- Benchmarks distribute victories: generation (Grok 3 Beta, o3-mini), edition ( Gemini: 2.5 Pro) and real bugs (Claude 3.7).
- The context of the repo is key: IDEs with IA How Cursor and copilots in the editor boost productivity.
- CopilotTabnine, IntelliCode, Codex and Ghostwriter cover different profiles depending on integration and team.
- Try before you pay: evaluate connectivity, cost, and support; AI assists, but always review the code.
Artificial intelligence has become an integral part of daily development, and while programming already demands focus and methodology, now you also have to decide which assistant or model to use in each case. Between co-pilots in the editor, IDEs with integrated AI, and cutting-edge models for generating or editing code , the options are 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 your needs: generating functions from scratch is not the same as fixing real bugs, editing code in large repositories, or working in C++ with Visual Studio . Other factors include price, connectivity, privacy, support, and whether the tool understands your entire project. From here, let's 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 set the framework: the "best" AI should help you reliably generate, edit, and explain code , integrate into your workflow (e.g., VS Code, Visual Studio, or JetBrains editors ), understand the context of your repository, and offer good support at a reasonable cost.
Furthermore, three capabilities set these tools apart: high-quality contextual autocompletion, 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 through all the code is crucial.
Another less glamorous but crucial factor is connectivity. Many solutions rely on being online to provide real-time suggestions and analysis . If your environment has limited connectivity, this can be a serious obstacle.
Finally, cost and the option to try before you pay matter. Some people have regretted paying large fees without testing, so it's best to prioritize free versions or trials when available, especially if you have a budget of around €20/$ per month.
What the tests say: generation, editing, and real bugs
Companies publish benchmarks when they launch models, and there are also external measurements. OpenAI 's announcements regarding its o3 and o4 families reflect improvements over o1 in several areas, including coding, editing, and software engineering tasks . Although these materials primarily compare against their own models, they demonstrate broad coverage of technical skills.
Google , with Gemini 2.5 , does stand out against third-party tools: 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 achieved the best result over several attempts (79,4%), while o3-mini excelled in a single attempt (74,1%).
For code editing, the Aider Polyglot test favors Gemini 2.5 Pro . And when it comes to fixing real issues on GitHub with SWE-bench , the winner is Claude 3.7 Sonnet . Three tasks, three different winners: the obvious conclusion is that each model shines in different situations.
External sources such as LLM Stats help provide perspective. In the Aider Polyglot ranking (May 5, 2025), the o3 and o4-mini appear at the top , followed by Gemini 2.5 Pro, o3-mini, DeepSeek-R1, GPT-4.1, DeepSeek V3, and Gemini 2.5 Flash, with GPT-4.1 mini, GPT-4o, and GPT-4.1 nano in later positions.
In the SWE-bench ranking (May 5, 2025), Claude 3.7 Sonnet takes first place , followed 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 in finding real-world bugs.
Another indicator, LMC-Eval (Logical Math Coding Eval), tests mathematical reasoning with logic and programming . In April 2025, o1 and o3-mini led the rankings, followed by Claude 3.7 Sonnet, GPT-4.5, Gemini 2.0 Flash , Claude 3.5 Sonnet, GPT-40, and Mistral Large. This illustrates how the top three positions change depending on the nature of the challenge.
Copilots and extensions in the editor: immediate productivity
GitHub Copilot
Copilot integrates seamlessly with VS Code and also works in other environments, offering very useful autocomplete and contextual suggestions . For VS Code-centric workflows, the fit is especially good thanks to its ecosystem of extensions.
Its strengths lie in its multi-language support and continuous updates, resulting in relevant offerings for Python , JavaScript, Go, PHP, Ruby, and more . Furthermore, it reduces friction in repetitive tasks and is a great learning tool when exploring APIs or patterns.
On the other hand, it relies on an internet connection. Without a decent connection, the quality of the autocomplete suffers . And full access is paid, so individuals or small teams with tight budgets need to weigh the return on investment.
Ideal if you're already part of the GitHub/VS Code ecosystem and looking to boost productivity without switching editors. Its multilingual versatility is also a plus for multilingual teams.
tabnine
Tabnine focuses on AI-powered predictions and stands out for its deep customization : you can train models with your own repos to align suggestions with 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 seamless adoption across diverse environments . In large, long-term projects, this consistency with team standards is invaluable.
The price to pay is that getting the most out of it requires configuration and a learning curve. Training models and fine-tuning the tool is time-consuming and can be resource-intensive , especially with large repositories.
It shines in teams that prioritize style consistency, control, and alignment with internal guidelines . If 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 team-customized models.
Its compatibility covers C#, C++, Java, JavaScript, Python, TypeScript, and more, and it works best where Visual Studio and VS Code are the norm . Customization 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, you may lose integration compared to the native experience in VS/VS Code.
Recommended for organizations already using Microsoft that want consistency and speed with added intelligence.
OpenAI Codex
Codex, the foundation of Copilot, 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 speed . It's 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 scaling the budget according to the volume of requests.
It fits with advanced teams looking to equip their workflows with tailored AI capabilities , with fine control over quality and deployment.
Replit Ghostwriter
Ghostwriter lives within Replit's online IDE and offers a cloud-based, collaboration-ready environment with AI suggestions and real-time shared editing . Ideal for moving between devices without any local setup.
Simultaneous collaboration shines in pair programming, hackathons, and education; in addition, the public project community is 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
Among environments that natively integrate AI, Cursor has gained significant traction. Based on VS Code, it inherits extensions and configuration, and integrates editor chat, contextual suggestions, and refactoring with advanced models. In a survey with over 28.000 votes against alternatives like Windsurf, Replit, v0, Lovable, and Bolt, more than 70% recommended Cursor.
A key advantage is the ability to choose the model to use. Some point out that the Cursor + Claude 3.7 Sonnet combination works particularly well for programming with AI, blending project context with strong reasoning in editing and issue resolution.
Furthermore, the "you have the final say" approach keeps control in the developer's hands. AI suggests, 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 among them, they share the idea of an IDE with AI as a co-pilot within the workflow , without 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.
- 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 Studio ) and find that "deep" problems are beyond your current AI, the priority is for the assistant to understand the entire repository context. Tools with in-IDE chat and access to the file tree make a difference because they don't just reason about the active tab.
In that scenario, an IDE like Cursor with a robust model for editing and bug fixes (for example, Claude 3.7 Sonnet ) can be a winning combination. Copilot is also a good addition if your work revolves around GitHub/VS Code, while Tabnine shines if your team wants to align suggestions with their style by training the model with internal repositories.
Budget around €20/$ per month: try starting with free trials or entry-level plans before committing to larger fees. Avoid paying annual fees without evaluating how well it fits your cash flow, especially if you can't test it first . Experience shows it's best to validate with your code before making a big investment.
If your work is cloud-based and collaborative, Replit Ghostwriter is incredibly convenient, offering real-time collaboration and lightning-fast onboarding. For fully Microsoft-based teams, IntelliCode provides seamless integration with Visual Studio/VS Code and improves with training in your repository.
Remember the limitations: many tools rely on the internet; if your environment is intermittent, consider solutions that work better offline or adjust your expectations . And, 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 incredibly helpful. There are spaces focused on the "programmer side" of AI chat where experiences, tips , creations, and rules for participating without interference are shared. They're a great place to learn from real-world examples and compare tools.
Furthermore, choosing an assistant isn't just about the model: the product layer matters. Interface, repository integration, contextual search, plugins, and good support make all the difference. Platforms like Copilot or even search assistants like Perplexity can add value with their experience layers, beyond the underlying model.
A practical tip: many websites and services display cookie notices and request consent for analytics or device preferences. This is standard practice; adjust your privacy settings and keep this 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 there's no single winner for everything: some excel at 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, fully understands your repository, integrates seamlessly with your editor, and enhances your workflow without taking away your code control.
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