DeepSeek vs ChatGPT: Detailed AI Model Comparison

Imagine you are driving a car and in a split second you notice: a pedestrian on the left, a traffic light ahead, and a “yield” sign on the side. The brain instantly processes the image, recognizes what is where, and makes a decision. Computers have learned to do this too. This is called object detection, a task in which you not only need to see what is in an image (for example, a dog), but also understand exactly where it is located. Neural networks are required for this. And one of the fastest and most popular ones is YOLO, or “You Only Look Once.” Now let’s break down what it does and why developers around the world love it. What YOLO Object Detection Does There is a simple task: to understand that there is a cat in a photo. Many neural networks can do this: we upload an image, and the model tells us, “Yes, there is a cat here.” This is called object recognition, or classification. All it does is assign a label to the image. No coordinates, no context. Just “cat, 87% confidence.” Now let’s complicate things. We need not only to understand that there is a cat in the photo, but also to show exactly where it is sitting. And not one, but three cats. And not on a clean background, but among furniture, people, and toys. This requires a different task: YOLO object detection. Here’s the difference: Recognition (classification): one label for the entire image. Detection: bounding boxes and labels inside the image: here’s the cat, here’s the ball, here’s the table. There is also segmentation: when you need to color each pixel in the image and precisely outline the object's shape. But that’s a different story. Object detection is like working with a group photo: you need to find yourself, your friends, and also mark where each person is standing. Not just “Natalie is in the frame,” but “Natalie is right there, between the plant and the cake.” YOLO does exactly that: it searches, finds, and shows where and what is located in an image. And it does not do it step by step, but in one glance—more on that in the next section. How YOLO Works: Explained Simply YOLO stands for You Only Look Once, and that’s the whole idea. YOLO looks at the image once, as a whole, without cutting out pieces and scanning around like other algorithms do. This approach is called YOLO detection—fast analysis of the entire scene in a single pass. All it needs is one overall look to understand what is in the image and where exactly. How Does Recognition Work? Imagine the image is divided into a grid. Each cell is responsible for its own part of the picture, as if we placed an Excel table over the photo. This is how a YOLO object detection algorithm delegates responsibility to each cell. An image of a girl on a bicycle overlaid with a 8×9 grid: an example of how YOLO labels an image. Each cell then: tries to determine whether there is an object (or part of an object) inside it, predicts the coordinates of the bounding box (where exactly it is), and indicates which class the object belongs to, for example, “car,” “person,” or “dog.” If the center of an object falls into a cell, that cell is responsible for it. YOLO does not complicate things: each object has one responsible cell. To better outline objects, YOLO predicts several bounding boxes for each cell, different in size and shape. After this, an important step begins: removing the excess. What if the Neural Network Sees the Same Object Twice? YOLO predicts several bounding boxes for each cell. For example, a bicycle might be outlined by three boxes with different confidence levels. To avoid chaos, a special filter is used: Non-Maximum Suppression (NMS). This is a mandatory step in YOLO detection that helps keep only the necessary boxes. It works like this: It compares all boxes claiming the same object. Keeps only the one with the highest confidence. Deletes the rest if they overlap too much. As a result, we end up with one box per object, without duplicates. What Do We Get? YOLO outputs: a list of objects: “car,” “bicycle,” “person”; bounding box coordinates showing where they are located; and the confidence level for each prediction: how sure the network is that it got it right. An example of YOLO in action: the bicycle in the photo is outlined and labeled with its class and confidence score, and the image is divided into a 6×6 grid. And all of this—in a single pass. No stitching, iteration, or sequential steps. Just: “look → predict everything at once.” Why YOLO is Fast and What the “One Glance” Feature Means Most neural networks that recognize objects work like this: first, find where an object might be, and then check what it is. This is like searching for your keys by checking: under the table, then in the drawer, then behind the sofa. Slow, but careful. YOLO works differently. It looks at the entire image at once and immediately says what is in it, where it is located, and how confident it is. Imagine you walk into a room and instantly notice a cat on the left, a coat on the chair, and socks on the floor. The brain does not inspect each corner one by one; it sees the whole scene at once. YOLO does the same, just using a neural network. Why this is fast: YOLO is one large neural network. It does not split the work into stages like other algorithms do. No “candidate search” stage, then “verification.” Everything happens in one pass. The image is split into a grid. Each cell analyzes whether there is an object in it. And if there is, it predicts what it is and where it is. Fewer operations = higher speed. YOLO doesn’t run the image through dozens of models. That’s why it can run even on weak hardware, from drones to surveillance cameras. Ideal for real-time. While other models are still thinking, YOLO has already shown the result. It is used where speed is critical: in drones, games, AR apps, smart cameras. YOLO sacrifices some accuracy for speed. But for most tasks this is not critical. For example, if you are monitoring safety in a parking lot, you don’t need a perfectly outlined silhouette of a car. You need YOLO to quickly notice it and point out where it is. That’s why YOLO is often chosen when speed is more important than millimeter precision. It’s not the best detective, but an excellent first responder. How to Understand Whether a Neural Network Works Well Let’s say the neural network found a bicycle in a photo. But how well did it do this? Maybe the box covers only half the wheel? Or maybe it confused a bicycle with a motorcycle? To understand how accurate a neural network is, special metrics are used. There are several of them, and they all help answer the question: how well do predictions match reality? When training a YOLO model, these parameters are important—they affect the final accuracy. IoU: How Accurately the Location Was Predicted The most popular metric is IoU (Intersection over Union). Imagine: there is a real box (human annotation) and a predicted box (from the neural network). If they almost match, great. How IoU is calculated: First, the area where the boxes overlap is calculated. Then, the area they cover together. We divide one by the other and get a value from 0 to 1. The closer to 1, the better. Example: Comment IoU Full match 1.0 Slightly off 0.6 Barely hit the object 0.2 An image of a bicycle with two overlapping rectangles: green for the human annotation and red for YOLO’s prediction. The rectangles partially overlap. In practice, if IoU is above 0.5, the object is considered acceptably detected. If below, it’s an error. Precision and Recall: Accuracy and Completeness Two other important metrics are precision and recall. Precision: out of all predicted objects, how many were correct. Recall: out of all actual objects, how many were found. Simple example: The neural network found 5 objects. 4 of them are actually present; this is 80% precision. There were 6 objects in total. It found 4 out of 6—this is 66% recall. High precision but low recall = the model is afraid to make mistakes and misses some objects. High recall but low precision = the model is too bold and detects even what isn’t there. AP and mAP: Averaged Evaluation To avoid tracking many numbers manually, Average Precision (AP) is used. This is an averaged result between precision and recall across different thresholds. AP is calculated for one class, for example, “bicycle”. mAP (mean Average Precision) is the average AP across all classes: bicycles, people, buses, etc. If YOLO shows mAP 0.6, this means it performs at 60% on average across all objects. YOLO Architecture From the outside, YOLO looks like a black box: you upload a photo and get a list of objects with bounding boxes. But inside, it’s quite logical. Let’s see how this neural network actually understands what’s in the image and where everything is located. YOLO is a large neural network that looks at the entire image at once and immediately does three things: it identifies what is shown, where it is located, and how confident it is in each answer. It doesn’t process image regions step by step—it processes the whole scene in one go. That’s what makes it so fast. To achieve this, it uses a special type of layer: convolutional layers. They act like filters that sequentially extract features. At first, they detect simple patterns—lines, corners, color transitions. Then they move on to more complex shapes: silhouettes, wheels, outlines of objects. In the final layers, the neural network begins to recognize familiar items: “this is a bicycle,” “this is a person”. The main feature of YOLO is grid-based labeling. The image is divided into equal cells, and each cell becomes the “observer” of its own zone. If the center of an object falls within a cell, that cell takes responsibility: it predicts whether there’s an object, what type it is, and where exactly it’s located. But to avoid confusion from multiple overlapping boxes (since YOLO often proposes several per object), a final-stage filter, Non-Maximum Suppression (NMS), is used. It keeps only the most confident bounding box and removes the rest if they’re too similar. The result is a clean, organized output: what’s in the image, where it is, and how confident YOLO is about each detection. That’s YOLO from the inside: a fast, compact, and remarkably practical architecture, designed entirely for speed and efficiency. How YOLO Evolved Since YOLO’s debut in 2015, many versions have been released. Each new version isn’t just “a bit faster” or “a bit more accurate,” but a step forward—a new approach, new architectures, improved metrics. Below is a brief evolution of YOLO. YOLOv1 (2015) The version that started it all. YOLO introduced a revolutionary idea: instead of dividing the detection process into separate stages, do everything at once—detect and locate objects in a single pass. It worked fast, but struggled with small objects. YOLOv2 (2016), also known as YOLO9000 Added anchor boxes—predefined bounding box shapes that helped detect objects of different sizes more accurately. Also introduced multi-scale training, enabling the model to better handle both large and small objects. The name “9000” refers to the number of classes YOLO could recognize. YOLOv3 (2018) A more powerful architecture using Darknet-53 instead of the previous network. Implemented a feature pyramid network (FPN) to detect objects at multiple scales. YOLOv3 became much more accurate, especially for small objects, while still operating in real time. YOLOv4 (2020) Developed by the community, without the original author’s involvement. Everything possible was improved: a new CSPNet backbone, optimized training, advanced data augmentation, smarter anchor boxes, DropBlock, and a “Bag of Freebies”—a set of methods to improve training speed and accuracy without increasing model size. YOLOv5 (2020) An open-source project by Ultralytics. It began as an unofficial continuation but quickly became the industry standard. It was easy to launch, simple to train, and worked efficiently on both CPU and GPU. Added SPP (Spatial Pyramid Pooling), improved anchor box handling, and introduced CIoU loss—a new loss function for more accurate learning. YOLOv6 (2022) Focused on device performance. Used a more compact network (EfficientNet-Lite) and improved detection in poor lighting and low-resolution conditions. Achieved a solid balance between accuracy and speed. YOLOv7 (2022) One of the fastest and most accurate models at the time. It supported up to 155 frames per second and handled small objects much better. Used focal loss to capture difficult objects and a new layer aggregation system for more efficient feature processing. Overall, it became one of the best real-time models available. YOLOv8 (2023) Introduced a user-friendly API, improved accuracy, and redesigned its architecture for modern PyTorch. Adapted for both CPU and GPU, supporting detection, segmentation, and classification tasks. YOLOv8 became the most beginner-friendly version and a solid foundation for advanced projects—capable of performing detection, segmentation, and classification simultaneously. YOLOv9 (2024) Designed with precision in mind. Developers improved how the neural network extracts features from images, enabling it to better capture fine details and handle complex scenes—for example, crowded photos with many people or objects. YOLOv9 became slightly slower than v8 but more accurate. It’s well-suited for tasks where precision is critical, such as medicine, manufacturing, or scientific research. YOLOv10 (2024) Introduced automatic anchor selection—no more manual tuning. Optimized for low-power devices, such as surveillance cameras or drones. Supports not only object detection but also segmentation (boundaries), human pose estimation, and object type recognition. YOLOv11 (2024) Maximum performance with minimal size. This version reduced model size by 22%, while increasing accuracy. YOLOv11 became faster, lighter, and smarter. It understands not only where an object is, but also the angle it’s oriented at, and can handle multiple task types—from detection to segmentation. Several versions were released—from the ultra-light YOLOv11n to the powerful production-ready YOLOv11x. YOLOv12 (2025) The most intelligent and accurate YOLO to date. This version completely reimagined the architecture: now the model doesn’t just “look” at an image but distributes attention across regions—like a human scanning a scene and focusing on key areas. This allows for more precise detection, especially in complex environments. YOLOv12 handles small details and crowded scenes better while maintaining speed. It’s slightly slower than the fastest versions, but its accuracy is higher. It’s suitable for everything: detection, segmentation, pose estimation, and oriented bounding boxes. The model is universal—it works on servers, cameras, drones, and smartphones. The lineup includes versions from the compact YOLO12n to the advanced YOLO12x. Where YOLO Is Used in Real Life YOLO isn’t confined to laboratories. It’s the neural network behind dozens of everyday technologies—often invisible, but critically important. That’s why how YOLO is used is a question not just for programmers, but for businesses as well. In self-driving cars, YOLO serves as their “eyes.” While a human simply drives and looks around, the car must detect pedestrians, read road signs, distinguish cars, motorcycles, dogs, and cyclists—all in fractions of a second. YOLO enables this real-time perception without lengthy computations. The same mechanisms power surveillance cameras. YOLO can distinguish a person from a moving shadow, detect abandoned objects, or alert when an unauthorized person enters a monitored area. This is crucial in airports, warehouses, and smart offices. YOLO is also used in retail analytics—not at the checkout, but in behavioral tracking. It can monitor which shelves attract attention, how many people approach a display, which products are frequently picked up, and which are ignored. These insights become actionable analytics: retailers learn how shoppers move, what to rearrange, and what to remove. In augmented reality, YOLO is indispensable. To “try on” glasses on your face or place a 3D object on a table via a phone camera, the system must first understand where that face or table is. YOLO performs this recognition quickly—even on mobile devices. Drones with YOLO can recognize ground objects: people, animals, vehicles. This is used in search and rescue, military, and surveillance applications. It’s chosen not only for its accuracy but also for its compactness—YOLO can run even on limited hardware, which is vital for autonomous aerial systems. Such YOLO object detection helps rescuers locate targets faster. Even in manufacturing, YOLO has applications. On an assembly line, it can detect product defects, count finished items, or check whether all components are in place. Robots with such systems work more safely: if a person enters the workspace, YOLO notices and triggers a stop command. Everywhere there’s a camera and a need for fast recognition, YOLO can be used. It’s a simple, fast, and reliable system that, like an experienced worker, doesn’t argue or get distracted—it just does its job: sees and recognizes. When YOLO Is Not the Best Choice YOLO excels at speed, but like any technology, it has limitations. The first weak point is small objects—for example, a distant person in a security camera or a bird in the sky. YOLO might miss them because it divides the image into large blocks, and tiny objects can “disappear” within the grid. The second issue is crowded scenes—when many objects are close together, such as a crowd of people, a parking lot full of cars, or a busy market. YOLO can mix up boundaries, overlap boxes, or merge two objects into one. The third is unstable conditions: poor lighting, motion blur, unusual angles, snow, or rain. YOLO can handle these to an extent, but not perfectly. If a scene is hard for a human to interpret, the neural network will struggle too. Another limitation is fine-grained classification. YOLO isn’t specialized for subtle distinctions—for instance, differentiating cat breeds, car makes, or bird species. It’s great at distinguishing broad categories like “cat,” “dog,” or “car,” but not their nuances. And finally, performance on weak hardware. YOLO is fast, but it’s still a neural network. On very low-powered devices—like microcontrollers or older smartphones—it might lag or fail to run. There are lightweight versions, but even they have limits. This doesn’t mean YOLO is bad. It simply needs to be used with understanding. When speed is the priority, YOLO performs excellently. But if you need to analyze a scene in extreme detail, detect twenty objects with millimeter precision, and classify each one, you might need another model, even if it’s slower. The Bottom Line YOLO is like a person who quickly glances around and says, “Okay, there’s a car, a person, a bicycle.” No hesitation, no overthinking, no panic—just confident awareness. It’s chosen for tasks that require real-time object recognition, such as drones, cameras, augmented reality, and autonomous vehicles. It delivers results almost instantly, and that’s what makes it so popular. YOLO isn’t flawless—it can miss small objects or struggle in complex scenes. It doesn’t “think deeply” or provide lengthy explanations. But in a world where decisions must be made fast, it’s one of the best tools available. If you’re just starting to explore computer vision, YOLO is a great way to understand how neural networks “see” the world. It shows that object recognition isn’t magic—it’s a structured process: divide, analyze, and outline. And if you’re simply a user, not a programmer, now you know how self-checkout kiosks, surveillance systems, and AR try-ons work. Inside them, there might be a YOLO model doing one simple thing: looking. But it does it exceptionally well.

Swagger is a universal set of tools for designing, documenting, testing, and deploying REST APIs based on the widely accepted OpenAPI and AsyncAPI standards. API vs REST API API (Application Programming Interface) is a set of rules and tools for interaction between different applications. It defines how one program can request data or functionality from another. For example, calling a method from the mathematical module of the Python programming language is the simplest form of a local API, through which different components of a program can exchange data with each other: import math  # importing the math module result = math.sqrt(16)  # calling the API method of the math module to calculate the square root print(f"The square root of 16 is {result}")  # outputting the result to the console In this case, the math module provides a set of functions for working with mathematical operations, and the sqrt() function is part of the interface that hides the internal implementation of the module. REST API is an API that follows the principles of REST (Representational State Transfer) architecture, in which interaction with the resources of another program is performed via HTTP requests (GET, POST, PUT, DELETE) represented as URLs (endpoints). For example, retrieving, sending, deleting, and modifying content on websites on the Internet is done by sending corresponding requests to specific URLs with optional additional parameters if required: Retrieving the main page GET / HTTP/1.1 Host: website.com Retrieving the first page of article listings GET /website.com/page/1 HTTP/1.1 Host: website.com Retrieving the second page of article listings GET /website.com/page/2 HTTP/1.1 Host: website.com Publishing a new article POST /website.com/newarticle HTTP/1.1 Host: website.com Deleting an existing article DELETE /website.com/article/some-article HTTP/1.1 Host: website.com Thus, a REST API is an extension of an API that defines a specific type of interaction between programs. OpenAPI vs AsyncAPI As network interactions developed, the need arose to unify API descriptions to simplify their development and maintenance. Therefore, standards began to emerge for REST and other API types. Currently, two standards are popular. One describes synchronous APIs, and the other describes asynchronous ones: OpenAPI: Intended for REST APIs. Represents synchronous message exchange (GET, POST, PUT, DELETE) over the HTTP protocol. All messages are processed sequentially by the server. For example, an online store that sends product listing pages for each user request AsyncAPI: Intended for event-driven APIs. Represents asynchronous message exchange over protocols such as MQTT, AMQP, WebSockets, STOMP, NATS, SSE, and others. Each individual message is sent by a message broker to a specific handler. For example, a chat application that sends user messages via WebSockets. Despite architectural differences, both standards allow describing application APIs as specifications in either JSON or YAML. Both humans and computers can read such specifications. Here is an example of a simple OpenAPI specification in YAML format: openapi: 3.0.0 info: title: Simple Application description: This is just a simple application version: 1.0.1 servers: - url: http://api.website.com/ description: Main API - url: http://devapi.website.com/ description: Additional API paths: /users: get: summary: List of users description: This is an improvised list of existing users responses: "200": description: List of users in JSON format content: application/json: schema: type: array items: type: string The full specification description is available on the official OpenAPI website. And here is an example of a simple AsyncAPI specification in YAML format: asyncapi: 3.0.0 info: title: 'Simple Application' description: 'This is just a simple application' version: '1.0.1' channels: some: address: 'some' messages: saySomething: payload: type: string pattern: '^Here’s a little phrase: .+$' operations: something: action: 'receive' channel: $ref: '#/channels/some' You can find the full specification description on the official AsyncAPI website. Based on a specification, we can generate various data: documentation, code, tests, etc. In fact, it can have a wide range of applications, to the point where a neural network could generate all the client and server code for an application.   Thus, a specification is a set of formal rules that governs how an interface to an application operates. This is exactly where tools like Swagger come into play. With them, you can manage an API specification visually: edit, visualize, and test it. And most importantly, Swagger allows you to generate and maintain documentation that helps explain how a specific API works to other developers. Swagger Tools Aiming to cover a wide range of API-related tasks, Swagger provides tools with different areas of responsibility: Swagger UI. A browser-based tool for visualizing the specification. It allows sending requests to the API directly from the browser and supports authorization through API keys, OAuth, JWT, and other mechanisms. Swagger Editor. A browser-based tool for editing specifications with real-time visualization. It edits documentation, highlights syntax, performs autocompletion, and validates the API. Swagger Codegen. A command-line tool for generating server and client code based on the specification. It can generate code in JavaScript, Java, Python, Go, TypeScript, Swift, C#, and more. It is also responsible for generating interactive documentation accessible from the browser. Swagger Hub. A cloud-based tool for team collaboration using Swagger UI and Swagger Editor. It stores specifications on cloud servers, performs versioning, and integrates with CI/CD pipelines. Thus, with Swagger, we can visualize, edit, generate, and publish an API.  You can find the full list of the Swagger tools on the official website. Who Needs Swagger and Why So far, we have examined what Swagger is and how it relates to REST APIs.  Now is a good time to discuss who needs it and why. The main users of Swagger are developers, system analysts, and technical writers: the first develop the API, the second analyze it, and the third document it. Accordingly, Swagger can be used for the following tasks: API Development. Visual editing of specifications makes using Swagger so convenient that it alone accelerates API development. Moreover, Swagger can generate client and server code implementing the described API in various programming languages. API Interaction. Swagger assists in API testing, allowing requests with specific parameters to be sent to exact endpoints directly from the browser. API Documentation. Swagger helps form an API description that can later be used to create interactive documentation for API users. That is exactly why we need Swagger and all of its tools: for step-by-step work on an API. How to Use Swagger So, how does Swagger work? As an ecosystem, Swagger performs many functions related to API development and maintenance. Editing a Specification For interactive editing of REST API specifications in Swagger, there is a special tool, Swagger Editor. Swagger Editor interface: the left side contains a text field for editing the specification, while the right side shows real-time visualization of the documentation. You can work on a specification using either the online version of the classic Swagger Editor or the updated Swagger Editor Next. Both editors visualize live documentation in real time based on the written specification, highlighting any detected errors. Through the top panel, you can perform various operations with the specification content, such as saving or importing its file. Swagger Editor Next offers a more informative interface, featuring a code minimap and updated documentation design. Of course, it is preferable to use a local version of the editor. The installation process for Swagger Editor and Swagger Editor Next is described in detail in the official Swagger documentation. Specification Visualization Using the Swagger UI tool, we can visualize a written specification so that any user can observe the structure of an API application. A detailed guide for installing Swagger UI on a local server is available in the Swagger docs. There, you can also test a demo version of the Swagger UI dashboard. A demo page of the Swagger UI panel that visualizes a test specification. It is through Swagger UI that documentation management becomes interactive. Via its graphical interface, a developer can perform API requests as if they were being made by another application. Generating Documentation Based on a specification, the Swagger Codegen tool can generate API documentation as an HTML page. You can download Swagger Codegen from the official Swagger website. An introduction to the data generation process is available in the GitHub repository, while detailed information and examples can be found in the documentation. However, it is also possible to generate a specification based on special annotations in the application’s source code. We can perform automatic parsing of annotations using third-party libraries that vary across programming languages. Among them: Gin-Swagger for Go with the Gin framework Flask-RESTPlus for Python with the Flask framework Swashbuckle for C# and .NET with the ASP.NET Core framework FastAPI for Python It works roughly like this: Library connection. The developer links a specification-generation library to the application’s source code. Creating annotations. In the parts of the code where API request handling (routing) occurs, the developer adds special function calls from the connected library, specifying details about each endpoint: address, parameters, description, etc. Generating the specification. The developer runs the application; the functions containing API information execute, and as a result, a ready YAML or JSON specification file is generated. It can then be passed to one of the Swagger tools, for example, to Swagger UI for documentation visualization. As you can see, a specification file acts as a linking element (mediator) between different documentation tools. This is the power of standardization—a ready specification can be passed to anyone, anywhere, anytime. For example, you can generate a specification in FastAPI, edit it in Swagger Editor, and visualize it in ReDoc. Code Generation Similarly, Swagger Codegen can generate client and server code that implements the logic described in the specification. You can find a full description of the generation capabilities in the official documentation. However, it’s important to understand that generated code is quite primitive and hardly suitable for production use. There are several fundamental reasons for this: No business logic. The generated code does nothing by itself. It’s essentially placeholders, simple request handlers suitable for quick API testing. No real processes. Besides general architecture, generated code lacks lower-level operations such as data validation, logging, error handling, database work, etc. Low security. There are no checks or protections against various attack types. Therefore, generated code is best used either as a starting template to be extended manually later or as a temporary solution for hypothesis testing during development. API Testing Through Swagger UI, you can select a specific address (endpoint) with given parameters to perform a test request to the software server that handles the API. In this case, Swagger UI will visually display HTTP responses, clearly showing error codes, headers, and message bodies. This eliminates the need to write special code snippets to create and process test requests. Swagger Alternatives Swagger is not the only tool for working with APIs. There are several others with similar functionality: Postman. A powerful tool for developing, testing, and documenting REST and SOAP APIs. It allows sending HTTP requests, analyzing responses, automating tests, and generating documentation. Apidog. A modern tool for API development, testing, documentation, and monitoring. It features an extremely beautiful and user-friendly interface in both light and dark themes. ReDoc. A tool for generating interactive documentation accessible from a browser, based on an OpenAPI specification. Apigee. A platform for developing, managing, and monitoring APIs owned by Google and part of the Google Cloud Platform. Mintlify. A full-fledged AI-based platform capable of generating an interactive, stylish documentation website through automatic analysis of a project’s repository code. API tools differ only in implementation nuances and individual features. However, these differences may be significant for particular developers — it all depends on the project. That’s why anyone interested in API documentation should first explore all available platforms. It may turn out that a simple tool with few parameters is suitable for one project, while another requires a complex system with many settings. Conclusion It’s important to understand that Swagger is a full-fledged framework for REST API development. This means that a developer is provided with a set of tools for maintaining an application’s API —from design through documentation for end users. At the same time, beyond classic console tools, Swagger visualizes APIs clearly through the browser, making it suitable even for beginners just starting their development journey. If any Swagger tool does not fit a particular project, it can be replaced with an alternative, since an application’s API is described in standardized OpenAPI or AsyncAPI formats, which are supported by many other tools. You can find Swagger tutorials and comprehensive information about its tools in the official Swagger documentation.

Neural networks and artificial intelligence can process not only text data, videos, and graphics but also work with audio information. This capability makes it possible to create music. Just a few years ago, it was believed that creating your own musical compositions required a studio and instruments, or at least the skills to work with specialized software. However, the rapid growth of artificial intelligence is completely changing this paradigm—now, AI takes on the entire process of creating musical compositions. The user only needs to create a text prompt specifying the requirements for the composition. Today, we review top AI music creation platforms: Suno AI, AIVA, Soundraw, Mubert, MusicGEN, Loudly, Riffusion. How AI Makes Music Before reviewing the AI platforms, let's understand how they make music. Typically, AI uses deep learning to create musical compositions. This method allows analyzing large volumes of musical data and generating new compositions based on it. The algorithm for generating music involves training a model on large datasets (e.g., MIDI files and audio recordings) and then generating music based on parameters such as genre or instruments. Below are the types of neural networks used in music creation: Recurrent Neural Networks (RNN) A recurrent neural network is a deep learning model trained to process and transform sequential sets of input data into sequential output. Sequential data are data in which components have a strict order and relationships based on complex semantics and syntactic rules, such as words and sentences. As mentioned earlier, RNNs are well-suited for working with sequences. In music, these sequences are melodies and chords, thanks to the network’s ability to "remember" previous notes. Transformers Transformers are a type of neural network architecture designed to transform an input sequence into an output sequence. They study context and track relationships between components of a sequence. In music creation, transformers are used to handle complex musical structures and generate multilayered compositions. Generative Adversarial Networks (GAN) GANs are named for their use of two neural networks that "compete" with each other: one network generates data samples, while the other tries to predict whether the data is original. In music generation, one network creates tracks while the other evaluates their quality, improving the final result as needed. Autoencoders Autoencoders are neural networks that do not use supervision during training and do not rely on data compression. They are used to create variations based on existing tracks or to apply musical stylization. Suno AI Suno AI is a popular AI music software launched in December 2023 that creates vocal and instrumental tracks using a simple text prompt. You can specify the style of the composition and the song lyrics in the prompt. Its popularity led Suno, Inc., in partnership with Microsoft, to integrate Suno AI into the Microsoft Copilot chatbot. Suno AI is ideal for background music and advertising tracks. Advantages: Simple and user-friendly web interface. Supports using images and videos in addition to text prompts. Completely ad-free in the free version. Provides editing tools for generated tracks. Automatic selection of cover images for compositions. Official mobile app available for iOS and Android. Disadvantages: The free version includes 50 credits, allowing only 5 compositions per day; 50 more credits are added daily. Duration limits depend on the AI model used: v2 up to 1:20 min, v3 up to 2 min, v3.5 up to 4 min. AIVA AIVA is one of the best AI music generators designed specifically for creating music, from classical and symphonic compositions to electronic dance music tracks. AIVA was first released in February 2016 by Luxembourg-based Aiva Technologies SARL. Advantages: Advanced editing tools: change tempo, key, duration, style, and instruments. Ability to upload existing tracks to use as templates. Export of compositions in MIDI, WAV, or MP3. Official documentation available. Available as a web interface or desktop app (Windows, macOS, Linux). Monetization of tracks (only in the Pro plan). Disadvantages: The free plan allows only 3 downloads per month. Limited editing features in the free version. Soundraw Soundraw is an online AI song generator, launched in February 2020 by Japanese company SOUNDRAW, Inc. Soundraw is suitable for creating tracks in any genre. It can be used by individuals to create personal tracks or by artists and labels for commercial music (paid plans only). Advantages: Simple, intuitive web interface. Ability to mix multiple genres in a track. Extensive editing options: track length, tempo, genre, mood (epic, happy, angry, sentimental, romantic, etc.), and theme (corporate, cinematic, comedy, documentary, etc.). API available (as of 2025, API for music generation is available in the Enterprise plan only). Disadvantages: Track downloads require a subscription. Mubert Mubert is an online AI platform for generating music tracks in real-time using text prompts, images (.png, .jpg, .webp), or by selecting a genre. Ideal for background music in videos and podcasts. Advantages: Simple 3-click track creation. You can specify genre, mood, track type (Track, Loop, Mix, Jungle), and duration (5 seconds–25 minutes). API available (beta) for registered users. Mubert Studio allows monetization and promotion of tracks. Official iOS and Android apps available. Integration with YouTube, Twitch, TikTok, Streamlabs, Kick. Disadvantages: Instrumental-only tracks; no vocals. Free plan: 30 min/day, 25 tracks/month; paid plans increase limits (up to 500–1000 tracks). Cannot mix multiple genres or use sound effects. No track stems or MIDI export. MusicGEN MusicGEN is a simple AI service for creating music via text prompts or audio samples. Focused on short tracks (up to 2 minutes). Requires installation and setup, which can be challenging for beginners. Advantages: Simple interface. Open-source AudioCraft language model used in MusicGEN and AudioGen. Ready-made implementations available online. Disadvantages: Requires technical skills for setup. Tracks limited to 15 seconds. No customization during track creation. Loudly Loudly is a platform with built-in AI for generating music and tracks. Tracks can be created via text description or a built-in generator. Ideal for social media, videos, and streaming services. Advantages: Rich functionality: choose instruments, genre (15+ including EDM, Hip Hop, Techno, Rock), tempo, subgenres. Built-in templates with flexible filters. API available on request. Disadvantages: Free version: 25 tracks/month, 30 sec each; cannot download tracks. Riffusion Riffusion is an AI service based on the Stable Diffusion deep learning model, generating short music fragments including vocals using text prompts. Advantages: Free, unlimited creation in "relax mode." Ability to create remixes and covers. You can provide the song lyrics.  The web version allows grouping tracks into projects and playlists. Disadvantages: Paid plan required for commercial use. Paid plans allow audio uploads, WAV and Stem downloads. Limited editing functionality compared to competitors. Conclusion: Comparative Table Feature Suno AI AIVA Soundraw Mubert MusicGEN Loudly Riffusion Music creation method Text, images, video Styles, chords, MIDI, or track Interface with options Text, images, filters (genre, mood, tempo) Text prompt, audio import Text prompt, generator Text, image, interface with options Free plan Limited: 5 compositions/day (50 credits) Limited: 3 tracks/month, max 3 min, MP3/MIDI only Limited: cannot download Limited: 25 tracks/month, MP3 only Unlimited Limited: 25 tracks/month, max 30 sec, no download Limited: cannot download or use commercially Paid plans Pro $10, Premier $30/month, 20% annual discount Standard €15, Pro €49/month, 33% annual discount $11.04–$32.49/month, Enterprise by request $11.69–$149.29/month, custom & lifetime plans None (open-source) Personal $10, Pro $30/month Starter $8, Member $48/month, 25% annual discount Interface language English English English, Japanese English, Spanish, Korean English English English Supported song languages 50+ English English English English English English Music editing Text, style, audio template, instrumental style, duration Tempo, chords, instruments, effects, duration Tempo, genre, mood, theme, duration Genre, mood, track type, duration (5 sec–25 min) None Genre, mood, tempo, instruments, duration Text, style Commercial use Paid plans only Pro plan only Artist Starter & above Paid plans only None Paid plans only Paid plans only API No No Yes Yes (on request) No Yes No Export formats Free: MP3, Paid: MP3, WAV, stems Free: MP3, MIDI; Pro: MP3, WAV Paid only: MP3, WAV, stems Free: MP3 (25 tracks/month), Paid: up to 1000 tracks WAV only Paid: MP3, WAV Paid: WAV, stems Mobile app Yes (iOS, Android) No No No No Yes (iOS, Android) No Desktop app No Yes (Windows, macOS, Linux) No No No No No