The Best AI for Programming in 2025 and Which to Avoid

In recent months we have witnessed a veritable explosion of tools based on artificial intelligence (AI) that promise to revolutionise the work of programmers. These tools, often built on advanced large language models (LLMs), tempt with faster development, automation of routine tasks and help with solving complex problems. To separate the genuinely useful assistants from the marketing promises, a thorough test of 14 major LLMs was carried out. The aim was to find out how these models perform in the real situations programmers encounter every day.

Testing methodology

Each of the 14 selected models was put through a series of four specific programming tasks. The tasks were designed to represent the typical activities of a developer:

• Editing existing code: Modifying the functionality or structure of a given piece of code.
• Fixing bugs (debugging): Identifying and correcting errors in faulty code.
• Generating new functions: Writing code for a specific, newly defined task.
• Code analysis: Understanding and explaining the purpose or structure of existing code.

This hands-on approach made it possible to assess the abilities of each AI tool objectively, in the context of everyday programming work.

Surprising results and ratings

The test results brought several surprises. It turned out that some tools in which great hopes had been placed, and which are frequently talked about, failed in the practical tasks. Conversely, some lesser-known or new models delivered a very solid performance and showed their potential.

The best AI tools for programming tasks:

1. ChatGPT Plus (with the GPT-4o model):
   • This paid version from OpenAI excelled and successfully handled all four test scenarios.
   • The output it produced was not only functional but also accurate and relevant to the context.
   • It is the ideal choice for professionals and teams willing to invest in a monthly subscription in return for top-tier performance.
2. Perplexity AI (free version):
   • This tool, which in its free version probably uses a model at the level of GPT-3.5, offered surprisingly solid and usable help, entirely free of charge.
   • It is an excellent alternative for individuals, students or developers on a limited budget who are looking for a capable AI assistant without any financial commitment.
3. Grok (developed by X / Twitter):
   • Grok was one of the pleasant surprises of the test. It successfully handled three of the four tasks.
   • Although this model is not yet widely used or well known, its performance points to considerable future potential in programming assistance.
4. DeepSeek V3:
   • This open-source model proved its quality by passing most of the tests.
   • An interesting choice for more technically skilled users who value transparency and the option to customise.

Tools that disappointed in the tests and are better avoided (for now):

1. DeepSeek R1: This model could not cope even with the relatively simpler tasks and proved insufficiently mature for practical use in a programming workflow.
2. GitHub Copilot: Despite strong marketing support and integration into development environments, it failed in the tests. The solutions it generated were often inaccurate, non-functional or far from optimal.
3. Meta AI & Meta Code Llama: The tools from Meta (formerly Facebook) also failed most of the test tasks and are not currently a reliable choice for programmers.
4. Claude 3.5 Sonnet & Gemini Advanced: Even these advanced models from the renowned companies Anthropic and Google struggled with several important tasks. Their output often lacked the necessary logic or accuracy, or they failed to interpret the brief correctly.
5. Microsoft Copilot: This tool failed the tests completely and proved unsuitable for serious development work within the tested scenarios.

The current generation of AI tools can be a useful helper for smaller, isolated tasks, such as fixing a simple syntax error, refactoring a short section of code or generating a few lines for a standard function. They are still not, however, at a level where they could independently design and implement a complex software application from start to finish.

Based on the tests carried out, the most reliable tools appear to be those built on OpenAI technology, specifically the paid ChatGPT Plus (GPT-4o) for maximum performance and the free Perplexity AI as a very capable no-cost alternative.

It is important to remember, though, that the field of AI is evolving extremely fast. It therefore pays to keep testing different tools and, where appropriate, to combine them according to the specifics of the task at hand. And most importantly, never trust AI output blindly. Generated code must always be checked carefully, its workings understood, and its correctness and security verified before it is deployed to production. Human oversight and critical thinking remain essential.

Source: ZDNet

Update: A drop in performance, or what is going on with GPT-5?

Update: The older GPT-4o model is back

The older GPT-4o model has returned to the chatbot, but it is available only to users with an active subscription.