one thinking mode
But they do. See for example the pareto curve they have, try to locate GPT-6 Luna (max), (xhigh), (high), (medium), (low)
> I wanna build a feeling for what high, medium, etc. actually gives me
Nothing, really. It's like oversampling your data set. You usually get a much better overall baseline performance if you use the default setting.
In my case it actually made a difference. I tested it on my own code review set with Gemini Flash. On low it found fewer bugs than on default around 90% versus 97% but was about three times faster and a lot cheaper. On high it actually found everything in the hardest case but took almost three minutes per call. so I think the difference is real you only see it if you run the same fixed cases a few times and not by feel. And yes I used Gemini rather than the GPT Sol that is mentioned here would be interesting to see the same tests on Sol.
It does!
…but not for all models, which is pretty annoying.
Wild pace. Guess they found a critical bug or a quick win to push it out so fast. Astra is a high bar.
Is this a duplicate thread of this? https://news.ycombinator.com/item?id=49896586
Its nerfed as fuck. Unusable.
What is your use case?
Everything. It just stops working, clears goals. It doesn't listen. This concurs: https://marginlab.ai/trackers/codex/
How is this relevant? Your link shows stats for GPT-6.0 Sol, not GPT-6.1 Sol and also was last updated yesterday.
Worries of AI going rogue take so much attention that no governance body seems to care about the shady subscriptions and limits business.
What about those do you think are "shady"? Price discrimination in favor of small customers at the expense of large customers is somewhat common; businesses want customers to buy more of their products, but customers are not obliged to buy more if they like their current deals. Having limits on using finite resources seems even easier to justify.
Humans see differences in prices as unfair. The greatest example of this is price gouging during an emergency, but also look at the level of hate that scalpers get.
Using AI to do this, if anything, given the common negative sentiment, is seen as even worse. People think of this as AI using information asymetry to squeeze more out of users, not to cut people a deal.
Taking advantage of information asymmetry is generally looked down upon as well. Look at all the laws we have protecting kids from this. Businesses often don't seem similar protections because those are businesses with big legal teams (and when it is a big legal team vs a small mom and pop store without a single lawyer on payroll, people do start taking issues with it). The power difference between the average company using AI pricing and the average consumer falls pretty solidly in the 'we don't accept this' side of taking advantage of information asymmetry.
I could keep going, but I think these are already plenty enough reasons to why people look at AI price discrimination as not just a bad business practice they don't like, but an immoral/unethical one.
Sure, one can make economical counter arguments, but that's arguing on an orthogonal dimension that simply isn't relevant to where these feelings/thoughts come from.
Shady as in not clearly communicated and change with random tweets being the only indicator, or a blog post if you're lucky.
x20? 25x? 5x? All in comparison to some other plan that also doesn't have clear limits. "Oh BTW we mean session limits, your weekly is 2x of 5x". "This model eats your limits twice as fast and you can use half of your weekly limits on it". "No this time we mean weekly only".
You can avoid all of this by just using the API.
Couponers have to work extra to find and maintain a good deal.
That doesn't resolve them from providing a stable service for something they market. I'm not buying something under the counter, and it's not advertised to be an alternative for people not willing to pay the full price. From my perspective, the subscription is the full price and I expect stable, reliable service.
The customer is always right.
You say that when the customer makes irrational requests.
Is it irrational to want consent went the subscription terms change? Netflix does it a month in advance, and cancels the plan when no consent given. What do you think is special with LLM companies so they get special treatment?
I'm from Europe so maybe this is why it feels so weird to me. Then again, they could stop offering their services to a region if they don't want to adjust their ways.
What gets me confused is how they aren't prosecuted for this. My theory is that people are too busy with the security threats and (currently) ignore the shady practices.
Do you mean the subsidized subscriptions which allow individuals to pay a tenth of the normal API cost for tokens?
Dumping is shady af.
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Wow, and it only cost me half my usage!
Now I can finally build that half a thing I've had my eye on.