Codex 2.0 vs Cursor vs Copilot CLI vs Opencode: Which Wins?

Every developer knows this moment. You're debugging an authentication bug that's been breaking user logins intermittently. The code spans eight microservices. Each service connects to databases differently. Some use OAuth directly, others route through custom handlers. The business logic exists partly in code, partly in configuration files, and partly in senior engineers' heads.

You need help. So you turn to AI coding tools. Which one should you choose?

Most people compare these tools wrong. They look at features. Context windows. Pricing. IDE integration. How many tokens can each tool process? Which has the slickest interface?

Here's what they miss: the question isn't which tool has better features. It's which tool understands what you've already built.

This matters because there's a fundamental difference between AI that generates generic code and AI that understands your specific codebase. One helps you build new things. The other helps you work with the complex systems you're stuck maintaining.

Most AI tools fall into the first category. They're great at writing hello world applications. They struggle with the messy, interconnected reality of enterprise software.

The Thing Everyone Gets Wrong

When people evaluate AI-assisted coding tools, they focus on capabilities. Can it write functions? Can it explain code? Can it integrate with my editor?

But here's what's counterintuitive: better capabilities don't always mean better results when you're working with complex existing systems.

Think about it this way. You wouldn't hire a contractor to renovate your house based solely on how fast they can build new walls. You'd want someone who understands why your current walls are where they are, which ones are load-bearing, and what will happen if you move them.

Same thing with code. The most sophisticated AI code generator is useless if it doesn't understand why your authentication service connects to three different databases or why your payment processor has those weird timeout settings.

Yet most tool comparisons ignore this entirely. They assume all codebases are the same. They test on clean examples instead of the sprawling, historically-grown systems most developers actually work with.

What These Tools Actually Do

GitHub Copilot CLI works as a terminal companion. It has a 64k token context window and integrates with GitHub workflows. You install it with gh extension install github/gh-copilot. It's good at generating shell commands from natural language.

Cursor positions itself as an AI-first editor built on VS Code. It supports context windows up to 200k tokens and multiple AI models. The interface lets you select code and describe changes without switching contexts.

Codex CLI from OpenAI provides a 192k token context window and runs locally. But it's undergoing a major rewrite from Node.js to Rust, which creates uncertainty about stability.

Opencode is an open-source terminal agent that supports multiple AI models but has limited documentation.

All of these tools can generate code. None of them understand why your code works the way it does.

Why Context Windows Don't Matter

Here's where most comparisons go wrong. They obsess over context window size. How many tokens can each tool process? Cursor gets 200k tokens. Copilot CLI gets 64k. Codex gets 192k.

These numbers sound important. More context should be better, right?

But context window size misses the real problem. It's not about how much code you can feed to the AI. It's about whether the AI understands what the code is trying to accomplish.

You can give an AI 200,000 tokens of your authentication service code. It'll process every line. But if it doesn't understand that the service connects to three databases for historical business reasons, or that certain timeout values prevent abuse patterns, or that some "inefficient" queries are actually rate limiting mechanisms, then all that context is useless.

It's like showing someone a detailed map of your city but not explaining which neighborhoods to avoid or why certain roads are closed during rush hour. They have all the information but none of the understanding.

This is why developer communities show no consensus on which tool works better. Tools with smaller context windows sometimes produce better results than tools with larger ones. Understanding beats information every time.

The Authentication Test

Want to see the difference? Try this test with any AI coding tool. Pick your most complex authentication system. The one that connects to multiple databases, has client-specific customizations, and contains business logic that exists nowhere in documentation.

Ask the tool to suggest improvements to the login flow. See what happens.

Generic tools will suggest clean, modern authentication patterns. OAuth 2.0 with JWT tokens. Proper separation of concerns. Well-structured error handling. The suggestions will look professional and follow current best practices.

They'll also break everything.

The tools don't know that your legacy mobile app depends on specific session format. They don't know that changing timeout values will trigger compliance violations. They don't know that your "poorly structured" authentication code actually handles edge cases that took years to discover.

Context-aware tools approach this differently. Before suggesting improvements, they try to understand why the current system works the way it does. What constraints shaped these decisions? What would break if certain patterns changed?

This understanding transforms suggestions from dangerous to useful.

Where Each Tool Breaks Down

GitHub Copilot CLI excels at terminal workflows. Commands like gh copilot suggest generate commands from natural language, while gh copilot explain explains existing commands. It's genuinely useful for command-line tasks.

But when you ask it about complex codebase changes, it treats your code like generic programming problems. It doesn't understand your architectural decisions or business constraints.

Cursor's AI-first approach integrates assistance directly into editing through Ctrl+K interfaces. You can select code and describe desired changes. The large context window means it can process more of your codebase simultaneously.

Yet community feedback suggests mixed results. Some developers report "much lower success rate than Github Copilot's" during extended trials. Processing more code doesn't help if the tool doesn't understand why the code exists.

Codex CLI offers extensive configuration and multiple model support. But the ongoing Rust rewrite means you're betting on experimental technology. And like the other tools, it treats codebases as generic programming exercises.

Opencode remains too experimental for serious evaluation.

The pattern is clear. These tools excel at code generation but struggle with code understanding.

The Real Comparison

Instead of comparing features, compare understanding. Which tool can explain why your systems work the way they do?

Can it tell you why your payment service uses message queues instead of direct API calls? Can it explain why certain services still use deprecated libraries? Can it predict what will break if you change session handling?

Most tools can't answer these questions because they don't understand the constraints and decisions that shaped your codebase. They see code as text to be processed rather than solutions to specific business problems.

This creates a false sense of progress. The AI gets better at generating code while remaining ignorant of the systems it's supposed to improve.

What Actually Matters

Pricing matters to some extent. GitHub Copilot costs $10/month for individuals, $19/user/month for businesses. Cursor pricing starts at $20/month for Pro plans. Opencode is open-source but requires self-hosting.

IDE integration affects daily workflows. Cursor's VS Code foundation provides familiar interfaces. Copilot CLI works well in terminal environments. Each tool fits different development styles.

But these factors become secondary when you're dealing with complex existing systems. The best IDE integration in the world doesn't help if the AI suggests changes that break production systems.

The fundamental question remains: does the tool understand your architecture?

The Missing Piece

Most AI coding tools assume you're building new things. They're optimized for greenfield development where you can follow current best practices and avoid legacy constraints.

Enterprise development is different. You're working with systems built over years by different teams solving different problems. The authentication service connects to multiple databases because of an acquisition three years ago. The payment processor has weird timeout settings because of fraud patterns discovered during Black Friday 2019. The user management service routes through a message queue because direct database access couldn't handle traffic spikes.

This context isn't documented. It exists in institutional memory, accumulated debugging sessions, and hard-learned lessons about what works in production.

AI tools that understand this context can suggest improvements that build on existing constraints rather than ignoring them. They can recommend refactoring strategies that preserve business logic while improving maintainability.

Tools that don't understand context generate clean code that looks right but breaks existing integrations.

The Broader Pattern

This problem extends beyond AI coding tools. It shows up everywhere new technology meets existing systems.

Organizations adopt new tools based on feature lists and demos. The tools work great on clean examples. Then they try to integrate with existing systems and discover that features don't matter if the tool doesn't understand what already exists.

The companies that succeed with new technology focus on integration rather than capabilities. They choose tools that work with their existing constraints rather than tools that ignore them.

This applies to AI tools, but it also applies to databases, frameworks, cloud services, and every other technology decision. Understanding your existing systems matters more than adopting the latest features.

What You Should Actually Test

Before choosing any AI coding tool, test it on your most complex legacy system. The one everyone's afraid to touch. The authentication service with three database connections. The payment processor with client-specific edge cases. The user management system with accumulated business rules.

Ask the tool to explain why these systems work the way they do. Can it identify the constraints that shaped current implementations? Can it suggest improvements that preserve existing functionality?

Tools that understand your architecture will help regardless of their feature lists. Tools that don't understand will generate problems regardless of their capabilities.

The choice isn't between Copilot CLI, Cursor, Codex, or Opencode. It's between tools that understand your systems and tools that generate generic code without comprehending what they're changing.

For teams managing complex existing systems, understanding beats features every time.

Context understanding isn't just a nice-to-have feature. It's the foundation that makes AI assistance valuable instead of dangerous. The future belongs to tools that understand what you've built, not tools that generate what you might build.

Ready to test whether AI actually understands your codebase? Augment Code focuses on understanding existing enterprise architectures before suggesting changes. Try it on your most complex system and see the difference between AI that understands your constraints versus AI that ignores them for the sake of development productivity.