What is the best AI for programming: tests, tools and real-world examples

La Artificial Intelligence It has fully infiltrated the day-to-day of development, and if programming already requires focus and method, now it also requires decide which assistant or model to use in each case. Between co-pilots in the editor, IDEs with integrated AI and leading models for generating or editing codeThe range of options is so vast that it's natural to ask the big question: which is the best AI for programming?

The short answer is that it depends on what you need: generating functions from scratch is not the same as... Fix real bugs, edit code in large repositories, or work in C++ with Visual StudioOther factors to consider include price, connectivity, privacy, support, and whether the tool fully understands your project. From here, we'll get down to business with tests, tools, and practical recommendations.

Clear criteria for deciding the best AI for your needs

Before getting into specifics, let's establish the framework: the "best" AI should help you generate, edit, and explain code with reliability, integrate into your flow (for example, VS Code, Visual Studio or JetBrains editors), understand the context of your repo, and offer good support at a reasonable cost.

In addition, there are three capabilities that differentiate tools: high-quality contextual autocomplete, guided editing of existing files and autonomous issue resolution in real repositories. If you work with large projects, the ability of AI to "read" and reason about all the code is key.

Another less glamorous but crucial factor is connectivity. Many solutions depend on being online to offer their services. suggestions and real-time analysisIf your environment has limited connectivity, this can be a serious obstacle.

Finally, cost and the option to try before you buy are important. Some people have regretted paying large fees without testing, so it's best to prioritize. free versions or trials when they exist, especially if you have a budget of around €20/$ per month.

What the tests say: generation, editing, and real bugs

Companies publish benchmarks when launching new models, and there are also external measurements. In the announcements of OpenAI Regarding their O3 and O4 families, there is improvement compared to O1 in several areas, including coding, editing, and software engineering tasksAlthough these materials mainly compare with their own models, a broad coverage of technical skills is evident.

Google, with Gemini 2.5It does stand out against third parties: OpenAI's o3-mini and GPT-4.5, Anthropic's Claude 3.7 Sonnet, xAI's Grok 3 Beta and DeepSeek R1. In the LiveCodeBench v5 code generation test, Grok 3 Beta marks the best result with several attempts (79,4%), while o3-mini stands out in a single attempt (74,1%).

For code editing, the test Aider Polyglot favors the Gemini 2.5 ProAnd when we talk about fixing real issues on GitHub with SWE-bench, the winner is Claude 3.7 SonnetThree tasks, three different winners: the obvious conclusion is that each model shines in different situations.

External sources like LLM Stats help provide perspective. They appear at the top of the Aider Polyglot ranking (May 5, 2025). o3 and o4-mini, followed by Gemini 2.5 Pro, o3-mini, DeepSeek-R1, GPT-4.1, DeepSeek V3 and Gemini 2.5 Flash, with later positions for GPT-4.1 mini, GPT-4o and GPT-4.1 nano.

In the ranking of SWE-bench (May 5, 2025), first place goes to Claude 3.7 Sonnetfollowed by o3 and o4-mini, with Gemini 2.5 Pro, GPT-4.1, o3-mini, DeepSeek R1, and Claude 3.5 Sonnet trailing behind. Again, a competitive picture emerges between OpenAI, Google, DeepSeek, and AnthropIc, with Claude excelling at real bugs.

Another indicator, LMC-Eval (Logical Math Coding Eval), tests mathematical reasoning with logic and programmingIn April 2025, o1 and o3-mini were leading, followed by Claude 3.7 Sonnet, GPT-4.5, Gemini 2.0 FlashClaude 3.5 Sonnet, GPT-40 and Mistral Large. Here you can see how the podium changes depending on the nature of the challenge.

Copilots and extensions in the editor: immediate productivity

GitHub Copilot

Copilot integrates seamlessly into VS Code and also works in other environments, offering autocomplete and contextual suggestions Very useful. For workflows focused on VS Code, the fit is especially good thanks to its ecosystem of extensions.

Its strengths are multilingual support and continuous updates, which translates into relevant proposals in Python, JavaScript, Go, PHP, Ruby, etc.Furthermore, it reduces friction in repetitive tasks and is a great learning tool when exploring APIs or patterns.

On the other hand, it depends on the internet. Without a decent connection, The quality of autocomplete suffersAnd full access is paid, so for individuals or small teams with tight budgets, the return on investment needs to be considered.

Ideal if you already work within the GitHub/VS Code ecosystem and are looking for a productivity boost without switching editors. In multilingual teams, its multilingual versatility It also earns points.

tabnine

Tabnine is betting on AI-powered predictions and stands out for its deep customizationYou can train models with your own repos to align suggestions to your team's style.

One of its greatest strengths is its broad compatibility: it works with VS Code, IntelliJ, Sublime Text, Atom, and more, offering a smooth adoption in diverse environmentsIn large, long-term projects, this consistency with team standards is appreciated.

The price to pay is that getting the most out of it requires setup and a learning curve. Training models and fine-tuning the tool is time-consuming and... can be resource-intensiveespecially with large resting periods.

It shines in teams that prioritize style consistency, control, and alignment with internal guidesIf you're willing to set it up, the return is solid.

IntelliCode (Microsoft)

IntelliCode takes Visual Studio and VS Code's autocomplete a step further with code patterns learned from quality repositories and models customized by team.

Its compatibility covers C#, C++, Java, JavaScript, Python, TypeScript, etc., and it works best where Visual Studio and VS Code are the standardPersonalization based on your repository increases the relevance of the suggestions.

In return, it requires initial setup to train its own models, and its best version lives within the Microsoft ecosystem. If you use other IDEs, integration may be lost compared to the native experience in VS/VS Code.

Recommended for organizations that are already on Microsoft and want consistency and speed with an added layer of intelligence.

OpenAI Codex

Codex, Copilot's base, is distinguished by its ability to understand and generate code with high accuracy, encompassing multiple languages ​​and best practices.

It's not just for autocomplete: it can be integrated into internal tools, automate tasks, generate documentation, or create prototypes at speedIt is versatile for translating code between languages, writing tests, or refactoring.

The other side of the coin is the complexity of integration and the cost in intensive use cases. Implementing it optimally requires a thorough understanding of the APIs and size the expenditure depending on the volume of requests.

It fits with advanced equipment that seeks to equip its workflows with tailored AI capabilitieswith fine control over quality and deployment.

Replit Ghostwriter

Ghostwriter lives within Replit's online IDE and offers a cloud-based environment ready for collaboration, with AI suggestions and real-time shared editingIdeal for moving between devices without configuring anything locally.

Simultaneous collaboration shines in pair programming, hackathons, and education; furthermore, the public projects community It's a plus for learning and reusing ideas.

Disadvantages? Without an internet connection, there's almost nothing, and for very complex projects, a powerful local environment usually provides more control, performance, and dependency management.

Highly recommended for students, teachers, and small teams that prioritize accessibility and immediate collaboration.

IDEs with integrated AI: Cursor and company

Within the environments that natively integrate AI, Cursor It has gained significant traction. Based on VS Code, it inherits extensions and settings, and integrates chat into the editor. contextual suggestions and refactorings with advanced models. In a survey with more than 28.000 votes against alternatives such as Windsurf, Replit, v0, Lovable or Bolt, more than 70% recommended Cursor.

A key advantage is being able to choose the model to use. Some point out that the combination Cursor + Claude 3.7 Sonnet It works especially well for programming with AI, blending project context and strong reasoning in editing and issue resolution.

Furthermore, the "you have the final say" approach keeps control in the developer's hands. AI proposes, streamlines, and automates, but the final decision on changes rests with the user. preserving quality and style.

Other options from the same "club" (Windsurf, v0, Lovable, Bolt) are evolving and, although the focus changes between them, they share the idea of ​​an IDE with AI as a co-pilot. within the workflowwithout jumping to external chats.

Other useful AIs for code: from the cloud to security

• AWS Amplify It stands out if you're looking for AI-powered development and implementation with the strength of the Amazon ecosystem. It offers code generation, predictive analytics, and automated deployments with version control.
  • Typical application: quickly launching the backend of a mobile app with authentication and data, generating APIs and configuring Cognito and DynamoDB to focus on UX without fighting with servers.
• ChatGPT It's an effective all-rounder for generating, explaining, and reviewing code in multiple languages. It understands natural language instructions and It returns functional solutions. that you can adapt and optimize.
  • Practical example: describe a script using Python scraping to extract product prices and receive a first draft of code with appropriate libraries, shortening There de Boot of the project.
• Amazon Code Whisperer It's AWS's copilot, offering multilingual support and deep integration with its services. If your infrastructure is on Amazon, it makes sense to take advantage of it. the synergy with its ecosystem.
  • Typical example: a hackathon integrating Amazon Lex and Polly for a voice interface, where the Suggestions accelerate implementation in record time.
• AlphaCode It is geared towards solving competitive-style programming problems, offering several candidate solutions. Useful for complex algorithmic challenges, such as optimizing routes with A* search or heuristics more advanced.
  • In R&D contexts, having multiple approaches gives you a good starting point for iterate and validate ideas in less time.
• deep code It focuses on software security, detecting vulnerabilities and proposing fixes without slowing down the pace of development. Integrated into the CI/CD flowIt helps to reach production with fewer surprises.
  • A typical example would be an online store launching: analyze the repository to find common authentication flaws, injections or secret management and correct them before opening to the public.

How to choose well: real-life scenarios and practical advice

If you work on large projects (for example, C++ with Visual StudioAnd if you notice that the "deep" problems are beyond your current AI, the priority is for the assistant to understand the entire context of the repository. Tools with in-IDE chat and access to the file tree make all the difference, because They don't reason only about the active tab.

In that scenario, an IDE like Cursor with a robust model in terms of editing and bugs (for example, Claude 3.7 SonnetThis can be a winning combination. Copilot is also a good addition if your life revolves around GitHub/VS Code, while Tabnine shines if your team wants Align suggestions with your style training the model with internal rests.

Budget around €20/$ per month: try starting with free trials or entry-level plans before committing to larger fees. Avoid paying annual fees without assessing whether it truly fits your cash flow. especially if you can't test beforehandExperience shows that it's best to validate with your code before making a big bet.

If your work is cloud-based and collaborative, Replit Ghostwriter is incredibly convenient, with real-time collaboration and ultra-fast onboarding. Ideal for 100% Microsoft-based teams. IntelliCode It offers seamless integration with Visual Studio/VS Code and improves with training in your repo.

Remember the limitations: many tools depend on the internet; if your environment is intermittent, consider solutions that work better offline or adjust expectationsAnd, above all, review the generated code: they are assistants, not replacements. The advice in several guides is clear: use AI as support and always validate the result.

Community, support and privacy

Specialized communities are a huge help. There are spaces focused on the "programmer side" of AI chat where experiences are shared, Trickscreations and rules to participate quietly. They are a good place to learn from real cases and compare tools.

Furthermore, choosing a wizard isn't just about the model: the product layer matters. Interface, repository integration, contextual search, plugins, and good support They make a difference. Platforms like Copilot or even search assistants like Perplexity can add value with their layers of experience, beyond the underlying model.

A practical note: many websites and services display cookie notices and request consent for analytics or device preferences. This is normal in the industry; Adjust your privacy settings And keep that in mind when evaluating online tools.

If you are interested in formal training, there are specific AI programs applied to development that include these tools in their curriculum, with a Full Stack approach and high-demand career paths. aimed at professionalizing the use of AI in real projects.

After reviewing benchmarks, tools, and scenarios, it's clear that there's no single winner for everything: some excel in code generation, others shine in editing and real-world bugs, and AI-powered IDEs make a difference in product integration. The best choice is the one that, with testing and controlled costs, It understands your entire repository, integrates with your editor, and reinforces your workflow. without stealing your control of the code.

Related article:

Google launches Gemini Code Assist: the free AI-powered programming assistant

Isaac

Passionate writer about the world of bytes and technology in general. I love sharing my knowledge through writing, and that's what I'll do on this blog, show you all the most interesting things about gadgets, software, hardware, tech trends, and more. My goal is to help you navigate the digital world in a simple and entertaining way.