OpenAI Just Dropped GPT 5.4 With Insane 1M Token Window

0:00OpenAI just released GPT 5.4 with a 1 million context token window. They are now doing monthly updates to catch up with Anthropic and Google, which just makes sense. Feels like they're falling behind a little bit. But here's the thing that no one's talking about. This might be the most desperate move we've ever seen from Open AI yet. So, let me break this down for you. Because of what just happened changes everything how we think about the AI race right now. Looking at chat GPT 5.4 that just dropped. There are some pretty insane specs that are going to put a lot of pressure on Anthropic. We've got this 1 million token context window. So, it's memory is much better, which is two and

0:45a half times bigger than what we had with 5.3. They've introduced this new extreme thinking mode specifically designed for scientific reasoning. We're seeing improved multi-hour task reliability, fewer errors on complex workflows, and better memory across multiple step tasks. But here's what's really interesting. Open AAI just announced that they're moving to monthly model updates. Monthly. Think about that for just a second. We went from waiting like a year to 6 months to 3 months for new model releases. Now we're getting updates every single month. These models are getting better and better, especially in the open-source model race like Quen, Kimmy K2, Miniax. So, you

1:31know what this is telling me is OpenAI is feeling the pressure. I don't believe they're even profitable yet. So, they're scared. So, let me give you some context on why this matters right now. Over the past 6 months, we watched Anthropic absolutely dominate with Opus 4.6, Sonnet 4.6, one of the best reasoning and context models and for coding. I've been using Opus 4.6 in basically all of my daily workflows. So, Google's been making some serious pushes with anti-gravity. It's been getting better and better as far as from like a design perspective. Really good. Open AI, which used to be the king for largely 2025, uh, has been playing catch-up ever since. Honestly, it's been pretty painful to watch uh, OpenAI, you know, struggle as I was a chat GPT user for at

2:18least a year and a half before switching over everything to Claude. I've been running my entire content pipeline through Claude and my mission control dashboard is all done by Opus 4.6 and I'll tell you that the consistency has been incredible. Memorizing what we're working on, the memory is better. It's faster. It just understands what I'm saying. So, my agents are Atlas, the Rizza, all of them. They run every morning at 6:00 a.m. on the open claw. And I've been using Cloud for script generation. Frankly, it's just been more reliable for complex multi-step tasks. It's been helping me with content creation, helping me with outlines, helping me with organizing more of my thoughts and ideas for the AI space. So, when I see OpenAI making these moves,

3:03I'm paying attention because I might actually switch back. Doesn't mean I'll stay there. Especially when I'm using OpenClaw, I know that it's going to consume a lot yet less usage, token usage versus using something like Claude, whether it's through the OOTH or through the API. So, let's dive dive in a little bit more about 5.4 and what it actually brings to the table because some of this is a bit mind-blowing. First up, this 1 million token context window. Now, for those of you who aren't familiar with tokens, think of them as chunks of text that AI can remember and work with at one time. So, the more that you go back and forth with AI and the large language models, that context window fills up. Therefore, the memory

3:48gets worse and worse as you progress through the conversation. So, the more tokens, the more information the model can keep in its working memory during the conversation or tasks. So 5.3 had 400,000 tokens, which was pretty good. But 1 million tokens, that's pretty crazy. We're talking about roughly 750,000 words of context. To put that into perspective, that's like being able to remember and work with about three fulllength novels simultaneously. For someone like who me who's building for someone like me who's building complex AI workflows, this is huge. Here's why it matters for builders and shippers. When I'm working on my mission control dashboard, I often need the AI to understand my entire codebase to

4:34remember specific conversations about features we're shipping, things we're implementing yesterday, a week ago, and maintain that context across multiple files and functions. With the larger context window, you can literally feed the entire project structure and everything and have it maintain that understanding throughout your session. But here's what's really interesting. This is where I think OpenAI is making a strategic play. They're not just increasing the context window, they're doing it while maintaining response speed and quality. That's really hard to do. Usually, when they increase the context window, you sacrifice speed and accuracy. The fact that they're claiming to maintain both suggests that they've made some serious infrastructure improvements. Now, let's talk Now, let's talk about this extreme thinking mode because this is where things get really

5:20interesting for those of us building AI applications. Open AI is positioning this as a specifically designed for scientific reasoning. But I think the applications go way beyond just the science. What extreme thinking mode appears to do is give the model more time to actually think through complex problems before responding. Instead of just generating the first plausible answer, it's taking a step back and really working through the logic for complex coding problems, mathematical reasoning, or multi-step business analysis. This could really change the game. I'm thinking about how this can impact my workflow. Right now, I'm having multiple agents analyze trends, generate scripts. Sometimes the

6:05reasoning can be a bit shallow and just straight up stupid. I'll identify a trend, but nothing will really dig into why it matters or how it connects to broader patterns. It just seems to be very shallow and boring. So, with extreme thinking mode, I might be able to get much deeper, more thoughtful analysis, which is what I'm going to be testing all throughout the day today in my open cloth setup. But here's the thing that has me more curious. How much slower is it? Because Opus 4.6 six is generally pretty fast in its responses because if extreme thinking mode takes longer to respond, is that really even worth it? Does that create a tradeoff? Do I want fast responses or do I really want it to be more thoughtful and deep

6:50and analytical? For time applications, speed matters most. For complex problem solving, depth matters more. So, I may have it split up doing different tasks for different agents. The improved multi-hour task reliability is something that honestly I've been waiting for. One of the biggest pain points I've had with GPT models is that they tend to lose track of complex tasks over extended periods. So, you know how it is. You start coding session, you're going back and forth, and an hour or two later it's suddenly like it forgot what you were working on in the first place. So building easy flip, magic hand, and other projects, this has been a common problem, but has gotten better with the release of Opus 4.6 and Sonnet 4.6. And

7:35that's why I never have really used any codeex models, even though it has gotten much better. So the real problem is, you know, people that are just getting into this industry have a hard time figuring out what model to even use. That's why we launched Shipping School, a community for non-technical founders that want to ship fast, want to hit the ground running and get their app actually built rather than procrastinating. So, we have multiple resources inside there with a full course on how to set up your project correctly. So, making real progress is the key in this industry and also adapting to all these new releases. So, going back to like using GPT 5.4 before. And if you're just getting started, you probably want to start there because you're going to be able to

8:21maintain much larger of a context window. It's going to be able to remember the projects that you're actually working on. Claude Code and Claude Opus 4.6 can be a little bit more intimidating, but if you're just getting started, you might want to jump in and start 5.4 fresh off the bat. So, I'm curious how OpenAI is actually measuring the comp the the context window and deep thought process. Multi-hour task reliability is one of those things that's really hard to benchmark. What counts as maintaining reliability? Is it just remembering the conversation or is it actually maintaining the quality of the responses over time? These are the details that matter when you're actually using these tools for real work. I'm curious to see what the benchmarks will

9:06show, even though they don't really meet much until you're actually using the LLM in actual building and specific coding tasks. The fewer errors on complex workflows piece is something I'm interested in. Complex workflows are exactly what I'm building with mission control and upkeeping, easy flip, and magic hand and the codebase. So right now I currently have multiple agents that all work together, pass information between each other and execute multi-steps reliably. Right now my workflow looks like this. So I have multiple agents. They're all scheduled on cron jobs. I get a morning briefing for my specific workflow. My workflow is probably going to be a lot different than yours. So Atlas is the brain running by 4.6 Opus. I don't use Opus

9:51for any other tasks um because it does use up a lot of usage. So Atlas sends out a a response to all of the agents. So the Rizza is to analyze the trends. It pulls information from X and Reddit. It generates the scripts and then it passes that information to inspect the deck to create social media posts and eventually it will work with the Jiza to create newsletters and blogs. So each step depends on the previous step. They all work together, not one without the other. So if one agent makes an error, it can cascade through the entire workflow. So this is actually one of the areas where Cloud has been really strong, which is why I prefer working with Opus 4.6. The consistency and accuracy on complex multi-step tasks has been pretty impressive compared to

10:37switching to other open-source models like Kimmy K2 or Miniaax, things like that. If OpenAI can match or exceed that with 5.4, I'm going to have to consider switching over, which I'll be testing with today. and how I kind of architect the whole brain of my open cloth setup. So, here's what I wanted to see. Real world testing and you probably haven't seen much out since it just got released. So, benchmarks are great, but how does it actually perform when you're building real applications? That's to remain to be seen. How does it handle edge cases? How gracefully does it degrade when it encounters something uninspected? Now, let's talk about the elephant in the room. This shift to monthly updates. We used to get models released every six months, every four months, every three months, and it seems like it's getting closer and closer to a

11:24release every week. This is probably one of the most significant changes Open AAI has announced. And I think that tells us everything we need to know about where this space is going and where OpenAI sees themselves in the competitive landscape. Monthly updates means they're moving to a much more aggressive development and release cycle. Instead of the traditional approach of big infrequent releases, they're adopting more of a continuous improvement model and that honestly feels like a direct response to Anthropic and Google, which they are catching up. I think Anthropic has been in the lead for some time now. Anthropic has also been incredibly aggressive with their release schedule. We've seen multiple versions of cloud this year already, each with meaningful improvements. Google has been doing the same and playing catchup with Gemini.

12:09OpenAI, which used to be able to release a model and then sit in their lead for months, they cannot do that anymore. No way. But here's what worries me about monthly updates is the quality control. Because shipping fast doesn't mean always good quality. So when you're pushing updates that frequently, there's always a risk that you're prioritizing speed over stability. Some of the most successful software companies have learned this lesson the hard way. Move fast and break things works great until you break something important. I do believe in shipping fast and then iterating and always understanding what the LLM is doing, especially if you're in the code base, learning how your repo is set up, learning how you set up things and why they're set up that way. Because just because you shipped fast,

12:54doesn't mean your product works. And if you want to get customers, that's going to be a problem. So for those of us building every day, shipping every day on top of these models, the stability, I think, is the more important part. If I'm running my entire content pipeline on 5.4 and they push an update that changes the behavior in subtle ways, that could break my entire workflow. Like GPT 5.4 can be good today, but once more and more people use it, does that nerf and degrade the LLM? So, I need predictability and reliability, not just the latest features. This is actually one of the reasons I've been hesitant to go all in on any single uh OpenAI provider right now because cloud has been the most stable for the last 6 months since I've been using it. Building out projects. I've got plenty

13:40of fallback systems in place as well. Um you know things that check the codebase, things that check the work. It's essential when you're building on rapidly evolving platforms. You want to have consistency. You want to have stability. It's not always about speed. So, let's look at the broader competitive picture because this is where things get really interesting. Despite all the improvements of GPT 5.4, they sound amazing, but how does it perform? The benchmarks still show anthropic and Google are ahead in key areas. Cloud continues to lead on reasoning tasks, coding ability, and what I'd call conversational quality, which is really nice. It's like you're speaking to a more personable LLM versus the other models which are more robotic. That is hard to measure. Um, you know,

14:27when you see these benchmarks come out and you really don't know what they mean. I think using them in a firsthand experience on what you're building, you're really going to get much more perspective. So, Google's Gemini has been making impressive gains as well, especially with from the design perspective and also on multimodal tasks and integration with Google's ecosystem. So, where does that leave OpenAI? I think that they're in a really interesting position right now. They still have some of the strongest brand recognition and the largest user base. Most non-technical people when they think AI, they think chat GPT. That's valuable, but in terms of pure capability, they're not the clear leader anymore because once you start getting into this space and actually building things, you're going to learn that it's just a very I don't know, compared to

15:13Opus 4.6, six. It seems like it's a very ordinary model. I have used codeex 5.3, it's performed well, but as far as from like a personality point of view and having back and forth chats about managing my business and such, it's just more personable and I just prefer it. So, back in the day, it reminds me of a lot of like the browser wars. Microsoft had Internet Explorer, you remember, and massive market share, but Firefox and Chrome were later released and were technically superior. Eventually, technical superiority won out, and it just took years. The question is whether OpenAI can actually leverage their user base and brand recognition to buy themselves some time to catch up technically. Monthly updates suggest that they're trying to do that. Exactly.

15:59From a builder's perspective, this competitive environment is actually great for us. Competition drives innovation and we're seeing rapid improvements across all the major providers which also creates some challenges. Which platform do you build on? How are you managing the risk of betting on the wrong horse? Put in the comments down below what have you been rocking with. I like I said have been a claude user for most of the this year and half of last year and I just have a hard time turning to any other model when I'm starting to build any. So my approach is to been to build on platform agnostic systems as much as possible. My mission control dashboard can swap between different AI providers depending on the task and what's performing best at any given time. Like I have it a lot

16:45of the cron jobs running through Gemini 2.5 flash through open router because it uses so many less tokens even compared to sonnet and opus. So it's more complex to build but it gives me more flexibility as the landscape evolves. Let me talk about why this means what this means specifically for the types of applications we're building in shipping school. We've got people building everything from customer service chat bots to content creation tools to complex data analysis applications. The improvements in GPT 5.4 could be significant for all of these use cases, but we really need to see how it actually performs on a firstirhand basis. Don't just believe the benchmarks. So for customer service applications that can be improved with the multi-hour task reliability, it

17:32could be huge. Instead of conversations that feel disjointed as they go on, you could have the AI that maintains that larger context window and relationship of understanding throughout the extended support interactions. for content creation tools. That 1 million token context window opens up possibilities for analyzing and working which much with much larger data sets, multiple reference documents and detailed brand guidelines have it maintain understanding of all that context while generating content. For data analysis applications, the improved complex workflow reliability could enable much more sophisticated multi-step analysis processes. Instead of simple queries, you can have AI that works through complex analytical frameworks over

18:18extended periods of time. But here's the thing that I keep coming back to. How much is this going to cost exactly? Open AAI hasn't announced any pricing for 5.4 yet, but given the improvements, I expect it to be a bit more expensive than previous models. So for builders, especially for those who are just starting out, cost matters a lot. And for $200 a month, you can get that Max Claude Code plan and really take that a long way and even downgrade your plan. Most people are running the 20 to $100 plan and that takes you a long way unless you're shipping multiple apps. This is actually one of the reasons why I moved my entire setup to run locally on OpenClaw. So instead of paying for hundreds of months or hundreds of dollars a month in various API calls, I'm running everything on my Mac Mini.

19:04It's not as powerful as the cloud models, but for many tasks, it's good enough. And the cost savings are significant. Like I said, I do everything through the OOTH subscription through Claude. And you just need to be careful and be aware setting up um some checks within your dashboard, so it's not exceeding the token limit. I think we're going to see the beautifification in the market. High-end applications that need the absolute best performance will use cloud models like 5.4, Cloud, or Gemini. But there's going to be a huge there's going to be a huge market for good enough local models that can handle most tasks at a fraction of the cost. Speaking of cost, let's talk about this competitive environment and what it means for pricing. I think OpenAI was clear leader for a while. They could

19:50charge premium prices, but now with the real competition, we're starting to see pricing pressure. Anthropic has been aggressive with cloud pricing and Google has the resources to subsidize Gemini if they want market share. This is great for builders because it means we're getting better capabilities at lower prices. But it also means the AI providers are going to be looking for other ways to monetize, which is probably why you're seeing ads in your CLI and in your chat GPT chat. Pretty annoying, but you're going to see more of it. So, we could see things like that coming to multiple platforms, exclusive features, or bundled services. Now, let's talk about the technical implications of these improvements. A 1 million token context window isn't just a bigger number. It's fundamentally changes how you build, how you ship, and

20:36how you architect AI applications. With a larger context window, you can move away from techniques like rag databases, which is retrieval augmented generation. For some use cases, this works great. Instead of chunking up documents and retrieving relevant pieces, you might be able to just feed the entire document into the context window. This simplifies the architecture but also changes the performance characteristics. The extreme thinking mode that open AI is moving toward more sophisticated reasoning architectures. Instead of just predicting the next token, the model is doing something more like actual problem solving. This could be a step toward more general intelligence, but it also raises questions about how transparency and explanability. So for those of us building AI applications, these

21:21architectural changes matter because they do affect how we design our systems, how we build, how we set up the codebase. Do we build for the current limitations or do we anticipate these improvements in design for where the technology is heading? I've been thinking about this a lot with mission control and my dashboard and with open call. Right now I'm designing around the concurrent limitations, chunking tasks, managing context carefully, building and error handling for all for when all models fail. If these improvements deliver on their promises, some of these designs decisions might be unnecessary overhead. Let me share with you what I'm planning to test with chat GPT 5.4 today. Once I get access, I want to show how it handles my entire content creation workflow. Figure out if it

22:06actually remembers what I'm telling it and see if it can remain the and retain the context. So, can I feed it like trend data about my brand and my brand guidelines, previous scripts, audience feedback, and get an actual coherent answer? We'll see. Second, I want to test the extreme thinking mode. This sounds great, but does it actually work? And I want to test it on some of the more complex strategic questions I face in building my businesses. So instead of just getting quick answers, can it work through the implications and can it consider multiple scenarios? Can it provide deep insights and not actually be shallow? Third, I want to see how it handles the kind of multi-step technical tasks we work on shipping school. So can it maintain context and quality while

22:51someone is through building a complete application from start to finish? This remains to be seen. So, while we're this is getting rolled out, I want to hear back feedback from you guys. How is it working in your workflow? Have you gotten access? And what does it differ from what you're currently using right now? Or how does it differ? So, here's what I'm curious about the most. How does it compare to Claude in the real world? Like, I'm using Claude every day. So, benchmarks are great, but they really don't mean anything to me. Because if I'm using something and it has great benchmarks, it may actually perform terrible for what I'm working on. So, does it feel more does the LLM feel more like natural and does it feel more personable when I interact with it? Does it understand more nuance requests better? Does it maintain consistency

23:37across conversations? The competitive dynamics here are fascinating from a business perspective, but OpenAI built the market, but now they're fighting to maintain their position. It's a classic innovators dilemma situation. They created the category, but now they have to compete with companies that can learn from their mistakes and focus purely on building better products. Anthropic in particular has been really smart about this. They focused more on safety and reliability, which are exactly the areas where early chat GBD had problems. Google has leveraged their existing infrastructure and data advantages. Meanwhile, OpenAI has to balance maintaining their current user base while innovating for the future. So for builders like us, this creates both opportunities and challenges. The

24:22opportunity is that we're getting access to an incredibly powerful model that are that are improving rapidly every week it seems. But the challenge is that the landscape is changing so fast that it's hard to know where to place your bets. If you're a new beginner coming into the space, you were probably so overwhelmed on what to use, how to build, how to set it up, which is why we have that school community to get people on track, equip them with the tools, and get building. My strategy has been also to stay flexible while all these changes are occurring while we're building real value for customers and users. So, instead of betting everything on one AI provider, I'm building systems that can adapt, that can be more plugandplay. Instead of just following like the latest trends and the crowds and all the hype, I'm focusing on solving real problems for real people and how I can

25:09automate that process really like buying back my time. So, this is what we really focus on in shipping school. I'll have it linked down below is actually building sustainable businesses, not something that will actually be irrelevant in 3 months. So, it's great that GPT 5.4 for is out and it has a million token contact window. But what really matters is whether you can use that capability to create something people actually want to pay for. Looking ahead, I really think we're going to be entering a really interesting phase in the AI race. The easy gains from just scaling up models are getting harder to achieve. We're starting to see more specialized approaches, more focus on special use cases, and more attention to practical considerations like cost and reliability. I truly predict by the end of the year we're going to be running a

25:54lot more models locally that are open source on our own machines which are going to just get better and better and then there's a point where it's good enough right so this is probably good for the field overall the peer research phase is of let's see how big we can make these models is giving way more to practical engineering focused on building useful products that's exactly what builders need but it also means we need to be smarter about how we think about AI in our applications. Instead of just throwing the latest model at every problem, we need to think about what capabilities we actually need. What are we willing to pay for? How we're going to build systems that can evolve as technology improves. So, what's my take on Open AI and are they catching up? Are they falling behind? I think that

26:39they're fighting for their life right now. And they know it. You've seen it. All the bad press coming out lately. there's a massive drop on people not using GPT for uh chat GPT anymore and uninstalling the app and it's getting a little crazy out there. So, the shift to monthly updates is kind of proof in the pudding right there. The aggressive feature development, the focus on practical improvements. These are the moves that of a company that's no longer dominating the market, but that doesn't mean that they're finished. Open AI has significant advantages in brand recognition and user base in developer ecosystems and deep pockets as well. A lot of investmentbacked companies are all in on open AAI. The question is whether they can execute on this new strategy effectively. So like from a

27:25builder's perspective and someone who's been building with these models for the last eight months, I think the best approach is to stay platform agnostic while taking advantage of rapid improvements across all providers. Use GPT 5.4 4 where it makes sense. Use cloud where it's better. Use local models where the cost matters more than performance. I really think that people can get so caught up with like like the super fan of all these LLMs like I am an open AI user till I die and it's like you need to be adjusting your perspective perspective and have an open mind as these models continue to release. So the AI race is far from over and I really don't believe there's an end in sight. So, I think this is great news for all of us building with AI and, you know, building cool applications.

28:11Competition drives innovation, like I said, and we're the beneficiaries of that innovation. If you're looking to learn how to build apps with AI and actually navigate this rapidly changing landscape, we do this exactly inside shipping school. We've got a whole community of builders who are shipping real products and sharing what's working in practice. We also host weekly boot camps three times a week and we help you step by step get your app actually built. I'll put a link down below. The key is to stay focused on creating real value while being ready to adapt as the technology evolves. Chat GBT 5.4 is great and it's impressive, but what really matters is what you build with it. Otherwise, it's useless. So, that's my take on the OpenAI situation. We'll see you in the next one. Be blessed.