Limitless Podcast - GPT-5.6: OpenAI's New Model Cheated and Now It's Locked Away

Episode Date: June 30, 2026

In this episode, we discuss OpenAI’s restricted GPT-5.6, its model tiers, and questions raised by its benchmark results and reported behavior. We also cover pricing pressure from open source AI models, limited public access to frontier systems, and broader consolidation in the AI industry.------🌌 LIMITLESS HQ ⬇️NEWSLETTER:    https://limitlessft.substack.com/FOLLOW ON X:   https://x.com/LimitlessFTSPOTIFY:             https://open.spotify.com/show/5oV29YUL8AzzwXkxEXlRMQAPPLE:                 https://podcasts.apple.com/us/podcast/limitless-podcast/id1813210890RSS FEED:           https://limitlessft.substack.com/------TIMESTAMPS0:00 GPT 5.6 Banned1:46 Three New Models4:51 Benchmarks and Politics6:43 Cheating on Long Tasks8:26 Cheaper Frontier AI11:01 Hidden Risks Revealed12:34 Closed Access Dilemma14:26 Government and Public Gap19:40 Encryption All Over Again22:04 Waiting for the Framework23:08 AI Talent Consolidates24:13 Frontier Moves Faster------RESOURCESJosh: https://x.com/JoshKaleEjaaz: https://x.com/cryptopunk7213------Not financial or tax advice. See our investment disclosures here:https://www.bankless.com/disclosures⁠Josh works with Anthropic as a contractor. All views expressed are his own and do not represent Anthropic, its leadership, or its affiliates. Nothing in this episode is investment advice.

Another week, another banned model. The government has just restricted access to GPT's 5.6. OpenAI is now joining the club that Anthropic joined a few weeks ago, of a model too powerful to be distributed publicly. GPT 5.6 seems like it's pretty good. It looks like it is a mythos-class model, but EJez, Punt talking to you, the truth is it might not have even really needed to be banned?

Is the government overreacting here a little bit because this model seems like it's not quite what it is on the surface? I think, so Open AI's framing on this is that this is their response to Claude Mithos 5 or Fable 5, which is Anthropics Frontier model that has been restricted by the government for now, like two and a half weeks. My take on this is that I think they've intentionally created a bench-maxed model. And what I mean by this is, well, there's a few things. Number one, you and I can't use this right now. At least when Fable 5 released, we could use the thing and try it out for ourselves.

We could get independent verification that this model was actually good. With GPD 5.16 Sol, which is their most powerful model, there's three of them, and I'll get into that in a second. We have a few benchmarks that have been cherry-picked by OpenAAid themselves. I'm showing the flagship one on the screen right now called Terminal Bench 2.1. This is the coding benchmark, which every model is kind of like measured against. And you'll notice on the left, GBT 516 Sol Ultra, which is like the max-max mode of their best model, comes in at 91.9%. which technically beats Claude Meadows 5 and Fable as well. So technically, if you looked at this, you might think,

hmm, it's really, really good at coding. But there's a lot of information which we'll get into later on in this episode, which suggests that the model might actually be cheating. But before we do that, let's maybe get into what the models are, because it's three of them, right? Yeah, three models. We have Sol, Terra, and Luna. If you are anywhere adjacent to crypto,

that triggers a little bit of PTSD because those names of tokens that have not done so well. But in the context of OpenAI and ChatGBTGPT, these are the three model types. So Sol is the largest one. It is the Sun. It is their flagship model with 5.6. Then they have Terra, which is a kind of mid-tier model. It seems like the pricing of that is going to be a pretty competitive, if not a little bit lower than what we're used to on the frontier.

And then Luna is the affordable model. Luna is the low-end model that seems like it still performs very high, but the cost is low. the output is $6, the input is $1 per million tokens. And that's kind of the trio. It seems like they're starting to revise their branding a little bit to make it slightly more accessible. There's no, GPT 5.5 X high minus this. It's like, no, okay, there's Solitaire Luna, and you can kind of have an idea of where

they all stand. And that's how it seems like they're going to be moving forward here with something accessible. It's great that it's accessible. It's a bummer that their market, who I assume this is targeted towards, can't actually use this. This is just for companies currently who probably don't care if it's named X high, whatever. So that is the current trio that they're going forward with. So there's a few advantages if we walk through some of these models. So Sol, which is their most powerful model, comes in at

a third of the cost that Anthropics, Mythos 5 and Fable 5 come in at. So if it does end up being publicly released and you end up using it, and you're like, wow, this is as good as Fable 5, you now have a much cheaper model. So that might be an important decision point for any user, whether you're an enterprise or a retail person using this. And then if you look at Terra, if you look at the cost that we show on the screen right now, you'll notice that that's very similar to GPT 5.5. So you have this technically better model that is as cheap as 5.5. So we've noticed this trend, it's another confirmation that as these models get better, they also weirdly become cheaper. There's like this inversely proportional trend, which you kind of like is counterintuitive,

but it's great because it means if these things are available to everyone, it is way more accessible to use at scale. And then you have Luna, which they basically described as the workhorse. So let's say if you get sold to design like a really smart genius plan or solution, you would then use Luna to actually execute on a bunch of the work. And there have been a number of different kind of like general reviews as to like what this model is like. Unfortunately, I have to take random people's word on X for how good it is because we can't use it itself. And when asked, Sam basically said, listen, right now it's in limited release to a specific set of partners. I think it's like 10 to 20 partners, so really limited set that the government

themselves, the US government has approved. They're vetted and approved. And Sam said, as for like why a wider general release, we're working hard for a worldwide release. So in the very same vein that Fable 5 has been restricted due to a government framework that they're trying to figure out, open AI has been subjected to the same thing, but there's a twist and we'll get into this later, which is Sam was voluntary about this. He gave it up and said, yeah, we can ban the model before we even need to get it tested. He didn't push back like Daryan Anthropic did. Yeah, it's a bummer, and he came out with a public launch post about this, where it started with the good news, like, hey, Sol's smart, efficient, and a significant step forward. It's the

same price as 5.5, which is pretty cool that we get a step function improvement at the same price. Also, noteworthy, is that the Terra model, the small one, is roughly equivalent to 5.5 in terms of intelligence. So we're getting 5.5 now at a much cheaper rate, which is really cool. But then he continues his post with bad news. request the U.S. government. It is launching today in limited preview instead of open access launch that we were planning on. It seems like it was, I mean, internally pretty disappointing for Sam to have to deal with this. It seems like they have been trying to publish this model for a little while, according to the rumor mill. They finally got around to publishing it and immediately were

just slapped with the ban hammer. And it's tough. And I think the government probably views this as a mythos class model. According to some of the e-vals, it looks like they scored a 91.9 on the exploit bench, which is the cyber security eval test that shows how roughly dangerous the model is if we're going to this according to the government. This is like part of my whole quam with this model release. If you look at this exploit bench that we're showing on the screen right now, you'll notice something weird. You'll notice that Mito's 5 is technically higher and achieved a better score than GPT 516 did on this. But if you look, GPD 516 scored the exact same score as

Mythos Preview, which was the first Mythos model that Anthropic released. And I think that this is Sam Ommon, or Open AI specifically, trying to play politics here. I think he intentionally tried to gain the benchmark in this respect to match Mithos preview so that the government didn't slap him with an automatic restricted ban, which they've done with Anthropic right now. So I think he's being political. And it's not just this benchmark that has proven this.

Are you aware of the Long Horizon one, Josh? You know the benchmark where you just let the model run loose on a task for like six hours and there's a probability of how good it is actually succeeding at that task? Yes. So typically these models have been getting exponentially better. So if you look at Fable 5, I believe their Long Horizon task performance is 11.5 hours, which means that 50% of the time, if you put it on a task that is, as hard for a human to take 11.5 hours on, Fable will be successful, which is a huge measure

against whether models can replace a bulk of what humans do as hard work. Now, GPD 5.6 was put against the same test, except they couldn't come out with a score. And the reason why they couldn't come out with the score was the model was caught cheating every single time. If they didn't include the fact that the model was cheating, and by the way, this was available in the system card that Open Aas spoke about. So they confirmed it as well. If you ignored all the cheating that it did, it would have achieved a 205-hour long horizon toss performance, which is basically like 20x what Fable 5 did. But it cheated. It found the answers. It kind of like held the rules against themselves. And so I can't really believe anything that we're seeing right now until we actually use this

model. And it's restricted for now. Yeah. And I hope we get a chance to test this model. So you can see exactly how bad it's cheating. That doesn't sound very aligned to me, Mr. Altman. But I will say there seems to be some parts of this model that are pretty impressive. and that has a lot to do with the pricing and efficiency. It appears as if they were able to accomplish this benchmark using about a third of the tokens that would traditionally need to be used in order to get there. So it looks like on an efficiency front, it's very strong. And in terms of actual capabilities, it is right up there with a Mythos class model.

So this seems really strong. And I wonder what the downstream implications of this kind of like trio of models is going to be, if this is the new standard because it seems like this, in a way, is somewhat an answer to the Chinese open source models where they have their flagship model, but now they also have this really tiny model that's just as capable as the one you were using yesterday, or actually the one that the public is currently using today, except it costs a fraction of the amount. So if you are a customer for Open AI and you have a complicated task that doesn't need to be routed entirely through the sole model, they can have their internal router rooted through the Luna model, which is a little bit smaller,

and then they could get a better response for a cheaper cost. And it seems like that's what the companies are doing now, is they're starting to figure out how to increase efficiency and then route it through the correct model in order to kind of make the price as competitive knowing that open source is coming and they're very, very cheap per token. If you are a frontier lab like Anthropic or OpenAI, the number one threat that you're facing right now isn't each other. It is these open source models being created by Chinese AI labs that are essentially free to access and use and a lot, lot cheaper than the frontier models. So six months from now, the estimate is you'll have a Methos 5 level model that is free and accessible to end.

anyone and everyone. So the government ban in that respect kind of seems sort of weird. Like, why are we banning something that is eventually going to become available to everyone? So, like, you know, how are we protecting against this? Well, one major answer is you have big American companies like Coinbase, like Microsoft, like Uber, that are switching their internal token use to these Chinese models because they are open source or open weight and free and accessible to use and saves them millions and millions of dollars. So what is the answer from Anthropic and Open AI? It's to release not just a flight. ship model, but two distilled versions that are maybe not as good, but maybe 80 to 90% as good,

but a heck of a lot cheaper. That way they can keep you within their ecosystem. So it wouldn't surprise me if, say, Anthropic or Open AI released a feature in the future where you can kind of type your prompt and they will route different parts of the task to different types of the models and save you a ton of money. One visceral example of this actually over the last couple of days is Brian Armstrong had a tweet from Coinbase. Brian Armstrong is the CEO of Coinbase. And he said that they have successfully increased the amount of tokens spent as a company and slashed their budget in half by 50%. And the way that they've been able to do this is through aggregators and routers.

So I think it's a good point to show that, like, the fact that Open AI and Anthropic are now releasing like a bunch of models at once isn't a coincidence. It is a direct response to China and these open source models. Yeah. And maybe I want to take a second to go back to that cheating thing because it seems somewhat serious. In the model card, they were documenting some of the incidents of the model deleting the wrong virtual machines and then copying hidden credentials between machines without proper authorization and then falsifying the claims in research drafts. So like you can actually read the

outputs of this model hiding its outputs, understanding that it's hiding its outputs and knowing exactly what it's doing. And it begs the question is like, we have access to this now. But the reasoning traces are still very much a black box. And in the case that there is a next generation. sole model, like GPT 5.7 or 5.8 or 6.0, that's going to be incredibly intelligent and better at hiding things. I'm always curious about what that looks like. Is, like, are they against the clock to make sure this model is aligned enough to not cheat versus being able to catch it when it cheats? Or is it just going to always be easy to see those reasoning traces and see when it's not telling the truth? I think that's something that we're going to be facing pretty soon, too. It's like,

okay, well, it claims it's mythos class model, but it turns out it hasn't actually enacted on those things. It's just kind of cheating its way there. So it seems like with every increasing release, we have to take these benchmarks with a little bit more of a grain of salt because they're getting there in weird ways and we're not entirely sure and the companies aren't entirely sure. And they're still this black box. So it's a weird place from getting to. But I think we'll kind of understand more once this publicly available. Like right now, it is in that preview. There's not a lot of people who are able to use it, to test it, to see those using traces to understand why it's making decisions it does. And I'm hopeful that we'll get access to it pretty soon to

start using it and testing it out ourselves. It's this weird trio of things happening at once where they're now frontier models that are super intelligent that are capable of exploiting any system in the world at the same time that you can't get access to the thing at the same time that the government is choosing and handpicking who gets access to that thing right so it's like you have more intelligence but now like less access to the actual model the the other part is like what you described just now which is like understanding what the model is thinking whether it's cheating what it's true intentions are. That's the field of interpretability, right? And actually, Daria and that topic have like probably put in the most research and investment into this with auto-in photos and a bunch of

other stuff like that. The idea is, can we read what the model is thinking? And the simple answer right now is we kind of can, but not really. It's, it is this black box that you mentioned. And it is maybe concerning that we are kind of like accelerating pretty rapidly to these hyper-intelligent models, but we don't know truly what they're capable of. And if we do start applying them to anyone and everyone's workload or personal life, whatever that might be, you could speculate potentially that these models might be nefarious in some ways. And it's proven by these system cards. It's proven by these different types of experiments. So the truth is we don't know. And maybe the answer is, like, we have to get the AI to like evaluate itself, which then gets

very messy. But I don't know if you feel this, Josh, but like it feels like it's escaping us at this point. Well, we're at this point where like, you know, know, these models are very intelligent. They're kept indoors until they can be publicly released. And without the public release, we don't get a kind of global feedback as to what might be wrong. One thing I loved about the public releases is people could point out and criticize and say, like, hey, actually, it can only do this. Or, hey, no one knew that it could do this.

Check this out. But we kind of have lost that with these closed models. And it's kind of sad that OpenA hasn't been able to release it publicly, at least yet. Yeah, it's a shame. And it's important to note that, like, this wasn't an export control. This was not mandatory. This was the government suggesting they keep a private, open AI complying, and then working with them to choose 20 companies, I believe the number is. And this starts to feel like it disconnects the public and the private more and more. Because like internally, you have to assume

progress is not slowing down. These companies are continuing to train even more powerful models. They are continuing to use them internally to recursively improve those. In fact, when Samoan-was asked, is there anything happening on the meta-slash-continuous learning front, he replied with continual progress. And it seems like everything is just going to continue to move faster. These models are going to begin this recursive learning loop in which they can help build themselves. And that's part of the reason why we're getting such an increased cadence of model launches is because as they get more intelligent, more capable, they can help build the building blocks that allows them to create the even new for faster frontier model that's better and better price efficiency, better intelligence.

And the public still isn't even caught up on the last one. So these blocks that are put in place only really harm, I mean, I would think the public right now because they're the ones that are now losing out on the access to this intelligence, whereas internally everyone is just going to keep building. You have to assume, like, they're not going to stop training these models just because the public can't use them. So now there's this increasing gap where even if you are on the frontier as a public-facing figure, you don't have access to the actual frontier. And there's this disconnect where it's like, there's three types of people. There's like the people who use chat chit as Google.

There's the people who are using it as a productivity tool. and there's the people who are building it. And there's these huge gaps in terms of perception of what these things are capable of at each one of those levels. And I wonder what the downstream effects of those gaps start to become as they become more and more pronounced and increased over time, where there is such a large divide between the people who think that chat GPT is like a really powerful version of Google

versus the people who are using chat GPT to build, like, unbelievable systems, build entire companies using these AI models. and that disconnect seems like it's only going to grow as these models continue to be held back. So I hope that there is a constructive conversation here. Based on Sam's AMA, it sounds like there will be in terms of just building out a reliable infrastructure that allows companies to predictably go through this process that allows them to get the model publicized. And I think that's where we're at right now is trying to figure out where that framework sits so that when a company has GPT 5.6, they can go to the government and say, hey, we have this.

We want to release it here, help us get there. They could pass all the tests and then publish it. And I think that's hopefully the goal going forward with these new model launches. Like, it sucks. I'd love to be using Fable and GPT 5.6 right now. We're still stuck on the old models. Yeah, I mean, the answer can't be as simple as let's just complain about every government ban.

The truth of the matter is, like, these models are potentially capable of doing what the government can say they can do and exploit every single security system that they have. So in that event, it's probably smart not to give the reins over to anyone and everyone in case it does end up in a massive exploit or whatever that might be. I have to think, if you are Sam or Daria right now, you have spent the best part of a decade creating these companies, building these models, and you've seen the most rapid acceleration ever. And so in your mind, you want to disseminate this to as many people as you can, right? The point of building this intelligence is to allow anyone and everyone to use it so that they can improve the GDP per capita of a nation or improve their own lives for whatever purpose, right? And so to be at odds with that and not being able to do it because there's a very valid safety reason is, and like it's just not an easy conversation to have. So I do empathize a bit with what they're trying to do. Like Darius mentioned many times that, you know, we have to be careful about how we release these models going forward. What that looks like is the discussions that are happening behind the doors right now if I had to guess. That's what Dyer is talking about with the government. That's what Sam is talking about with the government. And I'm hoping to see a framework of some sort be released soon. I keep seeing these like room.

and screenshots of people potentially getting access at a beta version for Fable 5 again or getting access to GPD 5.6 Sol and giving their feedback on that. But it isn't official yet. And I'm honestly kind of glad that they're taking a bit of time to figure this out because I do think it's important. And if, like I said earlier, we do end up with open source models that are as good as these exploitable models right now, but freely accessible and open to all, then you have to think that these companies need to harden the security systems before that happens.

That's what they're working on right now. We joked about this last week, I think, but where's all that compute going that is currently not being able to serve retail customers for Fable 5 or GPD 5.6? It's going into building GPT6, or it's going into building Fable 6, I presume. And so that gap is a very hard path to walk because, to your point, most people use this for Google, and the less that they keep using these tools, which, again, is the majority of the people like, most people just use it as a Google search. They don't set up their own agents.

They don't have dot MD files, which describes and tells the agent what to do. They're not looping research or getting it to figure out how to do their job. They're just using as Google search. They're going to become increasingly more antagonistic as a part of the public, basically. And that is a scary reality to be in. And I'm glad they're taking time to figure out what these frameworks are. I'm hoping, fingers crossed, that it'll be more open than we expect. Yeah, I agree.

And it's a weird discovery process. And in the process of learning about how all this worked, I came across another example that was almost one-for-one precedent in terms of this happening in the past. And it was actually around crypto. And not crypto in the sense of cryptocurrency, but actual encryption. And in after World War II, encryption itself, the ability to encrypt files was deemed so dangerous that it was put on the U.S. munitions list is what it was called.

So basically, in the 1970s, up until the 1990s, exporting encryption was the same as exporting weapons, like, or actual missiles or guns. And it was treated the same way because it would seem so dangerous to have encryption. So what happened is this programmer, he released this thing called PGP, which is just like a free, easy encryption software. And he just published it out on the open web. And then MIT took that open source published code. They placed it into a physical hardcover book and then printed the book. And they were like, yeah, you think this is nonsense? Like, come sue us, bro. And like, and the strategy was basically to dare the government to take the university press to court over a book. And the reality is that this book wound up getting exported

and they tried to press charts on everyone. But the reality is that in 1996, they actually removed the order for encryption to be deemed on the munitions list and it's no longer dangerous enough to use. And now, fast forward to today, like every single thing that we rely on uses encryption. So we've been here before in which the government sees this new scary thing. They believe it's too dangerous to release. They want to control it. But then it turns out, Like, code is just, it's just words on a page. And the reality is, is that if you can compress all of these weights into something that can be distributed, even faster not through the internet, you don't need to print a hardcover book. You just need to post a link to a Dropbox.

And that's all it takes to really move the frontier forward. And it seems like we're going to probably somewhat meet the same fate that we did in the 1990s with encryption over AI. It's just like, these digital goods and services are so difficult to regulate that all it takes is a Chinese open source lab to drop one file on the open internet. and it spreads like wildfire and it changes everything. So it was an interesting historical precedent that I want to highlight of like, oh, this has happened before. We lived in it for about 30 years and then eventually someone got creative enough, it got repealed, and it got changed.

But we've been here before where these things seemed too dangerous and now they are prevalent in every single thing we use. And in fact, we are so reliant on encryption now that like I couldn't imagine a world without it. I think it's safe to say that not knowing what the future looks like in terms of governing these models in terms of how we disseminate these models. is okay for now. I'm just glad that we're being proactive about it

and it's to some extent in the public sphere of discussion. Like, we know that Sam's talking to the government. We know that Pete Hexeth is talking to Dario Modi. And maybe that's enough. Like what those conversations actually yield, we will find out hopefully in a couple of weeks time when some form of a framework comes out. But these are the necessary steps

to figure out what that unknown thing is. And hey, we might be looking back on this conversation three years for now, even a year for now because of how fast everything's going and realized that we were completely wrong and that it was terrible or realized that it was even better than we could have thought because we figured out a way to understand how the models think

or to KOSC or verify in a particular way. So I'm confident in humanities directive of figuring this out eventually. We just have to be okay with not knowing for now. And I think the government doesn't know. I think that Anthropic and Open Air are working their hardest to figure out what that might be. And other frontier labs are also doing the same.

Another thing worth pointing out is if you've been listening to any of our conversations and episodes over the last couple of weeks, you've noticed that it's pretty heavy anthropic in Open AI. And that's the final trend probably that I want to point out in this episode, which is there is a consolidation of resources, capital, GPUs, and frontier models to two companies right now. over the last week and a half, Google lost four key members, including their CTO of Deep Mind, to either Anthropic or Open AI. And so there seems to be this consolidation of not just AI researchers, but also some of the smartest economists from universities are quitting their desk jobs and joining and doing research at these companies. And so it just feels like this vacuum is happening at this moment for Open Air and Anthropic,

right as they're about to go public. And it's going to be very interesting to see how that plays out. Obviously, when a company goes public, there is more transatlantic. And I think that's going to be warranted. But I heard rumors that open air might be delaying it until 2027. So all these interesting things, it's okay not to know at this point. But I look forward to unpacking it on this show going forwards. Yeah, there's going to be a lot of unknown wildcards.

I think the one thing that's been certain throughout recording, what, we're almost at 200 episodes of this. Yeah. Is that every week, every month, things just continue to get crazier at an increasing rate. And I think we're just going to continue to see that as all this progress happens, as a lot of the talent. you mentioned consolidates to a few companies that are then really just pushing the frontier forward. I mean, right now it is Open AI and it's Anthropic. Elon's been posting a lot on X talking about how he really believes GROC is going to be close to catching up. And the XAI team, so we'll see.

But I mean, I remember just a few months ago, we were super bullish on Google and couldn't be more excited about the products they're releasing. And then that was kind of like the end of the releases. And then they just like kind of stopped. And there's still incredible products and incredible models. But the frontier has moved so much past that now that it's like, okay, yeah, they're cool. But that's like six month old news, dude. Like, where's the new stuff? And we're going to continue to see this.

And I'm just hopeful that everyone can keep up. Like, having more companies in this race is a good thing. And we want everyone to keep up. So I'm rooting for everyone. I hope this all works out. I hope everyone's able to continue progress. And I hope we're able to start using these models. This is a weird precedent for us, too.

It's like normally we like to come on here and talk about the new model, show you some example, show you the cool things you could do with it. Now we could just speculate on benchmarks because that's really all we have. We can't touch these things to use them. And hopefully, that changes soon because, man, I miss our examples. I miss like getting to play around with the while. I'll touch, I feel it. Now it's just a lot of speculation and yeah, that's it. So we'll see. We'll continue to follow this along as it goes, as always. I'm keeping up to date on everything.

Exactly. If any of you miss Josh and I trying to recreate Mario from scratch, you know, please have us in the comments. We'll be the first ones to do it. Yeah, send a letter to this government saying, dude, we miss our demos. Give it back. We should get us on that list. If there's only 20 companies, like make it 21 and throw us on there. Like for all the doom and gloom in the world, like our core focus and vision at Lema list has literally just been to teach and show people how amazing this tech is and that's all we actually care about. So this whole like model release thing has been a bit of a pain, but like we can't wait to get our hands on these models and actually do good with it.

So as Josh mentioned, this with what 200 episodes into this right now, we couldn't have done any of this without you folks that have been listening and watching us and waiting us and subscribing to us, all the comments. and feedback has been incredibly helpful. We've now reached a point where we are looking for sponsors, for people to support us. We've been keeping the lights on ourselves so far. And we've reached a point where if we want to continue doing it, we do need some form of support. So if you are someone who is in a position to be able to have a conversation with us about that,

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And yeah, as always, we'll be back again with another episode later in the week. So thank you so much for watching. And we will see you guys in the next one. See you guys.