Opus 4.6 and ChatGPT 5.3-Codex Are Here and the Labs Are at War

Today on the AI Daily Brief, we've got not one but two new models that show exactly

where the leading model labs priorities lie, and before that in the headlines, looks

like we're going to spend a cool two-thirds of a trillion dollars on AI infrastructure

this year.

The AI Daily Brief is a daily podcast and video about the most important news and discussions

in AI.

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Welcome back to the AI Daily Brief headlines edition, all the daily AI news you need in

around five minutes.

We kick off today with Google and Amazon rounding out big tech earnings with a very unified

message.

AI CapEx is accelerating faster than ever.

Both companies lifted CapEx forecast significantly.

Google guided AI spending between 175 and 185 billion for this year, vastly outstripping estimates

of 115 billion.

This level would double Google's already high 91 billion in CapEx for 2025.

Amazon though came in over the top the following evening guiding 200 billion in CapEx for

2026 for a 60% jump.

With Google Amazon Microsoft and Meta all lifting expectations, we now have 650 billion

in projected AI CapEx for 2026 from just these four.

That's now more than the inflation-adjusted cost of the multi-decade U.S. Interstate

Highway project anticipated to be spent in a single year.

It's about two and a half Apollo Moon missions or four and a half international space stations.

Now on the actual earnings, there was a slight divergence in performance.

Google reported annual revenue of 400 billion for the first time.

They saw an 18% increase in overall revenue year over year and a 48% jump for their

cloud division.

Still Google Cloud was a $17.7 billion business for the quarter, which puts them firmly

in third place behind Microsoft Azure and AWS.

At the same time, they recorded by far the fastest growth rate and were the only hyperscaler

that increased their pace of growth.

Amazon's story was slightly less positive.

Net profit was 21.2 billion right in line with expectations.

Applied revenue growth was 13.6% for a slight beat, reaching 213.4 billion for the quarter.

AWS revenue growth was 24%, their fastest growth rate in three years, bringing division

revenue to 35.6 billion for the quarter.

While the numbers were fine, they didn't necessarily speak to massive monetization of AI bets,

and CEO Andy Jassy spent much of the earnings call justifying the massive ramp up in CapEx.

He told investors, I think this is an extraordinarily unusual opportunity to forever change the size

of AWS in Amazon as a whole.

We see this as an unusual opportunity and we're going to invest aggressively to be the leader.

Later in the Q&A section, he pushed back against an analyst who questioned the conviction.

Jassy commented, this isn't some sort of quicksotic top-line grab.

We have confidence that these investments will yield strong returns on invested capital.

We've done that with our core AWS business.

I think that will very much be true here as well.

Similar to Microsoft, both Amazon and Google said they were capacity constrained in their

cloud businesses.

They claim that stronger growth would have been possible if they had more GPUs on racks

in 2025.

Still, both companies saw a big drop in share price following their earnings calls with

Google following 6% on Wednesday night and Amazon losing 11% on Thursday night.

Now one interpretation of this is investors being uncomfortable with spending at these

levels regardless of AI derived revenue.

But there is also something more going on here that is worth exploring.

For decades, hyperscalers have been doing hundreds of billions of dollars in stock buybacks

each year, peaking it over a trillion dollars in 2023.

And analysts expect the hyperscalers to reduce or even end buybacks this year.

CapEx plans also seem likely to require debt funding across the board for the first time.

Steve Goldstein, the Europe Euro Chief of MarketWatch, wrote,

It's funny that we had a decade of no stock buybacks or evil, and now that companies

are actually ramping up CapEx, it's no, not like that.

Quantian summed it up, investors have officially remembered that doing CapEx means you can't

spend money on buybacks and decided they don't like it anymore.

Architect pointed out that Stanley Druckenmiller has argued in the past that high corporate

CapEx, such as spending on factories, inventory, and equipment, acts on a drag on financial

assets because it drains liquidity from the financial system.

Now, this is a super important point.

We tend to think of these investors ascending signals about what they find to be a reasonable

amount to spend on AI, but what they really might be saying is not that they necessarily

think it's wrong to spend that on AI, they just don't like that it's not there to spend

on them.

I think we're going to see a lot more of this debate play out, but keep that in mind as

you try to interpret market's reactions to the hyperscalers over time.

Speaking of Amazon, the company is considering a deep partnership with OpenAI, including

using their models to power Alexa.

As previously reported, Amazon is in talks to take part in OpenAI's latest funding

round.

They are in fact rumored to be considering an investment as large as 50 billion, which

would be about half the money that OpenAI is seeking to raise.

The information now reports that Amazon isn't just interested in an equity stake or a

compute partnership, but is looking to get privileged access to OpenAI's tech.

Users said that OpenAI's models could bolster Amazon's AI products, including the Alexa

Voice Assistant under a proposed deal.

The process would require post-training OpenAI models to tune them for Amazon's use cases,

and would also require OpenAI to supply dedicated researchers and engineers to the process.

That could be the hitch as that would obviously divert resources to some extent away from

their own ambitions.

At this point, I'm not sure how much to make of it, with a spokesperson for OpenAI saying

we are focused on our strong existing compute partnership with Amazon.

On other little nugget from the earnings report, Gemini has, according to Google, hit

750 million monthly active users.

In December, Google said that Gemini's user base had searched from 450 million to 650

million in the final quarter, making this another substantial jump for January.

The latest figure we have for OpenAI came from sensor tower, with their data showing that

Chatchy B.T. had 110 monthly million active users as of November.

Now there is a little bit of a question mark around how some of these companies are

measuring user numbers.

Meta, for example, claims 500 million monthly users for Meta AI, but that's presumably

including quite a few people who stumble across the Assistant in Instagram or WhatsApp.

Google, however, was clear that these numbers are only counted using the Gemini app.

CEO Sundar Pichai said in a statement, the launch of Gemini 3 was a major milestone and

we have great momentum.

One quick nugget of fundraising news, 11 labs has secured a half billion dollars in new

funding at an $11 billion valuation.

The round triples 11 labs' previous valuation from their last funding round which closed

in January of last year.

In terms of what comes next, it sounds like they're interested in moving into video.

Co-founder Matti Stanisewski said, the intersection of models and products is critical and our

team has proven time and again how to translate research into real world experiences.

We plan to expand our creative offering, helping creators combine our best in class audio

with video and agents, enabling businesses to build agents that can type and take action.

Finally, speaking of taking action.

A story that deserves way more time than it gets in this headlines, but which I'm sure

we will come back to, in addition to the news, which will be the subject of our main

episode today, OpenAI also announced a new platform called Frontier.

The goal is basically to help businesses deploy AI co-workers.

OpenAI writes that it is a new platform that helps businesses build, deploy, and manage

AI agents that can do real work.

Frontier gives agents the same skills people need to succeed at work, shared context, onboarding,

hands-on learning with feedback and clear permissions and boundaries.

That's how teams move beyond isolated use cases to AI co-workers that work across the

business.

Basically, Frontier is a combined orchestration governance and optimization platform for OpenAI's

agents.

It allows users to manage the skills that each agent has access to, share context between

agents and set permissions and boundaries.

OpenAI noted that AI leaders across every industry are rapidly rolling out agent deployments,

adding that what's slowing them down isn't model intelligence, but how agents are built

and run in their organizations.

They noted that the capability gap between leading performance and live deployments is

actually growing due to increased complexity around agent governance.

Frontier is designed to give a unified platform to control all the things around the AI model

that goes into a successful agent deployment, context, data access, skills, management.

Now, this is something that we are going to necessarily talk a lot more about, but I

just wanted to flag a little bit of commentary around this chart that was flying around

Twitter and in particular financial circles.

For those of you who are listening, not watching, it's a chart that shows at the bottom,

your enterprise system of record, and then five layers above it.

This context, agent execution and evaluation and optimization right above it, then agents

above that and interfaces above that.

Investor Gokal Rajaram writes, check out where systems of records sit in this diagram

from OpenAI Frontier, at least three, if not four layers of context and intelligence

sit between them and the end business application.

It's one of the clearest representations of how AI companies plan to build next-gen systems

of action on top of existing systems of record and why the markets are so worried about

the future of software companies.

Buko Capital put it even more simply, quite a visual from OpenAI.

Your system of record is a dumb pipe and we will layer five rows of value on top of it

to steal the relationship and all the economics along with it.

No wonder sasses in the gutter.

Like I said, there is so much more to explore about Frontier, but we have not one but two

model releases to talk about, so we are going to close the headlines there and move on

into our main episode.

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Oh my goodness!

Look, it is a good day around here when we get news about upcoming models.

It's a great day when we actually get a new model that we get to play with.

And it is just something else entirely when within 20 minutes of each other, two of the

leading frontier labs drop dueling frontier models.

In this case, within an incredibly clear focus.

The models are of course Claude Opus 4.6 from Anthropic and GBT 5.3 Codex from OpenAI.

And for sure, the first thing that people noticed is the sequence.

Now, none of the frontier labs have ever been above trying to get out in front of one

of their peers when it comes to a big announcement, but we've never seen anything like this.

Versailles Alley writes Anthropic versus OpenAI is like Kendrick versus Drake, but for nerds.

This indeed was a common refrain.

A U-Shrout, Anthropic drops a model in OpenAI responds 15 minutes later.

This is basically a rap beef now.

Peter Yang writes, How did Opus 4.6 and GBT 5.3 drop back to back in like 20 minutes?

Is this a new Cold War or something where there are spies in each company?

The answer Peter is almost certainly yes.

But obviously way more important than the popcorn ball watching of the competition is

what the models actually have for us.

On the opposite side, Anthropic says there are key coding improvements, including better

code review and debugging skills to quote catch its own mistakes.

Interestingly, it does also feel like Anthropic, because Claude is so associated with coding,

use the chance to build on the themes that they've been exploring with Claude co-work

to talk about how Opus 4.6 was better for everyday tasks as well.

Still with the focus of course on white collar work, but things like running financial

analyses, doing research, and using and creating documents, spreadsheets, and presentations.

In fact, in their presentation of the benchmarks, they actually put knowledge work right up front.

On the benchmark side, they claim the leading score on the agent to coding benchmark terminal

bench 2.0, and also claim the top spot on the leaderboard for humanity's last exam.

Now humanity's last exam began as a general knowledge test, but is increasingly a measure

of reasoning and tool use capability.

In terms of new features, Opus 4.6 now supports million token context windows, and for folks

who feel like they constantly run against those limits that has got to be welcome news.

And they've also introduced something that I'm really excited about called agent teams.

Now anthropic seemingly got the memo that swarms was sort of an intimidating name, because

they are explicitly trying to move away from the naming of agent swarms and calling it

teams instead.

The feature basically allows users to set a whole team of cloths to work on a particular

problem, including a coordination layer to ensure they're working on separate tasks that

I'll contribute to the whole.

They write that agent teams are most effective for tasks where parallel exploration adds

real value.

Some of the examples they give are cross-layer coordination, where you've got coding

changes that span frontend, backend, and tests, but they also point to research and review.

Where for example, multiple teammates can investigate different aspects of a problem

simultaneously than share and challenge each other's findings.

The difference between sub agents and agent teams basically comes down to the extent to

which you need your agents to communicate with one another.

Anthropic sums up, use sub agents when you need quick focused workers that report back,

use agent teams when teammates need to share findings, challenge each other, and coordinate

on their own.

Anthropic is also added a feature called adaptive thinking.

It allows the model to pick up on context clues to determine how much reasoning effort to

expand on a particular task.

Users also have manual controls available to dial up or down the amount of reasoning

effort being deployed.

Net to demonstrate the power of their new agent swarm slash agent teams, they tested a fully

autonomous coding task.

Anthropic writes, we tasked Opus 4.6 using agent teams to build a C compiler and then

mostly walked away.

The task made use of the Ralph loop for continuous work, which we talked about in a show recently,

but basically means a continuous loop where it's always checking in so it doesn't get stuck

and continue to take on the same challenge even if it doesn't solve it in one session.

Overall the process consumed around 2 billion tokens, generating over 140 million output

tokens, and costing around $20,000 using standard API pricing.

The test was performed without internet access and only used the standard Rust library.

Anthropic also noted that this version of Clawd was also created by Clawd with the AI

model now the key driver of all coding within Anthropic.

Now there are a bunch of additional features that they talked about.

Opus 4.6 shows significant improvement on long context retrieval and long context reasoning,

which should lead to a much better experience in long horizon tasks as well as coding

work that requires the model to access large code bases.

Overall they said, with Opus 4.6 we found that the model brings more focus to the most

challenging parts of a task without being told to.

Moves quickly through the more straightforward parts, handles ambiguous problems with better

judgment and stays productive over longer sessions.

So that was 4.6 and then literally 15 minutes later, OpenAI dropped GPT Codex 5.3.

Now the first thing you might notice is that we don't have not Codex 5.3 yet.

And I think it's quite clear that the choice to release the coding tune version of GPT

5.3 as a standalone before the release of the regular GPT 5.3 tells you everything

you need to know about how these labs see the importance of various use cases.

OpenAI says that this model advances both the coding performance of GPT Codex 5.2 and

the reasoning and knowledge abilities of regular GPT 5.2 in a single package.

Similar to Anthropic, OpenAI's models are now the core of the development team.

With OpenAI writing, GPT 5.3 Codex is our first model that was instrumental in creating

itself.

The Codex team used early versions to debug its own training, manage its own deployment

and diagnose test results and evaluations.

Our team was blown away by how much Codex was able to accelerate its own development.

Max Storyber from the chat GPT team shared that a recently announced feature where chat

GPT had full support for MCP apps was built entirely with GPT 5.3 Codex.

Max writes zero lines of code written by hand.

Most times, the Codex CLI worked autonomously for hours and implemented parts of this

first try.

Now on the benchmark, self-reported, of course, Codex 5.3 is a significant jump on Sweet

Bench Pro compared to the previous version.

And maybe even more notably than performance, the performance that was achieved was achieved

with far fewer tokens, demonstrating OpenAI's work on token efficiency.

The model claims a new absolutely state-of-the-art score of 77.3% on Terminal Bench 2.0, which beats

Codex 5.2 at 64%, and is, if true, much higher than Opus 4.6 at 65.4%.

For their autonomous coding demo, OpenAI showed off a racing game.

Codex 5.3 used the web game scale and was fed generic prompts throughout the process

like Fix the Bug and Improve the Game.

The result demonstrated that Codex 5.3 can turn through a task using millions of tokens

without human intervention.

Like Anthropic OpenAI also highlighted various non-coding work that Codex 5.3 excels at.

While for developers, the model is trained to be able to debug, deploy, write PRDs, and

tests, and a lot more, basically supporting the entire development lifecycle.

For non-programmers, they showed off tasks, like a set of financial advice slides, a retail

training document, and a fashion presentation deck.

Now interestingly, on GDPVAL, Codex 5.3 was very similar to previous models, but on

the OS World Benchmark, which measures compute use in real-world tasks, Codex 5.3 scored 64.7%,

which almost doubles the performance of GPT 5.2.

Summing up OpenAI wrote, with GPT 5.3 Codex is moving beyond writing code to using it

as a tool to operate a computer and complete work end-to-end.

By pushing the frontier of what a coding agent can do, we're also unlocking a broader class

of knowledge work, from building and deploying software to researching, analyzing, and executing

complex tasks.

What started as a focus on being the best coding agent has become the foundation for a

more general collaborator on the computer, expanding both who can build and what's possible

with Codex.

Basically summing up, you got two labs which are both reaffirming everything we talked

about on the CodeAGI's FunctionalAGI episode, that by expanding the capability set around

coding, it unlocks use cases that are far beyond coding and are core to economically valuable

knowledge work.

So what were the first impressions?

The companies that had early access to these models?

Big surprise, like them!

AGI Orback from Triple Whale said, we've had early access to Clawed Opus 4.6 and have

been testing it over the past week.

It's the best model in the world for front-end report design, ad anatomy, and copywriting,

and orchestrating other tools to actually build creatives.

What's kind of crazy is how good these models are getting at long-running agentic work.

Boxes there in Levy said that overall Opus 4.6 represented a 10% jump over Opus 4.5 on

their hardest knowledge work tasks.

In terms of the features that people were most excited about, on the Opus side, many,

many focused on this 1 million token context window.

Now as Manlo Ventures, D.D. Doss pointed out, part of why people were excited about this

is that it came with as he put it insane-stated-the-art performance on all the long-context benchmarks.

In other words, this isn't one of those claims of a long-token context window, where

functionally it actually doesn't work.

Others honed in quickly on the Swarm-slash team mode.

McKay-rigly shared a test between Opus 4.6 with the team's mode and 4.6 without it and

found that the team's mode was 2.5 times faster and done better.

He also pointed out, reminder that Swarms is available in the Clawed Agent SDK as well,

you can build Swarms into any product literally right now.

Kieran Klassen from every, things the implications are even bigger.

He wrote, been running agent Swarms for a few weeks now, I think this is the future,

but I'm relearning what future development even means.

On the 5.3 codex side, the things that people were taking note of, or things like the

new token efficiency.

The Henny writes, the biggest 5.3 codex news is that it's roughly three times more token

efficient.

5.3 high is smarter than 5.2 high, but uses one-third the tokens, making it faster and letting

your weekly limit last about three times longer.

Good job.

But of course, the real question is, which of these models is better?

Perennial early tester Simon Willison kind of shrugged his shoulders.

In his blog post about them, he said, I don't have much interesting to say about these

models yet to be honest.

They're both incremental improvements on their predecessors and very capable.

Maybe my favorite tweet, which maybe you'll get right away, but did take me a second, came

from the prime agent who wrote, I cannot believe how much better 5.3 is than 4.6.

After some internal testing, results show it's 15.2% better.

Now rather than explain, I'll let you try to work that one out for yourselves.

Some people did try to actually make comparison though, and many did it with their own benchmarks.

Neil Chudley writes, TLDR opus 4.6, 1 million context, enterprise and knowledge work, agent

teams and clawed code, not benching as high as Codex 5.3.

Although he does point out that he doesn't really care about self-reported benchmarks.

On 5.3 Codex, he writes, wins Code benchmarks faster, mid-task steering, but less than

half the context window of opus.

His conclusion?

Gonna have to try Codex, I guess.

With things still pretty fresh on a performance side, latent space focused on the blood sport

that model releases have become.

They write, if you think the simultaneous release of Clawed Opus 4.6 and GPT-5.3 Codex

is sheer coincidence, you're not sufficiently appreciating the intensity of the competition

between the leading two coding model labs in the world right now.

Now, ultimately, they gave this round to Anthropic from a developer attention perspective,

getting out ahead of OpenAI and offering a huge range of new features to try out, and a

$50 credit for tinkering away this weekend.

At the same time, they noted that OpenAI won across most benchmarks, and delivered a model

that was 25% faster than their previous generation, and has higher token efficiency.

Their head-to-head comparison, nascent though it is, showed OpenAI was stronger in coding

and speed while Opus had the edge on agent orchestration and long context tasks.

However, and I think this is extremely important if you're trying to have a definitive answer

right now, they noted that the pair of models are so close that it's likely that, quote,

all first-day third-party reactions are either biased or superficial.

This idea that of Opus 4 orchestration Codex for coding is something that I've been seeing

even for the last couple of weeks, though, and it'll be interesting to see if with the

release of these new models, that framework or narrative reinforces itself among developers.

When vibe code apps Riley Brown asked what the vibe check is so far and how people were

feeling about 5, 3, and 4, 6, a lot of the comments were basically about how OpenAI's Codex

models had gotten really good and that people who hadn't tried them because they were so

hooked on Cloud Code really owed it to themselves to give them a try.

Still, when developer Ryan Carson asked which model are you going to code with this week,

but more than 700 voters, 53.3% set Opus 4 6 compared to 24.9% for Codex 5 3, suggesting

that Anthropics still has the developer devotion.

The team had every had early access to both models, with the end-shippers big thesis being

that the models are converging.

He writes, Opus 4 6 has all the things we love about 4 or 5, but with the thorough precise

style that made Codex the go-to for hard-coding tasks.

And Codex 5 3 is still a powerful workhorse, but it finally picked up some of Opus' warmth,

speed, and willingness to just do things without asking permission.

From this, we can only conclude that both labs are moving steadily towards a sort of

error-coding model, one that's wicked smart, highly technical, and fast-creative, and pleasant

to work with.

Why the Convergence?

Because Dan writes, a great coding agent turns out to be the basis for a great general

purpose work agent.

The behaviors that make AI useful for software development, parallel execution, tool use,

planning before asking, knowing when to dig deep versus when to ship, are the same

behaviors that make AI useful for any knowledge work, and that is the holy grail of AI.

Now, one of the things that I will be watching closely is not the day one or even the week

one reactions.

Remember, after we got Opus 4 5 and GPT 5.2 Codex, it took more than a month and everyone

going home for the holidays for people to really, fully appreciate just how much the

capability set has shifted.

Some are reporting an early continuation of that having to relearn how to do things,

and Thropic's Alex Albert writes, the jump in autonomy is real.

The biggest shift for me personally has been learning to let it run, give it the context,

step away, and come back to something pretty amazing.

The way we work alongside models is starting to completely change.

Open AI is in fact throwing down a gauntlet, trying to encapsulate this shift.

Open AI President Greg Brockman writes, software development is undergoing a renaissance in

front of our eyes.

If you haven't used the tools recently, you're likely underestimating what you're missing.

As December, there's been a step function improvement in what tools like Codex can do.

Some great engineers at Open AI yesterday told me that their job has fundamentally changed

since December.

Prior to then, they could use Codex for unit tests.

Now it writes essentially all the code and does a great deal of their operations in debugging.

Not everyone yet has made that leap, but it's usually because of factors besides the

capability of the model.

Every company, Greg continues, faces the same opportunity now, and navigating it well requires

careful thought.

And so the rest of the post then, he says, is about how Open AI is retooling their teams

towards Agendix software development.

As a first ep, he writes, by March 31st, we're aiming that one, for any technical task,

the tool of first resort for humans is interacting with an agent rather than using an editor or

terminal.

Two, the default way humans utilize agents is explicitly evaluated as safe, but also productive

enough that most workflows do not need additional permissions.

Greg Brockman then is saying that in less than two months, agent first is the way that

technical teams will build at Open AI.

If that doesn't put a starting gun on how we all should be thinking about changing our

own workflows, I don't know what will.

Ultimately, it is a very exciting moment, one which, as we'll see on our next episode,

even has people rethinking the whole idea of an AI bubble.

For now though that is going to do it for today's AI Daily Brief, appreciate you listening

or watching as always and until next time, peace!