# Opus 5.5 vs GPT-6 Sol and Luna — Transcript (2026-09-23)

https://aidailybrief.ai/e/2026-09-23 · Listen: https://pod.link/1680633614

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[00:00:00] The first half of this week has seen not one, not two, not three, but four major model releases, including two on Tuesday this week, a pair from OpenAI and one from Anthropic

For OpenAI, GPT-6, Sol, and Luna continue their quest to build cost efficient models at every level of the intelligence stack And for Anthropic Early indications suggest that Opus 5.5 is a return to glory, or at least early adopter acclaim that the company has not seen since Opus 4.6

the AI Daily Brief is a daily podcast and video about the most important news and discussions in AI All right, friends, quick announcements All right, friends, quick announcements before we dive in. Now, as is normal when it comes to big new model releases, this will be a main only episode And honestly, we're gonna have a hard time getting it all in even with that, so let's dive in 

Welcome back to the AI Daily Brief. It's fairly undeniable that the best days on the AI Daily Brief, certainly the most fun, exciting, dynamic

" Ooh, [00:01:00] boy, I can't wait to be done with this episode because now I get to go do things" types of episodes are ones where we get new models. And yesterday, we got the absolute rarest of treats a thing which I can't frankly remember ever happening before, which is the two most important labs of the moment, OpenAI and Anthropic, both releasing new models on the same day.



certainly we've had models released close together before



In fact, usually the pattern that we see

is what SpaceX AI did yesterday, releasing their model in advance So as not to be drowned out by models that they knew would get more attention than them

The challenge of the same-day release is that it inevitably gets people to ask not what's valuable about this particular model and where it's going to fit in my rotation, but instead which of these is better and what does it say about which lab is in the lead?



So today we are going to go through what was released, where things stand on the benchmarks, the first reactions examples around some particular use cases. The impact on the competitive landscape



what it says about the whole pacing the frontier [00:02:00] thing, and in the community's estimation, who won the day

The first model we got was Claude Opus 5.5

And frankly, it's been some time since an Opus class model was the big show



now the biggest reason for that is, of course, the introduction of Mythos and then Fable. But even before that, people had so much love for Opus that 4.7 and 4.8 were for many, if not a regression

certainly very incremental at best, and not even incrementally ahead in certain cases

And in fact, when it came to Opus 5 People just genuinely did not like the thing at all



now jumping ahead to where this conversation is landing, the perfect encapsulation comes from AI content creator Peter Yang

who used a meme of an illustration of a horse Beautiful and complete at the beginning

representing Opus 46, of course, to a much scratchier, more simplistic and childlike line drawing for Opus 47 and Opus 48, culminating in near scribbles for Opus 5, to finally once again the beautiful front side of a horse in perfect illustrated detail for [00:03:00] Opus 55



the people, in other words, are really liking this model



but how did Anthropic pitch it? In their announcement post, they said that Opus performs at the level of Claude Fable 5.1 for most tasks, but costs 40% less to run, not even just than Fable 5.1, but than Opus 5

5 In their announcement thread, they point out that it is a major step up from Opus 5 But also frankly, at least when it comes to the benchmarks, it's also a step up from Fable 5.1

On TerminalBench 4.0

The model jumped from Fable 5.1's fifty-five point eight percent to Opus 5.5's sixty-six point four percent

Cursor Bench, Frontier Code V1.1 And Humanity's Last Exam also all saw jumps, not only from Opus V, but from Fable 5-1 as well



In fact, there wasn't a single benchmark

That Opus 5.5 wasn't ahead of the other Anthropic models, Fable 5.1 and Opus 5 And only two Automation Bench, which measures business workflows, and Terminal Bench Science, which measures agentic scientific research, where Opus wasn't ahead of [00:04:00] GPT-6 Astra as well

Importantly, this is not just a performance gain

But also a pricing gain as well

The Claude account wrote, Claudethat because Opus 5.5 requires less compute to serve than Opus 5, Its pricing is consequently down compared to Opus 5's $5 per million input tokens and 25 per million output.

is $4 per million input and $20 per million output. however, because of additional efficiencies, they said that the cost gains will actually be about 40% as opposed to just the 20% reflected in the cost



a-- Although it's not pitched as a model focused on speed, they do note that because many of its default effort settings deliver better results than other models running at higher settings That Opus 5.5 generates output much faster than those other models, including an average of 30% faster than Opus 5

5 as a as a cherry on top they increased five-hour usage limits on all of their, Pro Max and Team plans

And through in the rest of the subscription users a rate limit reset which can be used whenever people need

On the safety front, they note that this is their first model released in the wake of the Pacing the Frontier note and that Opus [00:05:00] 5.5 was tested before release by external evaluators, including Frontier Design and METER



And overall, they argued that Opus 5.5 is basically the best aligned model they've released yet

Sam Bauman, who works on alignment at Anthropic, wrote, " "We We think Opus 5.5 is sufficiently safer than its predecessors that releasing it more likely than not reduces risks related to misalignment."



Now, Opus 55 alone would be more than enough for a complete episode

But OpenAI was determined not to let Claude have all the fun

and in the early afternoon Eastern Time on Tuesday, they dropped their own set of models, GPT-6 Soul and GPT-6 Luna

And if And if the Opus 5.5 announcement was a little bit focused on cost and efficiency



the six Sol and six Luna announcement was all about cost and efficiency



their announcement post on X reads, " GPT-6 Sol and Luna build on the advances behind GPT-6 Astra, bringing much of its strengths into faster and more affordable models to support work at scale. We've also made caching and inference more efficient, and we're passing the savings directly to you.



Fifty [00:06:00] percent lower API prices for Sol and Luna compared with GPT-6--promotional pricing. In other words, they're not just talking about this pricing being lower than Astra. They're cutting costs from the previous generation even before that.



and and what's interesting is that they're clearly presenting cost As enabling new ways to use the tool as well. As they put it, higher usage limits and lower cost give you more flexibility and room to iterate. In other words, one of the ways that they think that people should cash in on those cost savings

is to be more iterative in how they use the model

Going back a couple of generations of models at this point



one of the subtle divides between Codex and GPT usage

And Claude and Fable usage, on the other hand

has been an impulse to use Fable and Claude for more hands-off long-running tasks and the GPT models inside Codex for things where you need more interaction and iteration and these new Sol and Luna models seem to build on that as well

Like the Opus 5.5 announcement, OpenAI also points to advancements in alignment



And And what's notable when you get into the benchmarks on the OpenAI page

is that where Anthropic is still using the traditional charts 

which show a [00:07:00] benchmark name and then a set of percentage scores across a set of models. OpenAI has entirely abandoned that



only showing benchmarks on graphs that map performance on the Y-axis versus cost on the X-axis

Sam Altman tweeted, Sam Altman tweeted, "GPT GPT-6, Sol, and Luna are big improvements on intelligence, alignment, work output, coding, computer use, and more over their 5/6 family predecessors. They are also half the price per token and even less per task." He he then went and followed up and really reinforced just how important this way of thinking is to OpenAI now.

" Especially compared by per task pricing, which is the metric that should matter," Sam says. " I don't think there is anything competitive anywhere in the market. We We want people to be able to use tons of AI. it's important to being able to explore this new renaissance in front of us."



Putting some magnitude around what they mean by tons

in the announcement post, they point out that valued at API prices

the median researcher at OpenAI uses in tokens per day While the 90th percentile of researchers use $7,000 of tokens per day

Almost understating it, they write, " "As As coding [00:08:00] agents take on longer and more demanding tasks, the cost of sustained use matters more."

The upshot? " GPT-6, Sol, and Luna," they write, "combine strong coding performance with lower API prices, giving developers more room to iterate and teams the confidence to be more ambitious about what they ask Codex to take on."



but what about external benchmarks?

even for those folks who care about benchmarks Everyone assumes you have to take any internal measures with a grain of salt and one of the first places that people look when a new model comes out is independent sources like Artificial Analysis



on on their new V3 index

GPT-56-Luna and GPT-6-Luna are basically score the same at 37 overall, while GPT-6-Sol slightly beats GPT-56-Sol

That said



both of those models get those scores at a lower cost per intelligence. Indeed, artificial analysis' first sentence is, " GPT-6 Sol and Luna push the cost efficiency frontier by having cost relative to GPT-5, 6 Sol and Luna."

In another positive highlight, artificial analysis found that both models saw a significant reduction in hallucination

Zapier also tested GPT-6 Soul



on [00:09:00] a slate of real workflows, they found that 6 Sol scored 33.2% compared to GPT 5.6 Sol's 28.77%, and did so at roughly half the price of 5.6



their summation was pretty simple. Upgrade any of your workflows that are using 5.6. It's cheaper and better, a rare combo

Opus 5.5, however, was a different kettle of fish entirely



Opus 5.5 was notable

Because it absolutely thwomped everything else to get the new top score on the intelligence index by five whole points Claude Fable 5.1 and GPT-6 Astro were previously holding it down at the top with scores of 53.

But Claude Opus 5.5 jumped all the way to 58. And while its cost and efficiency gains might not have been as much as GPT-6 Soul, Artificial Analysis did note that this increase in performance also came with a 20% price cut

And honestly, While I've been trying to keep this analysis largely aligned and equivalent so far

Watching people's first responses, there is absolutely no doubt which model among these dominated the [00:10:00] discourse Matthew Berman wrote, " Opus 5.5 being the absolute best model on the planet and being cheaper than Astra and Fable was not on my bingo card." Chubby Kiminismus on X writes, " "The more The more I use, the more I fall in love with Opus 5.5.

It's so good and so much faster and so much less verbose. It's everything I could have asked for. It's as if my beloved Opus 4.6 came back but better and rebranded as 5.5



Now Now we'll get more into that love. But what were some of the specific areas that people were testing Opus 5.5 on?



Aaron Levy and the team at Brox... Aaron Levy and the team at Box

brought brought it into, quote, "A variety of complex enterprise knowledge work tasks dealing with unstructured data



and saw not just improvements in performance

But major efficiency gains as well. Versus Opus 5, they found sixty-three percent fewer tokens used, forty-two percent less verbosity, and thirty percent faster

On financial services tasks, they found an increase of thirty-nine percent task accuracy. On cloud cost analysis and technology use cases, they found an increase of sixty-five percent task accuracy and in use cases related to consumer products and clinical [00:11:00] diagnostics,They found an increase of 17% and 15% task accuracy respectively



Now, when it came to the social media demos, as has been the case with the last several models, A huge amount of the posts were highly visual things like graphics, 3D and game design



which are interesting to look at on the internet, if not necessarily super reflective of the type of use cases

that most people are going to actually have for this model. Still, given how much 3D graphics and visuals in Blender were a big part of the story of the GPT-6 Astra release



a lot of the demos of Opus 5.5 basically have it mogging that model on exactly that type of use. and given that my analysis just a week or two ago or whenever it was that we got GPT-6 Astra



was about how much those sort of capabilities asked us to think In more expansive terms about the opportunities that AI opened up that, it's worth noting that Opus 5.5, at least initially, seems to be another jump in those capability sets as well

Alex Albert from the Anthropic team showed Opus 5.5 using Blender to make claymations with a single prompt



Chase Lean made an interactive coral reef wallpaper

And some people showed how the models were [00:12:00] powerful enough To use generated code rather than image models to create real visual representations of the world. Peter Yang shares what looks like a video of the Golden Gate Bridge, but notes that it's actually entirely generated using code by Opus 5.5

He adds, "From my testing, I think Opus is just as good at building 3D scenes as Astra."

Alex Albert again did something similar, visualizing a historically accurate San Francisco Market Street in 1906 pre-earthquake using only Opus 5.5 code and Blender, no image generation



Dan Wood says that Opus 5.5, quote, "Absolutely frame MOGs every other model

On these sort of 3D generated worlds, adding that it is a, quote, "Astounding leap in spatial awareness."

In a wildly viral post, Jake Eaton from Anthropic shared a number of different what appear to be paintings

And said, " For the past few months, I've been asking our models to paint. Opus 5.5 is very skilled at emulating different styles. Every image here is a Python program generated pixel by pixel. There is no image model and no off-the-shelf art software. Instead, it's about seventy-five [00:13:00] hundred lines of code using standard libraries to emulate different brush styles.

The agents don't use any pictures as reference, instead working only from what they know about each painter."

And And certainly this is where you can see these capabilities, which might otherwise be cool to look at on social media, but not necessarily all that relevant for most of us, maybe start to become a little bit more relevant

If the advanced coding capabilities of Opus 5.5 are enough that it can, as in the case of this post by Tack

generate a one shot thirty second long marketing animation. Well, certainly all of a sudden a bunch of use cases open up that you might not have thought of before



Tariq from the Claude Code team asked Opusto do a bunch of redesigns on his personal website



And then make a trailer with all of its iterations



And on first glance, it seems to have done a great job. For what it's worth, this is one use case that I tried on Opus 5.5 as well



And I was definitely pleased with its analysis

and review of my AI Daily Brief website

It was clear, had good concision of thought, and was very practical

although I will say for those who are just trying to live in the world of strictly betters or strictly worse's, it wasn't that it was necessarily [00:14:00] strictly better than Fable 5.1's analysis, it was just different

And And yet one area where almost everyone seems to be in agreement that Opus 5.5 is indeed strictly better is writing



Anthropic Sholto Douglas reposted their announcement and wrote, " Also important news, we fixed the writing."

So So what does that mean? Well, part of it is, As Theo put it in all caps, " They removed the em dashes."



but but what the Anthropic account actually said about this is, " Opus 5.5 communicates more naturally, addressing some of the most common feedback we heard on Opus 5. It puts the most important information up front and follows the writing rules you give it, which makes long sessions easier to follow

The team at Every who have one of the best benchmarks and processes for testing AI writing wrote, 5.5 produces the most readable prose we've seen from an Anthropic or OpenAI model

In fact, presaging a point that would come up a lot more later in the conversation, they wrote that while Opus 5 had made their writers kick Claude models to the curb, quote, "Makes us want Claude back in the room." They [00:15:00] continue, " "It It responds well to feedback, builds on the material you give it, and explains its choices where Opus 5 wouldn't

And importantly, it turns out that this isn't just an improvement on the output of writing but an improvement on the actual process itself. They write that Opus 5.5 is a pleasure to write with. It takes feedback without a fight and builds on material instead of handing it back tidier

in fact the team writes, the the biggest upgrade is not even exactly about the writing

It's about the feel and vibe of the model As the Everyteam puts it, " Anthropic fixed Opus's personality. It's not an obstinate little turd anymore



and this is an experience that many have had



AI builder McKay Wrigley writes, " Opus 5.5 equals the personality of Opus 4.6 that we all desperately wanted back and the intelligence and taste of Fable 5.1."



and certainly Anthropic knew it

Nat McCallis from Anthropic posted, " Opus 5.5 is way, way, way better than Opus 5. Sorry about that model. Please try this one."



So have people had any issues yet with [00:16:00] Opus there is one that stands out and that for certain use cases will be effectively a non-starter. Chief of Staff on X wrote, " I burned all my usage reset benchmarking Opus 5.5 against Fable 5.1 for complex open-ended legal work, i.e.

drafting client and court-ready documents, draft and contract redlining, legal and matter-related research. As far as I can tell, Opus 5.5 is much worse due to increased safety rejections and what I'd guess you would call poor effort budgeting."

Simon Smith writes, " 5.5 looks good, but as a life science commercialization company, I'm a bit concerned by this." Quote, " Because Opus 5.5 is comparable to Claude Mythos 5.1 in biology and cybersecurity, we're deploying it with safeguards similar to those on Claude Fable 5.1."

Simon continues, " "We We faced issues using Fable for some tasks because of these guardrails. We've applied for Anthropic's Life Science Verification Program but haven't yet been approved. we had used Opus when Fable refused a request because of Fable's biology guardrails, but now it seems like this won't be possible anymore with Opus either."

[00:17:00] It's not everywhere, but I certainly have seen a number of people complaining

about these overzealous safeguards, which is unfortunately the type of thing where if that hits your use case, it basically makes the model null and void

So what are some of the big takeaways for folks?

after this huge slate of releases Aaron Levy writes, " What an insane day in AI. The frontier models just became substantially cheaper with the Opus 5.5 price cuts, and now with GPT-6 Sol and Luna dropping token prices by 50%. The rate at which the cost per task on a like for like basis drops in AI is unlike any other type of technology in history.

And every time the cost of AI drops, the use cases you can deploy agents against dramatically increase. This is Jayvon's paradox applied to agents. making a bit of a prediction, Aaron continues, " These improvements will directly lead to broader diffusion of AI in the economy as we can use agents to process all of our data, scan our code for security issues, read through all log data to make decisions, have agent swarms in workflows, and much more.

The cost of tokens is directly correlated to these use cases being opened up [00:18:00] at scale."

And And putting some research heft behind that

Epic AI Research yesterday also dropped data arguing that, quote, " AI is getting cheaper more quickly than any other transformative tech in history."

At a given level of performance, cost has fallen around forty-seven percent per quarter since twenty twenty-three. They point out that that's four X faster than DNA sequencing, six X faster than compute, eighteen X faster than lithium batteries, and at least up to nineteen seventy-three, fifty-four times faster than electricity



Now, when it comes to head-to-head analysis, you can find people arguing all sides when it comes to which of these models are the most valuable in aggregate ToYourTaxes writes, " Cheaper, better Sol and Luna. I assume they're natively trained for being Astra's henchmen. This makes OpenAI's value proposition considerably stronger I suspect that Opus 5.5 wins on cost and multi-agent are effectively negated with Astra plus Sol plus Luna



and there were also many a folks

Just happy to enjoy the unique values that each of these different models provided on their own terms

In answering the question of whether there was a winner, Chubby writes, "Both [00:19:00] OpenAI and Anthropic gave us plenty to be excited about."



and and if you have any question about the strategy that OpenAI is pursuing, Sam Altman could not have made it any more clear. He wrote, " We want the OpenAI API to feature the best model at every price point and to be the best at every modality, text, code, image, video, et cetera. And then we all want you to come up with great ideas and build them and get to be happy users."

Again, remember that he had written in a separate post, " " We want people to be able to use tons of AI. It is important to being able to explore this new renaissance in front of us." I think in many ways, you you can view this set of releases from OpenAI as a continuation of the strategy that they've clearly been pursuing for a while now, which is be really aggressive about focusing on cost and efficiencies,

and building a model slate for an era in which people are moving away from a single model towards complex model architectures that actually match tasks with the right type of intelligence



Claude, on the other hand, feels like it was out to reclaim some momentum. And if that is the case, they've done so pretty successfully

Here's Here's [00:20:00] how Every put it: " Sol feels like an S-class iPhone release. It will give you much of Astra's power at about a fifth of the price. Opus 5.5 is the bigger surprise. is tempting a few people on our team to switch back from Codex. You'll love this model if you're already in the Claude ecosystem, and if you're a Codex user, it's worth a look, especially for your top-end coding tasks."

Now, in some ways, this is just a continuation of the divide which I mentioned before and which we've seen for several model iterations now. Dan from Every concludes that article, " You'll like Sol 6 if you want a fast, affordable daily driver for reading, writing, and getting things done. It's the model I keep reaching for.

You'll like Opus 5.5 if you're willing to pay more for stronger performance on ambitious coding and visual projects its best work surpassed Soul in our tests, enough to pull some of our team back towards Claude

And the pulled people back to Claude is a real take that I'm seeing from a number of different people Yu Chen Jin writes, " "Claude Claude is back. Time for me to open Claude Code again after ignoring it for a month."



and and if anything, the more time that goes on, the more positive I'm [00:21:00] seeing people get on Opus 55 This morning, former investor turned AI builder Jeffrey Emanuel chimed back in, " My God, Opus 5.5 is breathtaking.

It's just grinding through incredibly tricky stuff like it's nothing, finding bugs and problems that eluded Fable and Astra for weeks in some cases, and showing a level of agency and resolve I haven't seen before."

Now for me personally, I'm gonna save a bit of my personal analysis for a little bit more time with the models, which will come together in more of a how-to episode for getting the most out of both of these models later in the week That said, I do think that there are a few interesting observations that are worth closing on

By the way, this visual that I'm talking over was created by GPT-6 Soul Which added quite a bit of text that I didn't have in there, I left it perhaps as an example of particular model quirks

Observation number one is that even early adopters who are more ruthless about switching and who profess to really only care about performance definitely have personality preferences



so much of the excitement around Opus is not about, wow, it does this thing which AI has never been able to do [00:22:00] before, but about, oh my gosh, it is such a pleasure to use this in a way that it hasn't been for some time



I I actually think that this is extremely important. When there was such a big dust-up around ChatGPT 4o being deprecated, there was a temptation to treat it like a phenomenon exclusively for crazy normies who had gotten obsessed with the sycophantic model.

But as time has gone on

For as much as AI might be a tool to some of us, it's clearly a very different type of tool. it's a tool that at least approximate having a personality

And different versions of that tool's personalities and more or less enjoyable to use actually has implications for how much we can get out of them



in short, when it comes to LLMs, personality is UX



and we should make sure we're treating it as such

Observation number two

While it is definitely the case that if you were just looking to declare one winner from the standpoint of buzz and excitement, it would be Opus 55 the battle of LLMs is not really about one thing anymore It's about one, figuring out what the stack of options needs to be, and then two, battling for each slot in that stack

[00:23:00] 

In other words, even the people who loved Opus 5.5 the most aren't saying you have to use this for absolutely every single use case

And in many ways, I'd even argue



that although at first glance



GPT-6 Soul seem to be direct competitors. The way that their respective builders are thinking about them is a little bit different. what matters at this stage is individuals and companies being able to understand their complete set of needs and how different models and harnesses combine to best fill those needs with respect to performance, capability, cost efficiency, and speed

A third observation is that we're definitely in the era where you can't really separate models from harnesses anymore, at least not fully

For a lot of folks who are fully invested, for example, in the Codex ecosystem now, it doesn't matter if Opus 5.5 is much better. As long as their OpenAI options are close enough, they're not going to fully shift out of that harness

Will Will Brown from Prime Intellect wrote, " wrote, "I I keep switching desktop agents every week, and it's getting out of control. Codex didn't have Fable 5.1. Claude didn't have Astra. Amp had them both, but now doesn't have [00:24:00] Opus 5.5, so I'm back to Claude. Self-build is annoying to maintain."

We didn't talk much about harnesses in this episode, but give it about a week and the real proof in the pudding will be whether anyone has actually switched their overall behavior because of any of these model changes

Or whether everyone tested things, found out what they liked, and just went back to the ecosystem that they were already using before, dictated in large part by the harness where they store their context, their tools, their rules, et cetera



a fourth a fourth observation is actually not so much about either Anthropic or OpenAI



but about the fact that as these models were being released, the other big buzzy thing that people were talking about was, I would argue, the first AI product that is seeing a lot of traction where the people using it genuinely don't know or don't care about the model underneath

I'm I'm talking of course about Muse Which you can tell just from the posting from Meta's chief AI officer, Alexander Wang, the team over there at Meta is very excited about the uptake and reception for

How will it change this sort of new model conversation if the models actually start to get abstracted away? That's something we've [00:25:00] talked about for a long time, but which so far hasn't really played out in practice But who knows? Maybe we're at the beginning of the product rather than the model era of AI in which the models really will disappear into the system



the the fifth and final observation for today



is what this all says about pacing the frontier. One of the standard jokes that could get you a bunch of reposts and likes on X

Was people staring with bleary eyes at four major model releases in two days and asking some version of, "This is pacing the frontier?"

But I think Theo had the right of this when he wrote, " Hot take, this is what pacing looks like. None of today's releases were Astra or Fable tier. This is intentional. The point of pacing isn't to stop iteration and improvement. the goal is to prevent the development of bigger models from spiraling out of control.

Opus and Soul class models are a great place for our focus to go right now. Lots of opportunity for real wins without as much risk."

And I think that that's correct. If this is what we get from the pacing the frontier era



in other words, models that solve specific problems with previous iterations of those models and deliver [00:26:00] incremental performance or value gains at significant cost and efficiency gains. That seems like a pretty good place to spend some time And not one where somehow all sorts of adoption is going to slow down



Tariq from the Cloud Code team is clearly pointing at this as a key direction for the labs. He wrote, " "The right The right way to use model capabilities is not to ship 10X more features to prod. It's to spend more time understanding your users, trying experiments, building prototypes, learning about things you don't understand so that you can ship things that actually work."

In fact, I think you could argue argue that that because there have been such radical capability increases at such a fast clip for the entire history of generative AI post-ChatGPT The labs have never really had to think in product terms. the raw capabilities jumps have been so pronounced throughout the entire period



that they can just splatter the latest thing at us and we're gonna eat it up



but at some point The market of people who are willing to do that saturates. the percentage of business use cases that that approach can get to saturates. and you actually have to think in terms of human [00:27:00] and system realities



now I don't at all think that somehow this means that all of a sudden all the resources are going to go to perfecting existing classes of models rather than trying to build bigger things but it certainly shows that the endless pursuit of bigger models is not the only be all and end all for the labs

Anyways, guys, it is a pretty phenomenal day. If you haven't yet, I would highly encourage you to go out and spend some time with these new models. For now, that is gonna do it for this edition of the AI Daily Brief. Appreciate you listening or watching as always. Until next time, peace 

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