Hey everyone, welcome back to the Elon Musk Podcast.
I'm thrilled to share some exciting news with you over the
next two weeks. We're evolving.
We'll be broadening our focus to cover all the tech Titans
shaping our world. And with that, our show will
become Stage 0. You'll still get the latest
insights on Elon Musk, plus so much more, so stay tuned for our
official relaunch at Stage 0 coming soon.
Now let's get into this episode. How much smarter and more useful
can an AI model really get before it starts coding entire
applications from scratch, fixing its own bugs, and writing
its own documentation? Well, Open AI unveiled GBT 4.1,
which is a new generation of AI models it claims is faster,
cheaper, and significantly more capable than anything it's
released before. But beneath the upgrade numbers
and benchmark scores lies something more consequential.
Open AI believes this model and its smaller variants could
eventually serve as the backbone of autonomous coding agents, The
kind of agents that don't just assist software engineers, they
are the software engineers. Open AI announced the new GPT
4.1 family models on Monday, introducing not just the full
size version but also scaled down editions called 4.1 Mini
and 4.1 Nano. Each one is designed with a
distinct balance of speed, size, cost, and power.
These models are available exclusively through Open AI's
API, meaning developers integrating them into apps and
tools. We'll be the first to see how
they perform in real world environments.
GBT users, for now, are left out of the loop, so no prompting on
openai.com. Now.
What's set? What sets GBT 4.1 apart is its
ability to comprehend massive inputs up to 100 million tokens,
or 750,000 words, far exceeding what GBT 4 O could process.
For comparison, that's longer than War and Peace and several
technical manuals combined. Now.
This makes it ideal for tasks requiring an understanding of
complex and like documents such as legal contracts, software
repositories or even academic papers.
That also makes it more effective in multi turn
conversations where earlier context tends to get lost
internally. Open AI's own testing shows GPD
4.1 outperformed GPD 4 O model by 21% in coding related tasks
against the earlier GPD 4.5 research preview.
GPD 4.1 showed a 27% improvement in the same category.
It isn't just about solving more problems though, it's about
solving them in a cleaner or structured way.
GPD 4.1 was specifically refined to avoid unnecessary code edits,
follow precise formatting instructions, and respect the
intended structure of its outputs, including correct
ordering and tool usage. And developers who tested
earlier models often pointed out that they had to guide the model
closely, correct its structure, or deal with inconsistent
formatting. GT 4.1, according to Open AI,
has been tuned to avoid these common frustrations.
One Open AI representative noted that front end coding tasks, the
kind that require strict adherence to format and visual
consistency, were a top focus of this update.
But the performance jump isn't just in limited decoding. 4.1's
improved ability to follow instructions make it a better
choice for powering AI agents, automated systems that perform
complex tasks based on natural language commands.
Now, whether it's sorting emails, organizing files, or
assembling documentation from various sources, GPT 4.1 can
manage more intricate tasks and than it ever could before, with
fewer missteps. It's capacity to comprehend
longer context also means it can maintain more coherent and
consistent actions over time. Now, in line with Open AI's new
release, the company will phase out GPT 4.5, which was a preview
model, and they're going to do that in July.
And the decision seems driven by both cost and performance.
GPT 4.1 offers either better or equivalent results, but with
considerably lower pricing. The economic argument could be
as compelling to developers as the technical upgrades now.
Cost is a core element of this launch.
The full GPT 4.1 model is priced at $2.00 per million input
tokens and $8 per million output tokens.
That's a substantial price cut compared to earlier models.
The mini version drops to $0.40 per input and 100 or $1.60 for
outputs, and the nano built for speed in minimal cost is $0.10
per million inputs and $0.40 for output tokens.
Now this is the most effective, cost efficient model of open EI
that it's ever released. However, smaller models trade
some accuracy for efficiency. GPT 4.1 Nano, for instance,
prioritizes speed and affordability, which means it
may not be the best option for tasks where precision is
critical. Still, for developers who need
fast responses for similar use cases, Nano might offer exactly
the right balance. Open AI tested the new models an
SWE bench, a popular benchmark for software engineering tasks.
The full GPD 4.1 model scored between 52 and 54.6%, and SWE
bench verified a human validated subset of the benchmark.
That's slightly behind Google's Gemini 4 point or 2.5 Pro, which
hit 63.8% in Anthropic's Clods 3.7, which reached 62.3.
However, Open AI noted that some solutions weren't runnable on
their infrastructure, creating variance in scores.
Now the release comes amid intensified competition from
other AI developers. Google, Anthropic and China
based DeepSeek are all chasing similar goals, building models
that can perform complex coding tasks by themselves and
eventually take over large chunks of software engineering
workflows. Which means software engineers
will be laid off or fired or find new jobs.
Google's Gemini 2.5 Pro and clawed 3.7 sonnet.
It both scored well on public benchmarks and include their own
long context. Now the future of developers,
it's getting a bit more tangible.
Instead of having to stitch together multiple tools or tweak
outputs by hand, they can begin to rely more heavily on models
that understand their intentions, follow instructions
precisely, and produce code that's ready to go to
production. Now, this could all dramatically
change how software is developed and who gets to develop it.
Now, if coding agents do become capable enough to handle large
projects autonomously, the role of human developers could shift
from creators to supervisors and then just an idea generator.
It's not a loss though, for these developers.
If you have ideas, it's a change in focus.
It means more people could build useful software without needing
deep engineering experience. But GPT 4.1 isn't perfect, and
it's not the end of this journey.
But it marks a clear improvement over earlier models in areas
that matter most to developers. Cost, reliability, instruction
following, and code performance. And for now, it's just a smarter
tool. In the near future, it could be
the foundation of all code being developed.
Now 4.1 is faster, cheaper, and more precise, pushing AI coding
tools another step closer to building software all by
themselves. Someday you'll have an idea.
You'll be able to write it into a prompt, write via software
that does XYZ ChatGPT will create the whole software from
start to finish, Back end, front end, database, everything in
between. That day will come soon and
hopefully I'll be around for it because I want to see that
happen. My job for the last 20 years has
been front end web developer and I'm excited about the future of
GBT 4.1. It's going to be a wild, wild
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