AI Agents for Developers: Which Tool Works Best in Real Projects?

AI Coding Assistants: A Practical Comparison

It is difficult to overlook the growing prominence of artificial intelligence and the way it has reshaped our everyday workflows.

The appeal of fast, nearly effortless access to information has captured widespread attention. Meanwhile, developers continue to refine their tools, pushing for greater accuracy and smoother user experiences.

This has sparked an intense race among AI providers, each eager to win over users with their offerings.

Developers want to harness the power of AI, yet the sheer number of choices makes it increasingly challenging to decide.

Beyond simple code suggestions, modern AI tools are evolving into something more akin to an assistant that understands context, proposes solutions, and guides developers through entire software development cycles.

These are the AI agents we will examine here.

This piece outlines the agents currently on the market and draws on the hands-on experience of our team at House of Angular to compare their performance in real-world scenarios.

Our goal is to help you see what sets each option apart.

Leading AI agents and their typical use cases

AI agents are designed to go beyond simple code generation. They analyze the broader context of a project, propose architectural decisions, and execute concrete actions. Notable examples include Claude Code, Cursor, and GitHub Copilot.
Typical tasks where these tools shine:

• creating repetitive or foundational code
• composing unit and integration tests
• restructuring and improving existing code
• identifying issues and explaining errors
• building quick prototypes to test an idea

In effect, they act as a sort of junior development partner, boosting productivity while still requiring careful oversight and validation.

Cursor

The main advantage here is its deep understanding of an entire codebase. It can correlate changes across multiple files, making it highly effective for large-scale modifications. Cursor comes closest to a fully integrated AI development environment.

Claude

This tool is less about editing and more about dialogue and analysis. It excels at examining problems, offering clear explanations, and decomposing complex requirements into manageable steps. Many developers use Claude to think through an approach before writing any actual code.

GitHub Copilot

Copilot is a real-time coding companion that operates within the editor. It is ideal for contextual auto-completions, filling out function bodies, and handling routine line-by-line coding. It offers the least autonomy but provides the most immediate, inline assistance.

Codex

This is the epitome of a task-driven agent. Developers can assign larger, more comprehensive objectives—from implementing a feature to overhauling a module. Codex acts as a focused executor, translating commands into code, rather than just making suggestions.

What our developers ended up with

We gathered quick retrospectives from the team: which AI agent they originally picked, what drove that decision, and what they rely on now.

The answers reveal how tool selection actually plays out on the ground and which factors end up mattering most.

What was your initial pick for an AI agent, and why did you go with it?

• I started with GitHub Copilot — the deciding factors were flexibility, the option to swap between different models (in practice, I usually landed on Claude Sonnet 4.6), seamless IDE integration through a plugin, and the cost: $100 per year for Copilot versus $200 for Claude, or $10 and $20 per month respectively (in both cases, the annual plan effectively gives you two months for free).
• GitHub Copilot — at the time, inline code suggestions right inside the IDE were what won me over.
• GitHub Copilot — it was simply the first widely adopted tool for AI-assisted programming. Back then it had no agentic features, but even basic autocomplete and the concept of pair programming with a model made a real difference to everyday workflow.
• It began with GitHub Copilot — the inline suggestions were convincing, but as the IDE plugin matured, I found myself using its built-in agent more and more.
• GitHub Copilot — solid JetBrains integration, with code generation and autocomplete bundled together.
• GitHub Copilot — mainly because it worked with WebStorm (I couldn't switch to VS Code). At the time, intelligent completion alone was impressive and genuinely accelerated daily work… until I first tried Cursor.
• Initially, I evaluated Copilot and ChatGPT. Copilot seemed fine for code suggestions, but after a while it became noticeably slow (possibly an IDE problem), and I eventually dropped it — writing code manually was quicker than waiting for a hint. I picked ChatGPT mainly because of its name recognition and the general buzz around it.
  • Interestingly, when I briefly lost faith in WebStorm and moved to VS Code, Copilot behaved COMPLETELY differently. Far better, far faster. Honestly, if it performed like that in WebStorm, I might have stuck with it.

• My first choice was ChatGPT. It could walk me through how a piece of code or a function worked, or draft a simple method. For daily work at that point, that was enough.
• The starting point was GitHub Copilot. That initial spark of AI enthusiasm. Although it made plenty of mistakes, it still provided solid help with routine tasks. For that era, it was impressive. As newer tools appeared, I stopped using it entirely.

Which tools do you reach for most these days, and in what scenarios?

• Right now, Claude Code is my go-to for tedious, repetitive work like syntax updates, test writing, and generating test mocks.
• Claude Code — daily tasks, refactoring, planning and building larger features, writing tests.
• These days, Claude Code is my primary tool — mostly for repetitive tasks, refactoring support, and tests. I also appreciate its planning mode, particularly for trickier assignments and for analysing, reviewing, and summarising code. Copilot is still around, but I use it mainly as autocomplete or for quick, local edits and simple questions.
• Claude Code — I use it practically every day whenever there's a sensible opening. I try to delegate all repetitive work or features that resemble existing ones. For uncharted territory, I often ask for its perspective, and if the model demonstrates it understands the problem well, I hand over the implementation too.
• Cursor — implementing or tweaking smaller features, writing unit tests or Storybook stories, improving component accessibility. For bigger jobs, I lay out the architecture and per-file implementation plan for the agent, then refine the result to match project standards. In personal projects, I use Codex.
• Cursor and Claude Code IDE — I turn to Cursor when I already have a clear flow in mind, since it reads intention very well (TAB-driven development). Claude Code is reserved for harder problems. In my view, it handles larger context more gracefully than Cursor. For complex tasks I use the Opus model, even though the same model is available in Cursor. Claude's output simply comes out better.
• Mostly Claude (Sonnet given Opus's price) plus Codex.
  I initially reached for Codex because I already paid for ChatGPT — and early on it was genuinely solid, especially for code generation. Clear instructions get the job done, and it handles unit tests particularly well.
  • Later, out of curiosity, I compared it with Claude. Today, when I do use AI, roughly 95% of the time it's Claude.
  • My usual use cases: boilerplate generation, unit tests, and occasionally trickier issues along the lines of “ doesn't work, should behave this way. Fix it.”

• At the moment, I use Codex. It comes with my ChatGPT Plus plan. It's been a big help with large-scale migrations and creating Storybook stories.
• For quite a while, my personal favorite has been Claude Code — a tool that genuinely set trends and established workflows for AI-assisted development. I default to Sonnet for fast prototyping, refactoring, optimising individual code sections, and tests. For more demanding work, planning mode shines, and its effectiveness can be raised further by switching to Opus. I maintain a sizable collection of side projects where I test all sorts of tools. For design work on those, I use Stitch. From time to time I check Antigravity to see where Google's models stand. Since new LLMs keep appearing, I use OpenRouter for benchmarking — it offers convenient access to most top models. Recently, I've also been looking into Open Code, an open alternative to Claude Code that allows swapping in models beyond Anthropic's offerings.

How does this tool hold up in your day-to-day workflow?

[Claude]
For the scenarios I described, it noticeably boosts my productivity. That said, I haven't yet pushed it toward more "ambitious" or complex work.

[Claude]
I see Claude Code as a daily work companion. It grasps project context well and manages more complex tasks effectively. Through skills (https://angular.dev/ai/agent-skills), it pairs quite well with the latest Angular, though I still occasionally need to hand-correct the code it produces.

On a regular basis, I stick with the Sonnet model — Opus would burn through the Pro plan's token allowance too quickly, while Sonnet keeps me working most of the day without hitting that ceiling.

[Claude]
I view Claude Code as reliable daily support that helps improve efficiency and reduce mistakes. The outcome depends heavily on how well you master the tool (kudos to Anthropic's documentation) and how skilfully you apply it — proper configuration (and there's plenty of it), supporting tooling, context provision, or simply crafting good prompts. Results vary by task type, so it pays to draw conclusions and adapt your approach.

On context limits and token efficiency — it would be lovely to always run at full capacity, but in practice I had to find my own balance. Picking the right model helps — I mostly use Sonnet and Opus, occasionally Haiku — along with more deliberate context management.

On the flip side: new features arrive quickly, but in my opinion, the pace is almost too rapid. I'm also wary of Anthropic's quietly changing tool and model parameters, which are easy to overlook yet do affect capabilities, stability, and overall experience.

[Claude]
It's become indispensable for quick prototyping, generating ideas, and staying on top of repetitive work. When it comes to new functionality, you still need to judge when a task suits full agent-driven generation and when it's better to rely on it only partially. With thoughtful task selection and instructions, substantial time savings are possible.

Even so, you can't expect it to do everything autonomously — ongoing oversight and corrections are necessary. One daily annoyance is the quality of IDE integration: not only is it sparse, it also doesn't always behave as expected.

[Cursor]
For demanding assignments, I lean on the Sonnet 4.6 model, while smaller tasks go to the Composer model. I'm pleased with the quality of generated code. The price is justified — it brings agent integration, excellent UX, and autocomplete that, in my view, outpaces the competition by a wide margin. The main drawback is token limits, which can feel constraining — though that's true of any AI tool with monthly caps.

[Cursor and Claude Code IDE]
In everyday use, both tools accelerate development and grasp context very well — but token costs add up quickly for larger undertakings, so breaking work into phases is worthwhile.

[Claude and Codex]
Claude generally performs best when you specify exactly what, where, and how to do it. Without that guidance, it can veer off course.

The output usually still warrants review, because sometimes what could be done in two lines turns into dozens.

Even so, it undeniably speeds things up.

The standout strengths:

• handles console/terminal errors exceptionally well (e.g., during migrations)
• very strong at refactoring
• unit tests are a complete game-changer

Writing meaningful tests used to be so time-consuming that they were occasionally skipped. Now, with AI, it feels almost negligent not to write them, since it takes only a moment.

[Codex]
Measured against ChatGPT, it's a game-changer, chiefly because it sees the project context and adapts to the existing code style. However, during extended repetitive stretches, it tends to "drift" and lose the thread, requiring you to restate what it should do, where, and how.

Codex hooks into VS Code, but I'm a WebStorm devotee, so I run Codex in VS Code while actually coding in WebStorm.

[Claude]
Claude Code genuinely elevates a developer's work. Not just for routine tasks, but also as assistance in crafting advanced code. Still, the tool demands some adapting and practice, and the codebase needs to be primed for AI collaboration to unlock its full potential. The breadth of features and commands is plenty for me, and I find it satisfactory. That's not to say it's without faults. CLI-based tools carry certain limitations — like the absence of a convenient way to reference multiple specific lines or code sections at once, which would be trivial in an IDE-based tool.

What does this tool do better than its predecessors?

[Claude]
A broad set of configuration options (agents, skills, etc.), superior reasoning and context retention, and more fine-grained usage limits (per session/week versus per month with Copilot).

[Claude]
Above all, a markedly better grasp of context.

[Claude]
Honestly, coming from UI/IDE-based AI tools, I had doubts about moving to the terminal. Yet despite the usual CLI constraints, it quickly became my preferred interface. The real value lies in the maturity of the ecosystem, the customizability, and the quality of output you can achieve. In my experience, even pairing Anthropic models with other harnesses yielded far weaker results — which suggests both the Claude models and the harness itself deserve credit.

[Claude]
Excellent context comprehension, highly advanced tooling, and configuration flexibility.

[Cursor]
Cursor's Composer model rapidly edits and generates boilerplate files. The UX is stronger than CLI tools with IDE add-ons. Beyond agent capabilities, Cursor offers remarkable autocomplete that almost magically anticipates my next move, which is especially handy when polishing AI output during review. In my opinion, it currently strikes the best balance between agent-based generation and direct IDE integration.

[Cursor and Claude Code IDE]
Relative to the Copilot version I started with, it delivers better context understanding and more refined code.

[Claude and Codex]
Claude tackles more intricate issues far better than Codex — it "thinks more broadly," but that also means it consumes more tokens.

Codex, in contrast, is more direct — it overthinks less and knocks out straightforward tasks faster, such as:

• code generation
• tests
• simple refactoring

Example: I once had a simple task — merging two states into one (some prop duplication). At some stage, Claude began overengineering things with providers and burned ~20% of the token budget over the next 5 hours.

Codex finished the job in a few minutes — cheaper and quicker.

On the other hand, for bugs or more complex situations, Codex falls short.

[Codex]
It has visibility into the project context and produces solutions suited to the specific case. It's well suited to simple and repetitive refactoring / migrations / test writing.

[Claude]
I think the Claude "ecosystem" itself does a fine job. The CLI can easily be swapped for something like Open Code. Native support for the best available models is the key ingredient. Anthropic keeps expanding its portfolio with new tools, which directly translates into work quality and satisfaction. Examples include Remote Control for remote terminal management or the recently introduced Claude Design. By subscribing to any package, you gain access not just to the most capable models but also to useful extras.

Would you trade this tool for another one?

[Claude]
A wide set of configuration options (agents, skills, etc.), superior reasoning and context retention, more granular usage limits (per session/week versus per month with Copilot).

[Claude]
Right now, I don't see an alternative.

[Claude]
I don't feel the urge to switch just now, but things move so fast — never say never.

[Claude]
As far as I'm aware, nothing better exists on the market today, and marginal differences aren't worth the effort of switching. Until there's a clear consensus that another tool holds a decisive advantage, I see no reason to move.

[Cursor]
Among agentic IDEs, I've tried Antigravity, but Cursor seems stronger. Terminal tools like Codex and Claude Code are impressive, but they lack the better UX for quick single-file edits. I probably wouldn't switch, though I might add Claude Code alongside to conserve tokens in Cursor when building larger features from scratch.

[Cursor and Claude Code IDE]
My ideal arrangement would blend WebStorm's interface and ergonomics, Cursor's autocomplete and speed, and Claude Code's context understanding and code quality.

[Claude and Codex]
Not at the moment.

[Codex]
I've heard plenty of opinions that Claude is markedly better, and its subscription cost is quite comparable. I think it's worth a try.

[Claude]
I'm not especially loyal to Claude. If something better emerges that meets my needs, I'd probably switch. Right now, nothing on the horizon fits that bill.

Any other thoughts or experiences to share? Advice for those still hunting for the right agent?

• I'd avoid an annual subscription to any single tool. Given how quickly AI evolves, far better options could appear within a year — and that money would be lost.
• Keep an eye on the AI landscape, but don't get swept up in it — there's so much hype and FOMO in this space that it's easy to lose perspective. I think it's wiser to find what boosts your productivity and comfort, then stick with that for a while. That gives you time to truly learn the tool and refine how you use it — and much of that knowledge transfers regardless of what you eventually move to.
• In my view, hopping between tools is rarely worthwhile. At their core, they usually offer similar capabilities, and the real difference lies in how effectively we harness them. Time is better spent on learning and proper configuration than on endless comparison — only then can you fairly judge whether another tool actually offers more.

• Set up your repository so the agent gets feedback on its output — skills, MCP, Husky, ESLint, NX generators, and tests give agents insight into what they're doing wrong and how to improve the generated code, ensuring it's high-quality and meets business needs. When picking a tool, look beyond code generation to the whole ecosystem. Features like pull request reviews or bug detection can surface issues before you submit code for team review, saving time for everyone.
• It depends heavily on how someone intends to use AI and what they prefer — the best approach is simply to try a few options and see what clicks.
  • Also worth exploring are the new prototyping tools (rivals to Figma). For instance, Stitch from Google or the new Claude Design could shift the landscape significantly. I've tried Stitch and came away impressed. Claude will likely push even further.
• Test different tools yourself and find which one you work best with. Picking a tool is just the beginning — what follows is configuration and building an agentic workflow tailored to you. Staying current is important, as the industry keeps moving — new models, patterns, and standards appear constantly. Not long ago, LinkedIn was full of predictions that prompt engineers would dominate the IT world and take our jobs. Who's writing prompts these days?

Main lessons learned

After collecting input from our team of developers, a few clear themes emerged:

• GitHub Copilot was the starting point for most developers – it is widely available, reasonably priced, integrates smoothly with common editors, and supports a pair-programming style of interaction
• Claude Code is now the most widely adopted agent – it demonstrates strong project awareness, can tackle larger portions of the codebase, performs code analysis, and handles complex challenges with good results
• Typical usage patterns include: scaffolding boilerplate code, writing unit tests, refactoring, handling migrations, and debugging errors
• The specific tool matters less than how it is used – providing adequate context, writing clear prompts, decomposing problems into smaller pieces, and maintaining a well-structured repository are key factors

However, these agents come with their own limitations. Developers highlighted several issues:

• the necessity of reviewing and validating generated code
• models sometimes lose track of context or propose unnecessarily complicated solutions
• token limits that constrain the scope of work
• command-line interfaces can feel less convenient than IDE-integrated tools

Most developers express satisfaction with their current choices, particularly with Claude Code, which is regarded as one of the most well-rounded options available today.

As for advice to others, several recommendations come up repeatedly:

• avoid switching between tools too often; pick one, learn it thoroughly, and refine your own workflow
• given the pace of AI evolution, long-term paid plans may not be the best commitment
• set up your repository for AI-assisted work, as it improves agent performance (with ESLint, tests, CI pipelines, code generators, and a solid feedback loop)
• experiment with different working styles (CLI / IDE / mixed)
• keep in mind that these agents are meant to boost productivity, not replace the developer entirely

Side-by-side overview of agents

Feature	Claude Code	GitHub Copilot	Cursor	Codex
Main advantage	Excellent context understanding, planning, code quality	Simple and fast autocomplete in the IDE	Best UX + very accurate suggestions (flow)	Speed and precision in simple tasks
Biggest drawback	Token limits, CLI less convenient than IDE	Weaker context understanding, can be slow	Token consumption, sometimes loses context with large tasks	Handles complex problems worse
Best use case	Refactoring, tests, complex features	Code suggestions, quick changes	Code editing, smaller features, “on-the-fly” work	Tests, migrations, boilerplate
Work style	Agent / task delegation	Autocomplete / inline support	Interactive development in IDE	Task-based (short commands)
For whom?	Developers working with larger context and architecture	Everyone – as support for writing code	People who value speed and UX	For simple tasks and automation
Level of control	High (requires guiding the model well)	Low (works in the background)	Medium (interaction + suggestions)	Medium (specific commands)

Pricing comparison for agents (April 2026)

	Claude Code	GitHub Copilot	Cursor	Codex
Base price	~20$/month (Pro)	~10$/month	~20$/month	~20$/month
Higher-tier plans	100–200$/month	~19$/month. (business)	60$ / 200$+ (Pro+/Ultra)	pay-as-you-go + Plus
Pricing model	Subscription + limits	Flat price	Subscription + overage	Subscription + usage
Additional costs	tokens with high usage	none (flat)	yes (compute/tokens)	yes (API usage)
Free tier	limited	reasonable free tier	limited	dependent on ChatGPT
Cost predictability	medium	very high	low	low

Entry-level: Copilot

• around $10 per month
• no hidden or extra charges
• costs are stable and predictable
  
  → A great starting point for beginners

Mid-range: Claude / Cursor / Codex

• subscriptions begin near $20 per month, with caveats:
  • Claude → limited token allowances
  • Cursor → additional fees may apply beyond the plan
  • Codex → cost often scales with usage
    
    → The real expense varies based on how heavily you use it

High-usage situations (heavy reliance)

• Claude: expenses can climb to $100–200 per month
• Cursor: experienced users may see significant additional charges
• team setups: monthly costs can exceed $1000
  
  → Regular, intensive use makes AI tools a considerable expense

How pricing models differ

Copilot (most straightforward)

• a flat monthly rate
• no need to track token consumption
• less flexibility, but also no surprises

Claude / Codex (cloud-oriented approach)

• cost is tied to both features and actual consumption
• greater capabilities, but you must:
  • stay mindful of context usage
  • watch your budget carefully

Cursor (mixed model)

• base subscription with possible surcharges
• user experience is very polished
  but:
  • the final cost can be hard to anticipate

Wrap-up

The findings make it evident that AI has moved beyond being a novelty and is now an integral part of everyday development work. The journey from basic autocomplete tools like GitHub Copilot to sophisticated agents such as Claude Code or Cursor has transformed our coding practices.

The focus is no longer solely on writing individual lines faster, but rather on:

• assigning whole tasks to agents
• dealing with expansive project contexts
• getting assistance with technical choices

At the same time, there is no single ultimate solution. Each agent serves a different purpose:

• certain tools shine during rapid coding sessions,
• others are better suited for in-depth analysis and refactoring,
• some simply provide a smooth editing experience within the IDE.

Still, the most significant takeaway holds firm – the tool itself is secondary to how you employ it.

People who get the most out of these agents:

• select the appropriate tool for each assignment
• are proficient at explaining the issue at hand
• actively review and validate the output
• continuously improve their own processes

Entry-level pricing remains accessible across all these products, but their structures differ:

• GitHub Copilot → roughly $10 per month, straightforward and predictable
• Claude Code → approximately $20 per month, but with usage limits and pricier tiers
• Cursor → about $20 per month, frequently with supplemental fees
• Codex → depends on your usage patterns (API charges or subscription)

In practice:

• the most affordable option is not necessarily the most economical in the long run
• expenses grow along with how often and how much you use the tool
• many developers manage their budget by mixing different tools

One final point: the AI landscape is changing at a remarkable speed. What is current today may quickly become just one of numerous choices a few months from now.

Rather than chasing a perfect all-in-one product, developers should:

• concentrate on learning how to work productively with AI
• develop a personal workflow that suits their needs
• view AI as an aid, not a substitute

Above all, it remains the developer's duty to uphold quality, maintain architecture, and make the final calls.

We appreciate the contributions of my colleagues at House of Angular for sharing their practical knowledge and perspectives on using AI.

Thanks to them, our readers gain access to real-world insights, encounter different approaches, and can extract relevant and valuable guidance.