AI Coding and Best Tools Compared

Over the past few years, AI-driven coding software has evolved. Once experimental helpers, they've become essential parts of the developer’s toolkit. While human expertise remains critical, AI now plays a much more active role in writing, reviewing, and optimizing code. Sam Babic, the Chief Technical Adviser at US-based Hyland Software Inc., described these solutions as a “second set of eyes,” offering a fresh perspective and valuable assistance in programming.

This article will define AI coding, explore its main benefits and features, and evaluate eight notable AI coding tools.

What is AI coding?

AI coding refers to using artificial intelligence to assist developers in writing and reviewing their code with the ultimate aim of enhancing code quality and boosting productivity. It is one of the many innovations present in AI-driven software development workflows.  

This practice is rapidly gaining popularity, as evidenced by a GitHub survey revealing that 92 percent of U.S.-based developers already leverage AI coding tools both in and out of work.

AI coding tools features

Here’s an overview of the standard features that many AI coding tools provide.

• Code completion: Suggesting code snippets as developers type by predicting the next code that is likely to be written based on the context.
• Code generation: Transforms natural language prompts that describe the functionality you want into executable, contextually relevant code that aligns with your programming patterns. Well-structured prompts play a great role in the quality of output you receive.
• Code translation: Converts source code into the desired programming language. This is useful for integrating code from different teams and can also reduce the effort and expense of updating legacy codebases written in older programming languages like COBOL or Ada.
• Code review: AI tools can evaluate code to detect code quality issues, bugs, and errors, and help fix them. They can also generate natural-language comments and explanations.
• Agent AI: AI assistants that can autonomously plan and execute multistep tasks with minimal human intervention. Agentic AI models can understand and break down goals into smaller steps and execute them or propose a plan for human approval. While performing tasks, they can interact with files, databases, external data, and more.
• MCP server support. Many AI coding tools now support the Model Context Protocol (MCP), allowing you to easily access various third-party data sources and tools. There are various third-party MCP servers for developers, including those from GitHub and BrowserStack.

Apart from the functionality mentioned here, some AI coding tools are also capable of code refactoring, code documentation, performing web searches, providing explanations, and searching for certain files and code snippets.

Best AI coding tools compared

The AI coding tools ecosystem is growing rapidly, with new options emerging constantly. Our goal isn’t to compare all options, but to give you a focused overview of some of the most popular and widely used tools on the market today. While there are many more options worth exploring, we've chosen to cover the ones that have gained the most traction among developers and engineering teams, as well as non-technical users, giving you a focused look at the tools you're most likely to encounter or consider in your own workflow.

GitHub Copilot: IDE plugin with agentic capabilities

GitHub Copilot is an AI pair programmer that works alongside you as you code. Copilot is not a dedicated IDE, but a plugin you can access on code editors or as a CLI tool for terminal-based workflows. It is also available on the GitHub platform as a chat interface, through GitHub Mobile, and via an SDK for teams that want to embed its capabilities into their own tools and platforms.

Core features. These are some main features GitHub Copilot provides.

• Agent mode: A chat mode inside your IDE that allows Copilot to handle multistep tasks across your entire codebase, including writing code, opening pull requests, and running tests. You stay actively involved throughout the session, reviewing and steering the work as it progresses. Copilot provides 3 more modes: Ask for questions and code explanations; Edit, which makes changes for you to review/accept; and Plan, which creates a detailed implementation plan before writing any code.
• Coding agent: While agent mode works inside your IDE, the coding AI agent is a fully autonomous background worker that runs entirely on GitHub's infrastructure—via a secure, sandboxed environment powered by GitHub Actions—and only hands back control when the work is done. It handles things like bug fixes, refactoring, test coverage, documentation updates, and addressing technical debt.
• Multiplatform task assignment: You can kick off tasks from wherever your team actually works, including Slack, Teams, GitHub Issues, and VS Code. Copilot picks up the context from the conversation, including decisions, links, and code references, so you're not writing long prompts or duplicating tickets just to get it moving.
• Code pull request review: Copilot can review pull requests—either by a user’s manual request or via automations—and leave feedback with suggested changes you can apply with some clicks. Copilot itself can also implement the suggestions it leaves on reviews, and the coding agent will create a new pull request against your branch with the changes applied.

Model support. Copilot gives you access to 23 models from 4 major providers: OpenAI, Anthropic, Google, and xAI. GitHub actively refreshes its model lineup, retiring older models and replacing them with newer alternatives as the space evolves, so the options available today may look different in a few months.

Pricing. GitHub Copilot offers a free plan that includes 50 agent-mode or chat requests per month and 2,000 code completions per month. Paid plans for individuals start from $10/month/user and from $19/month/user for businesses.

Cursor: AI-native IDE that understands your entire codebase — and acts on it

Cursor is a standalone AI-native IDE based on Visual Studio Code. It is designed to be a drop-in replacement for VS Code but enhanced with deep AI capabilities out of the box.

Cursor is one of the tools that developers at AltexSoft prefer. “This code editor understands your codebase, predicts your next edits, and allows you to edit code using natural language instructions. It gives users deep control and granulated features,” says Glib Zhebrakov, Head of the Center of Engineering Excellence, at AltexSoft

Core features. Cursor offers many capabilities. Let’s review some of the main ones.

• Chat modes: Cursor gives you four ways to work with the AI, depending on what you need. Agent mode for autonomously handling complex multifile tasks with minimal guidance; plan mode for mapping out an implementation approach before any code is written, so you can review and approve the direction first; debug mode for diagnosing and fixing errors; and ask mode for exploring and understanding a codebase. You can also create custom modes by combining specific tools and instructions to match your exact workflow.

• Cloud agents (formerly background agents): While regular agent mode still needs you actively involved, cloud agents run fully autonomously in isolated cloud environments, so your local machine doesn't even need to stay connected. You give it a task, it clones your repo from GitHub or GitLab, works on a separate branch, makes the necessary changes, and pushes everything back to you for review. You can run as many in parallel as you want, so nothing has to wait on anything else.
• Subagents: When a task is complex, Cursor's agent can spin up specialized subagents to handle specific parts of the work, each running in its own context window so they don't bloat the main conversation. You can run multiple subagents in parallel, so different parts of a codebase can be worked on at the same time. Cursor has three built-in subagents it uses automatically: one for exploring and searching codebases, one for running shell commands, and one for controlling the browser. You can also create your own custom subagents for specialized tasks.
• Browser control: Cursor's agent can control a built-in browser directly from the editor, so you can test your application, fill out forms, click through workflows, check console logs and network traffic, audit accessibility, and take screenshots. There's also a design sidebar that lets you visually adjust layout, colors, spacing, and other CSS properties, and when you're happy with the changes, hitting “Apply” triggers an agent to translate those visual edits into actual code changes in your codebase.

Model support. Cursor supports 31 models from 5 providers: Anthropic, Google, OpenAI, xAI, and its own proprietary Composer models. There's also an "auto" toggle in the chat that lets Cursor automatically pick the best model for the task at hand.

Pricing. Cursor offers a free plan and its paid plans start at $20/month for users an $40/user/month for businesses.

Claude Code: Terminal-first coding agent built for complex, multifile work

Claude Code is Anthropic's coding agent, built primarily for the terminal. It lives inside your command line and works across your entire codebase, handling everything from reading issues and writing code to running tests and submitting pull requests.

That said, it's not limited to the terminal and is also available as a native extension in VS Code, Cursor, Windsurf, and JetBrains IDEs, through the Claude desktop app, via the iOS app for on-the-go access, and in Slack for kicking off tasks directly from your team's workspace.

Core features. Here are some of Claude Code’s main functionalities.

• Subagents: When a task gets complex, Claude Code can spin up specialized subagents to handle specific parts of the work, each running in its own isolated context window. There are three built-in subagents: Explore for searching and analyzing codebases, Plan for gathering codebase context during planning, and a general-purpose subagent for tasks that need both exploration and code changes. You can also create your own custom subagents with their own system prompts, tool restrictions, model choice, and persistent memory.
• Agent teams: While subagents report back to a single parent, agent teams allow multiple independent Claude Code sessions to work together as a coordinated team. One session acts as the lead, coordinating work and assigning tasks from a shared task list. The other sessions are teammates, each with their own context window, and they can message each other directly rather than just reporting back to the lead.
• CLAUDE.md: A configuration file you add to your project root that Claude Code reads at the start of every session, giving it persistent context about your codebase without you having to repeat yourself every time. You can use it to document project architecture, key directories, coding standards, common commands, testing requirements, and preferred workflows.

• Skills: Reusable instruction modules that extend what Claude Code can do. You create a SKILL.md file with instructions and Claude adds it to its toolkit, either invoking it automatically when the task matches or waiting for you to call it directly. Skills can be simple, like a code review checklist Claude applies whenever you push changes, or more complex, like a deploy workflow that runs tests, builds the app, and pushes to production.

• Hooks: Shell commands that fire automatically at specific points in Claude Code's lifecycle, letting you automate actions around your coding workflow. You can use them to auto-format code after every file edit, block commands before they run, send desktop notifications when Claude needs your input, reinject context after compaction, or audit configuration changes.

Model support. Claude Code runs exclusively on Anthropic's own models: Opus 4.6, Sonnet 4.6, and Haiku 4.5. Enterprise users can also route Claude Code through existing Amazon Bedrock or Google Cloud Vertex AI instances if they have compliance or infrastructure requirements that make that a better fit.

Pricing. Claude Code is billed based on API token usage, with the cost determined by which model you use. Prices are per million tokens (MTok).

• Opus 4.6: $5/MTok input and $25/MTok output
• Sonnet 4.6: $3/MTok input and $15/MTok output
• Haiku 4.5: $1/MTok input and $5/MTok output

For individual users, Claude Code is included in the Pro plan ($20/month) and the Max plans ($100/month and $200/month), which come with usage limits rather than per-token billing.

OpenAI Codex: Coding agent that turns tasks into pull requests

Codex is OpenAI’s coding agent, built to handle real engineering work from start to finish rather than simply suggesting the next line. It can take on tasks like building features, complex refactors, migrations, and code reviews, working through them autonomously and delivering results as pull requests ready for your review. It is available as a desktop app, an IDE extension, a CLI tool, and a web app, all connected through a ChatGPT account.

Core features. Here’s an overview of the capabilities you get with Codex.

• Parallel task execution with worktrees: The Codex app lets you run multiple tasks across projects at the same time using built-in Git worktree support. Each worktree is an isolated copy of your repository, so Codex can work on independent tasks side by side without touching your main branch or active work.
• Automations: You can set up recurring tasks that Codex runs in the background on a schedule, without you triggering them each time. This is useful for things like triaging issues, monitoring errors, running CI checks, or generating reports on recent code changes.
• Skills: Reusable instruction modules that extend what Codex can do. Create a skill once and invoke it explicitly or let Codex trigger it automatically when the task matches.
• Cloud tasks: Offload larger or longer-running jobs to Codex Cloud, where Codex works in its own managed cloud environment with no dependency on your local machine.
• AGENTS.md: A configuration file you add to your repository that Codex reads to understand your project's conventions, coding standards, and preferences. Codex automatically picks it up at the start of every session and you can nest multiple AGENTS.md files deeper in the tree to give specific instructions for particular packages or directories.
• Hooks: Scripts you configure to run automatically at specific points in Codex's agentic loop. You can use them for tasks like sending conversations to a custom logging engine and summarizing conversations to create persistent memories.

Model support. Codex gives you access to 12 models, all from OpenAI, with the recommended ones being GPT-5.4, GPT-5.4-mini, GPT-5.3-Codex, and GPT-5.3-Codex-Spark. You can also use your preferred model from any provider as long as it supports the OpenAI Responses API.

Pricing. Codex is available across ChatGPT's existing subscription plans rather than as a standalone product, with pricing starting at $20/month for the Plus plan.

Lovable: Browser-based AI app builder for turning ideas into working products

Lovable is a web-based platform that lets you build apps and websites by describing what you want in plain language. It is widely loved by non-technical audiences because it is much easier to get started with compared to other tools reviewed here, requiring little to no technical know-how.

Instead of working in a terminal or IDE, everything happens in the browser through a simple chat interface, making it accessible to founders, product managers, designers, and anyone who wants to go from idea to working product without writing a single line of code.

Core features. Here’s a breakdown of Lovable’s main features.

• Built-in code editor: Even though Lovable is built around a chat interface, you can still view and manually edit your project's source code directly inside the platform without switching tools or exporting anything.
• Workspace and project knowledge: Define instructions once and Lovable carries them across sessions automatically. Workspace knowledge sets shared rules in all your projects, like coding standards and preferred libraries, while project knowledge adds context specific to a single project, like the app's purpose, database schema, and architecture decisions.
• Google auth: Lovable supports Sign in with Google out of the box, letting users authenticate with their Google accounts instead of creating a password. There’s a managed option where Lovable handles the OAuth setup, and a bring-your-own-credentials option for teams that need full control.
• Custom email sending: Lovable lets you send branded transactional emails from your own domain, covering both authentication emails like password resets, signups, and magic links, and app emails like order confirmations and shipping notifications. Lovable handles all the technical setup automatically, so you do not need an external email provider like SendGrid or Resend.
• Design systems: Lovable lets you define your component library, styling guidelines, and setup instructions once and apply them consistently in every project in your workspace.
• Visual edits: A no-code visual editor that lets you click on any element in the live preview and update layouts, text, colors, fonts, and images directly without writing code or using credits.
• One-click deployment: When your project is ready, you can publish it to a live URL with one click. Each publish deploys a snapshot of the current version, and updates only go live when you explicitly republish. You can customize the URL subdomain, add a custom domain, and configure site metadata like the favicon, page title, and social sharing image.
• Project analytics: There is a built-in analytics dashboard that gives you real-time insights into how your published app is performing, including visitors, pageviews, bounce rate, visit duration, traffic sources, device usage, and which pages are getting the most attention.

Model support. Unlike other tools we’ve covered, Lovable does not let you choose which AI model to use. Model selection is handled internally, with Lovable automatically routing to what it considers the best option for tasks. That said, the team actively updates the lineup to ensure users always benefit from the latest and most capable models available.

Pricing. Livable offers a free tier and its paid plans start at $29/month. There’s also a 50% discount for students.

Amazon Q Developer (CodeWhisperer): AWS's AI coding assistant built for the full development lifecycle

Amazon Q Developer is Amazon Web Services' AI-powered coding assistant that’s available as an IDE extension and a CLI tool for terminal interactions.

A major edge of Amazon Q Developer is its deep integration with the AWS ecosystem. It is built into the AWS Management Console, where it can help optimize cloud costs, review architecture decisions, investigate operational incidents, and troubleshoot networking issues.

Here are some features.

• AWS Console-to-Code: Lets you record actions you take in the AWS Management Console and automatically convert them into reusable code. If you launch an EC2 instance, Console-to-Code generates the equivalent AWS CLI commands, CloudFormation templates, or AWS CDK code.
• Infrastructure-as-Code (IaC) generation: Produces deployment-ready infrastructure code for you. Describe what your application needs and Q Developer outputs the corresponding CloudFormation templates, AWS CDK code, or Terraform configurations.
• Workload transformation: Handles tedious modernization and technical debt work that engineering teams typically spend weeks on. It can upgrade Java applications and port .NET Framework applications to a cross-platform .NET.

Pricing. Free tier is available and paid plans start at $19/month.

Model support. Amazon Q Developer doesn't let you choose models, but under the hood, it uses multiple foundation models, automatically routing tasks to the model best suited for it.

Tabnine: Enterprise-focused AI coding platform built around security, compliance, and organizational context

While other tools are primarily focused on developer experience and speed, Tabnine is tailored to making AI coding reliable and safe for enterprises, particularly those operating in regulated, mission-critical, or highly secure environments. It is available as an IDE plugin and as a CLI for terminal-based agentic workflows.

Core features. Here’s an overview of the main capabilities Tabnine offers.

• Context engine: Rather than working only with files available locally in your editor/PC, this indexes your connected remote repositories and builds structured, richer, organization-wide context from them.
• Tabnine agent: Works autonomously to achieve a specified goal. It can handle tasks like codebase-wide refactoring, automated test generation, documentation synthesis, and policy validation, all while breaking down complex work into steps and explaining its reasoning along the way.
• Testing agent: Automates test creation by analyzing your code, generating test plans, describing test cases, and producing test files.

Model support. Tabnine gives you access to 21 models in total: 20 third-party models from 6 providers, including Anthropic, OpenAI, Google, Mistral, MiniMax, and Zhipu AI, plus its own proprietary model.

Pricing. Paid plans start at $39/month.

Windsurf: AI-native code editor

Windsurf, formerly known as Codeium, is an AI coding tool that comes in two variants: The older one is a set of plugins and the new one, called the Windsurf Editor, is a standalone IDE powered by AI that can collaborate with you like a copilot and complete tasks independently.

It’s another instrument that AltexSoft engineers actively use. “Windsurf is a fantastic IDE that supports AI-driven autocompletion, inline editing, multifile editing, chatting with your codebase, and agent workflows. It’s comparatively simple, intuitive, and has a polished UI,” says Glib.

Core features. These are its main capabilities.

• Cascade: An agentic AI that tracks everything happening in your session, including file edits, terminal commands, clipboard activity, and conversation history, to understand what you are working on without you having to re-explain it each time. This flow awareness means you can ask it to “continue my work,” and it picks up where things left off based on what it has observed.
• Windsurf Previews: A built-in browser inside the IDE that lets you view a live preview of your web app without leaving the editor. You can click on elements or errors directly in the preview and send them to Cascade as context, so you can point at exactly what needs fixing rather than trying to describe it.
• App deploys: Lets you deploy web apps directly from Windsurf through Cascade without leaving the editor. You just ask Cascade to deploy your project and it sends the build and deployment to a public URL.
• Workflows: Reusable, step-by-step instructions you define as markdown files to guide Cascade through repetitive tasks.

Model support. Windsurf gives you access to over 100 models from 6 providers: its own proprietary SWE model family, Anthropic, OpenAI, Google, xAI, and Moonshot AI.

Pricing. Windsurf has a free plan and its paid plans start at $20/month.