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 ride. Hey, thank you so much for
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