By: Agustin Repetto, Developer at Urudata Software
Artificial intelligence for software development is no longer limited to autocompleting lines of code or suggesting small code snippets. In just a few years, these tools evolved from writing assistants into systems capable of analyzing repositories, interpreting errors, modifying files, executing commands, and supporting complete programming tasks.
In the latest edition of Urudata Software’s Tech Talks, we presented a session comparing three tools that are highly present in today’s ecosystem: Cursor, Claude and Codex.
There, we analyzed these tools from a practical perspective: how they integrate into daily work, what types of prompts work best, how they handle context, what permissions they require, and what precautions are worth considering in terms of security, privacy and costs.
In the following article, we take the comparison further, not with the goal of deciding which one “generates better code”, but to understand which part of the workflow each tool improves and in which situations it makes sense to use them.
From code assistants to workflow tools
Not long ago, when people talked about AI for programming, the main reference point was code assistants that completed a line, suggested a function or helped with syntax. That was useful, but the reasoning still fell almost entirely on the developer’s plate.
The developer was still the one who had to:
- understand the problem;
- find the relevant code;
- decide what to change;
- implement the solution;
- run tests;
- validate that everything worked.
Today, the landscape is different. Current tools can intervene in several stages of the process: analysis, planning, editing, validation and review. That is why the most useful question is no longer simply “which tool writes better code?”, but “which part of the workflow does each tool optimize?”
Cursor: AI integrated into the IDE
Cursor is the closest to a developer’s traditional workflow because it functions as a code editor with integrated AI. Its main advantage is that it avoids the constant context switching between an external page and the project.
Instead of copying code, pasting it into a chat and returning to the IDE, Cursor allows developers to work directly on the repository. The developer can select a function, request a modification, review the diff and accept or reject changes without leaving the programming environment.
When does it make sense to use Cursor?
Cursor makes a lot of sense when the developer already knows where they are working and needs to move quickly on concrete code. For example:
- fixing a specific function;
- simplifying existing logic;
- adding validations;
- generating tests for a method;
- reviewing where a function is used;
- applying small changes across several related files.
Among its most representative capabilities are Tab, Inline Edit, contextual Chat, Agent Mode, Rules and support for integrations such as MCP. Cursor’s official documentation presents these functions as part of its proposal for an AI editor with agents, rules and connections to external tools: Cursor Docs.
One especially useful feature in real projects is Rules, because it allows teams to define conventions: coding style, architecture, restrictions, patterns or practices that the AI should follow. This is key because, in a professional environment, it is not enough for code to work; it must also align with the project’s way of working.
Cursor supports developers while they code. Its greatest value lies in integrated editing, immediate IDE context and visual review of changes.
Claude: reasoning, analysis and long context
Claude takes a different approach. Although it can also generate code, its greatest value appears when the problem is not yet fully defined or when reasoning is needed before implementation.
It is especially useful for analyzing complex bugs, reviewing architecture, interpreting extensive documentation, comparing technical alternatives or understanding flows involving several system layers.
When does it make sense to use Claude?
Claude works well when we need to think before touching the code. For example:
- analyzing long logs;
- understanding a business flow;
- reviewing architectural decisions;
- comparing two possible solutions;
- explaining how several modules relate to each other;
- organizing technical documentation before implementation.
It can be used from the Claude web or app interface, and also through Claude Code, a tool oriented toward repository work from the terminal. Anthropic describes Claude Code as an agentic system capable of reading the codebase, making file changes, running tests and delivering reviewable code: Claude Code.
In addition, Claude stands out for its handling of long context and reasoning capabilities such as extended thinking, useful when a task requires analyzing alternatives before responding. Anthropic’s official documentation explains these capabilities for deeper reasoning scenarios: Extended Thinking.
Claude helps developers understand. It is strong when a problem requires analysis, context and reasoning before moving into implementation.
Codex: an agent for executing development tasks
Codex can be thought of as a programming agent. Unlike a tool designed only to answer questions or generate code snippets, Codex is oriented toward progressing through concrete tasks inside a project.
Instead of asking it only “explain how to fix this bug”, you can present it with a task closer to a real issue:
“Find where the cart total is calculated, fix the bug, run the related tests and show me the final diff.”
That type of interaction changes the tool’s role. Codex can search files, read code, modify it, execute commands, interpret errors and try again. Its value appears especially when the task has a clear objective and a way to validate it.
When does it make sense to use Codex?
Codex makes sense when the task can be expressed as a verifiable result. For example:
- fixing a bug with associated tests;
- implementing a bounded improvement;
- updating a function without changing the public API;
- refactoring a module with defined criteria;
- executing build or test commands to validate a change.
OpenAI presents Codex as a tool oriented toward agent-assisted programming, available across different environments such as ChatGPT, CLI or cloud workflows depending on the use case: OpenAI Codex.
Codex tries to execute a complete task. It is most useful when there is a clear objective, constraints and validation.
How the prompt changes depending on the tool
One good practice from the Tech Talk presentation was not using the same prompt for every tool. Each one works better with a different type of instruction.
In Cursor: short and local prompts
Because Cursor already has access to the open file, selected function and IDE context, the prompt can be more direct:
“Fix this function so it does not apply the discount twice. Do not change the method signature.”
It is not always necessary to explain the entire architecture because the immediate context is already available.
In Claude: context and reasoning
In Claude, it makes sense to include more information and request analysis before the solution:
“Analyze this cart calculation flow. Explain why the final total might be incorrect and propose possible causes before suggesting changes.”
The key is to take advantage of its ability to organize information, compare alternatives and explain relationships between system components.
In Codex: objective, constraints and validation
In Codex, the prompt should look more like a development task:
“Fix the bug in the total calculation. Do not change the public API. Run the related tests and show me the final diff.”
The clearer the definition of success, the more useful the result will be.
Context: not all tools use it the same way
The concept of context is central to artificial intelligence for software development, but it does not mean exactly the same thing in Cursor, Claude and Codex.
Cursor understands very well what the developer has in front of them: open file, selected function, indexed project and IDE environment.
Claude understands very well what is explained or provided to it: documentation, logs, extensive code fragments, technical decisions or architecture.
Codex can actively retrieve context: search files, read parts of the repository, execute commands and use that information to progress.
This difference matters because it shapes how each tool is used. Cursor is ideal when we are already located in the code. Claude helps when we still need to understand the problem. Codex is useful when we want the tool to move forward on a concrete task.
Security, privacy and permissions
When working with AI applied to development, security cannot be an afterthought. These tools can interact with proprietary code, internal documentation, logs, system configurations, architecture or even poorly placed credentials.
Before incorporating them into a team, it is worth asking three questions:
What data can the tool see?
It is not the same thing to analyze a selected function as to allow access to an entire repository.
What does it do with that data?
It is important to review training policies, retention, storage and available controls depending on the plan.
What permissions does it have to act?
Suggesting code is one thing; editing files, executing commands, installing dependencies or interacting with external services is another.
The greater the context and autonomy, the greater the control should be. In tools such as Cursor, it is important to review privacy settings and permission levels inside the IDE. In Claude, controls depend on the type of account or plan used. In Codex, the critical point is defining what it can execute, on which repositories and under what human review.
Good practice: treat these tools as technical collaborators with limited permissions, not as fully autonomous systems without supervision.
Token consumption
Cost does not depend only on “using AI”, but on the type of task, the chosen model, the amount of context and the level of autonomy.
In Cursor, many day-to-day tasks can be solved with local interactions or automatic modes. However, consumption can increase when using premium models, Agent Mode or extensive repository context.
In Claude, consumption is strongly associated with input size. Analyzing long documentation, extensive logs or multiple files implies more tokens. It also changes depending on the selected model: a model optimized for speed does not consume the same as one designed for deeper reasoning.
In Codex, consumption is usually related to the actions needed to complete the task. If it must read many files, run tests, review errors and retry, costs can grow through iteration.
The practical recommendation is to define the scope of the task clearly before delegating it. A clear prompt reduces unnecessary iterations and helps control consumption.
Practical example: the same bug in three tools
Suppose there is an error in the cart total calculation because a discount is applied twice.
In Cursor, we would select the suspicious function and request a local correction, reviewing the diff inside the IDE.
In Claude, we would provide the complete flow and ask it to analyze possible causes before touching the code.
In Codex, we would frame the task as an issue: find the bug, fix it, run tests and return the diff.
The problem is the same, but the approach changes. That is the key to the comparison.
Conclusion
Cursor, Claude and Codex show that AI applied to software development does not have a single use case. Each tool adds value at a different stage of the work: Cursor supports editing, Claude helps reasoning, and Codex attempts to execute complete tasks.
Rather than choosing one tool as “the best”, the important thing is understanding when to use each one. In a professional environment, the greatest value appears when they are integrated thoughtfully into the workflow, with good security practices, clear objectives and human review over the changes made.
Agustin Repetto works as a Developer at Urudata Software and participates in initiatives related to software development, AI and technology architecture. He co-presented the talk “Cursor vs. Claude vs. Codex” with Ronald Carbajal during the first edition of Tech Talks 2026.
