Transcription
So, OpenAI just dropped not one, but three new amazing AI models, and they're built to power the next generation of AI agents. So, in this video, I'm going to show you why the GPT-4.1 Mini and Nano are game-changers, especially if you're building no-code AI agents. We'll break down the real comparisons and how much cheaper and faster they are compared to any other model that's out there in the market, including the GPT-40 and the Claude Sonnet 3.7. I'll also show you exactly which model to use depending on your task. So, whether you're building voice AI agents or MCP agents, or any agent that requires tool usage, I'll show you examples of which models are best for those purposes.
All right, without any further delay, let's jump right into the video.
All right, so I'm going to start with pointing out a few very, very important things, especially when it comes to the context of building low-code AI agents or just building AI agents in general. So, we're going to take a look at the uh article that OpenAI released. I'll put the link in the description so you can take a look at it. So, just a few very quick notes, and then we're going to go into details of some of the most important things when it comes to building AI agents. First of all, just a quick overview: in this article, that shows you all the details when it comes to why GPT-4 4.1 excels in coding, instruction following, long contexts, and latency. We're going to focus on instruction following, accuracy, and latency because these are the three main important things, in my opinions, when it comes to building AI agents and why this is such an important step forward. And it might be the best model—not might be, I think, in my opinion, this is going to be the best model to use right now.
So, first of all, we're going to focus on instruction following. So, let's take a look at what this instruction following means, and then I'll show you a few examples of real AI agents that we have built and why this is such a game-changer. So, first of all, to give you an overview of what instruction following means, it just means that when you're giving your AI agents the ability to independently decide which tools that it has access to to use based on what the user is asking, that's where this important uh benchmark becomes crucial.
Right, so for example, let me go ahead and show you one of the uh complex AI agents that I've built. So, if I go to my community here, I'm going to go to these voice agents, or this voice agent AI army. So, I'm going to download this. Let me show you exactly what this looks like. So, I'm going to go to my Nan. Obviously, for those of you who are not familiar with Nan, I'm using Nan here, which is a great, powerful tool for building no-code AI agents. So, this is one of the agents that I've built, and feel free to check out the um video on this, the step-by-step build in the community. But just to quickly point out what this agent is doing is we're using a Telegram trigger to use our voice to be able to instruct this commander agent, which has access to multiple sub-agents right here, and it's going to use its brain, which in our case I was using GPT-40 because that was the most powerful model when I built this thing, but for now—but now I'm definitely going to switch this to GPT-4.1 because of how amazing it is and how it exceeds all the other models when it comes to uh instruction following. And when I say exceed, I'm also including the cost efficiency in here as well, because obviously when you're building AI agent cost becomes very important, especially when you're building more complex AI agents. But just quickly here, so what this commander agent is doing is based on what the user gives a task, it has access to all these sub-agents. Right, so these sub-agents, for example, calendar agent, company knowledge base—these in itself, they are complex agents that have access to multiple tools. So, for example, one of the agents—so let me download this uh personal expense agent, and I'm also going to download this calendar agent. So now, if I bring this over to my workflow so that way you can see exactly what these are. So here's a calendar agent. Right, so this agent has access to multiple tools through the tools uh usage here, and the calendar agent, as you can see, is being used inside this main commander AI agent, and we're giving it this instruction right here: we're saying that use this calendar agent to manage all calendar events. Right, so what this is doing is this main commander agent needs to determine when to use this sub-agent, and then inside the sub-agent there's this other agent that has access to these tools that will need to understand which tool to use based on what the query the user is asking. Therefore, as you can imagine, this is a very complex AI agent, and therefore it's crucial to use the proper um model, or a very strong model that's capable of following instructions. Right, so obviously I was using GPT-40, but now, as you can see, now GPT-4.1 is accessible here, and I'm definitely going to use GPT-4.1 mini when if I'm considering the cost, because obviously cost becomes a huge important in the bottom. Let's take a look at it right here. There's a good comparison chart. There you go. So, as you can see, the input and output tokens for GPT-4.1 mini is 40 and uh input, and then output is $1.60. Now, this is incredibly cheap compared to what's out there in the market. Now, Claude uh 3.7 Sonnet is the best model in my opinion so far, at least, but Claude is pretty expensive. So, I'm using this OpenRouter comparison here. As you can see, the input token for Claude 3.7 is $3, the output is 15, and we're comparing this to GPT-4.1 Mini is 40, and the output is $1.60. And again, this is a huge, huge leap forward when it comes to cost because when you're building these complex AI agents, and if they're live and they're being used constantly in your workflows and your automations, then you can imagine that the cost could add up pretty quickly. Therefore, when you combine the ability for GPT-4.1 mini for it to follow instruction, how good it is when following instruction, and when you combine it with the cost efficiency, it becomes—it's a no-brainer to use this moving forward.
Now, another important aspect, another very complex agent that you will be building in the future, and as I'm starting to do a deep dive topic on this, is using MCP servers. So, this is another uh agent that I built. So, if I download—download this and bring it to my um workflow here. So, let me get rid of this import from file. All right. So, MCP—obviously, the Model Context Protocol by Claude by Anthropic—um, is giving our agents the ability to become even more efficient. Now, MCP servers—so this again, this is the server uh trigger that I built that it has—again, same thing—access to multiple tools, including Pinecone vector database that's has all of uh my research related uh data that's sitting there; it has access to multiple other tools, and from our AI agent we're giving this—this MCP client tool. And again, if you're interested in checking out the step-by-step tutorial, I did this already, so you can check it out in my previous video. But this basically gives the AI agent the ability to utilize all of these different tools within the MCP server from this AI agent with a simple instruction. Right, and we're not giving this a long prompt; we're not giving this any prompt. So, the model needs to be smart enough to know exactly which tool to use. So, as you can see right here, if I select the tool—so these are all the different tools that this MCP server has access to—so this uh model, the AI model here, especially for these AI agents that's going to use MCP client tool, because it becomes extremely crucial, therefore it needs to know, or it needs to be really good at following instructions. And obviously, again, cost becomes a big aspect of there as well, and therefore, as—as I—as I said before, GPT-4.1 mini in this case also becomes the best model to use when it comes to your uh MCP AI agents or MCP servers that you're using.
All right, so that was kind of instruction following, which again I said is extremely crucial for building AI agents, and uh OpenAI mentioned that also in their article right here. The next important thing is latency. So, for those of you who don't know what latency is, in simple terms, it just means that whenever you ask an AI model a question or a task for it to do, the time it takes for the model to respond is what latency is. So, therefore, the lower the latency, the better the response, or the quicker the response will be. And as you can see here, the GPT-4.1 family—intelligence by latency—is way higher than GPT-40, and again this is extremely important because of how expensive GPT-40 is. You can see it is actually uh a huge—it says that it reduces the latency by nearly half and reduces the cost by 83%. Now, the combination of these two is extremely important, especially for low-latency usage. So, for example, any task that requires low latency, for example, a conversational agent. Right, so if you're using any kind of voice AI, if you're building any kind of voice agents, then low latency becomes extremely crucial because you don't want the AI model to have long pauses, because what that's going to do is that's going to reduce the natural conversation or the natural interaction between the human and the AI agent. Therefore, you want to use low-latency AI AI models when it comes to building conversational or voice AI agents. So, let me go ahead and bring you—bring in another example here. Let me go—rid of this. And then also, by the way, for uh following instructions or responding quickly, even when you're building general AI agents, this also becomes very important. Right, because if you have a low-latency model, then the AI—the—the AI model will be able to complete the tasks faster. All right, so let me go ahead and go to my voice AI agents here. Uh, I'm just going to bring in—So here's a uh—actually, that's not a good one—an advanced AI. There you go. All right, so here's an advanced voice AI that I built with ElevenLabs. So, if I bring this over here, let me import it from file again. All right, here we go. So, what this is doing is this AI agent is uh listening to the voice commands that are coming in from ElevenLabs. Let me go to ElevenLabs here to show you exactly what this is. If I go to conversational AI agents. All right, here we go. So, this is the agent—the front end of this agent. So, if we click on test agents, so this is where now you are able to interact with this voice agent from the front end, and in the back end everything is being handled, everything is being sent over to this main AI agent. And as you can see, I had to use Anthropic chat model because obviously that was the best model out there, but now I'm definitely going to use the 4.1 Nano or the 4.1 mini in this case because of how latency becomes an important thing here. So, when—when you're interacting with the voice agent in the front end, then it needs to be able to quickly respond, so the model needs to quickly respond, and therefore latency becomes a huge uh uh point here. I'm going to let that shut off quickly. All right, so let me show you exactly what's going on in the front here. So, again, if you want to check this out, please feel free to check out the full tutorial, but just quickly this is a uh Voice AI agent that I built for my—for my—for my agency here. So, what this is doing is this is using multiple tools here to reach out to our agent here using the webhook here, and this agent has access to—same thing—it has access to multiple tools, and the system prompt tells it to use the proper tool whenever uh the—the user asks something uh relevant to, you know, that particular instruction. So, therefore, if you're using a low-latency model—so early in this particular example, I believe I was using the uh Gemini 1.5 flash, but the problem is Gemini 1.5 flash was not that good when it comes to tool usage. So, therefore, you kind of—there's this give and take between uh low-latency model versus how good they are at following instructions. Therefore, in this particular situation, the best model will be the GPT-4.1 Mini because of how quick it is and how low-latency model is. So, therefore, moving forward and all of your voice AI agents, I think the GPT-4.1 Mini is going to be the best one. You can try the Nano as well, cuz the GPT-4.1 Nano is the fastest one; however, when it comes to usage, this might be a bit limited compared to the 4.1 mini. So, therefore, I say I think for uh moving forward the GPT-4.1 Mini is going to be the best combination of cost, low latency, and instruction follow. And I'm going to keep testing this out as I build more complex AI agents, especially as I'm focusing on building more uh MCP related agents and uh voice agents for the community and then also for my channel, then this is extremely important, this becomes extremely crucial, and I'm going to keep testing this out, and I'm going to make another video probably in a couple of weeks just to see if I find something different. But based on what I've seen so far, based on what I've tested so far, this is extremely promising, and I think it's the best model at the current stage to use for your AI agents.
All right, well, hopefully this video was not too long and I didn't bore you. Hopefully you found this uh helpful. Again, make sure you like and subscribe because I've got a lot of content coming up. And again, if you want to learn how to build amazing no-code AI agents, make sure you join the community. We've got a classroom section that's full of amazing content; we've got exclusive deep-dive topics on building full-stack web apps with LangChain and then voice agents, vector databases—all things AI agents. We have a calendar section where you can jump in with us and follow us when co-builds or tech support or anything you need, and then obviously we have a great group of people who are passionate about building amazing AI agents, and it's a great space for you to make connections and collaborate. So, make sure you join the community. I'm going to put the link in the description again. Thanks for watching. I'll see you on the next one.