Having spent decades immersed in the ever-evolving world of technology, I’ve grown accustomed to a steady rhythm of incremental advancements—so much so that few new innovations manage to truly surprise me anymore. Yet, when OpenAI first unveiled ChatGPT and I decided, somewhat on a whim, to ask it to build a custom WordPress plugin for my wife’s small e-commerce website, what happened next genuinely caught me off guard. Within minutes, it not only produced functional code but also delivered a working plugin that operated seamlessly. I remember staring at the screen in disbelief—something in the core of modern computing had just shifted dramatically. That was back in 2023, a year that now feels like a pivotal turning point in the evolution of artificial intelligence. Since then, the realm of generative AI and, in particular, AI-assisted programming has advanced at a breathtaking pace.
In those early days, our primary interaction with these systems was through simple chatbot interfaces. Developers would manually describe their desired functions, receive a block of generated code in return, and then copy and paste that output into their editors—rudimentary yet revolutionary in its implications. But that primitive stage of AI-assisted coding was destined to evolve quickly. Initially, comparisons among these systems centered on the performance of their underlying large language models (LLMs), but as AI development accelerated, it became evident that this focus alone no longer told the full story.
By 2025, a new era had dawned—the rise of the “coding agent.” Giants like GitHub, Anthropic, Google, and OpenAI each introduced their own sophisticated assistants: GitHub Copilot, Claude Code, Google Jules, and OpenAI Codex. These were not mere chatbots but intelligent tools embedded directly into developer workflows through integrations with environments such as Visual Studio Code, command-line terminals, and GitHub repositories. Their arrival marked the moment when AI stopped being an external reference tool and instead became a co-pilot—an active participant in the development process itself.
However, these powerful tools came at a cost, quite literally. The computational resources required to sustain such complexity are massive, and companies price their services accordingly. In my own testing, OpenAI’s ChatGPT Plus plan, at $20 per month, allowed roughly two days of intensive Codex use, while the Pro subscription, priced an order of magnitude higher at $200, unlocked extended capabilities suitable for longer projects. At those pro-tier speeds, I astonishingly completed the equivalent of four years of product development in a mere four days—an experience that still feels surreal. Comparable pricing structures emerged across competing platforms such as Claude, Gemini, and Copilot.
Of course, not everyone is eager—or able—to pay hundreds of dollars a month for AI access. For that reason, I shifted the focus of my recent tests to free AI chatbots, exploring how well these cost-free alternatives perform when confronted with real-world programming challenges. The purpose was clear: to determine whether no-cost tools could offer developers meaningful support, even if their raw power and accuracy lagged behind their expensive counterparts.
The results were illuminating. While none of the free offerings matched the precision of the pro-grade systems, several nonetheless produced competent and practical code. I performed a series of structured tests—among them, designing a WordPress plugin, rewriting a complex string validation function, debugging a subtle framework error, and scripting a multi-application automation involving Chrome, AppleScript, and the macOS utility Keyboard Maestro. Unsurprisingly, the success rate varied considerably from one model to another.
Microsoft’s GitHub Copilot, even in its free “Quick Response” mode, emerged as the clear winner, flawlessly passing all four of my tests. Its initial unresponsiveness reminded me humorously of HAL 9000—displaying a polite refusal before eventually cooperating—but once operational, it performed exceptionally, crafting accurate code and intelligently identifying logic flaws others missed. Notably, it handled the challenging AppleScript integration without error, showing a nuanced grasp of macOS scripting conventions.
ChatGPT’s free tier came next, delivering strong results across three of the four scenarios. It easily produced a functioning WordPress plugin and debugged errors effectively, but stumbled when advanced AppleScript logic entered the equation. The root of the failure was minor—a missing import line for a non-native function—but enough to mark the test as incomplete.
In third place was DeepSeek, which demonstrated impressive though imperfect reasoning. It generated thoughtful solutions and even added an intuitive “Copy to Clipboard” interface button, a small but user-friendly touch. Yet, its habit of offering multiple alternative code versions complicated the assessment process, forcing me to verify and compare outputs manually. When it worked, it worked beautifully—but consistency remained elusive.
The remaining contenders—Claude, Meta, Grok, Perplexity, and Gemini—each exhibited critical shortcomings that rendered their free versions unreliable for developers seeking serious coding assistance. Claude’s free Sonnet 4.5 model required cumbersome login verification and failed half the tests; Meta’s AI encountered validation logic errors and ignored key prompt components; Grok’s limitations in “auto” mode and restrictive query limits in “Expert” mode made it impractical for sustained work; Perplexity’s code occasionally crashed due to poor input handling; and Gemini’s free Flash model, despite an admirable history in its Pro incarnation, suffered from multiple functional failures and inefficient design.
After exhaustive testing, the verdict is clear: only three free chatbots—Microsoft Copilot, ChatGPT Free, and DeepSeek—demonstrate consistent value for programmers. The others may hold potential for non-technical uses but fall short in the rigorous context of real-world software development. For those operating within a zero-cost budget, a strategic combination of Copilot, ChatGPT, and DeepSeek can yield the best collective performance. In fact, I often feed the output of one AI into another to cross-analyze results—a powerful technique when you’re not constrained by usage fees.
Ultimately, the rapid evolution of these systems shows no sign of slowing. With every new release, generative AI becomes a sharper, more context-aware collaborator to developers across the globe. It is clear that even though free versions lag behind paid models in sophistication, they are improving at an astonishing rate—and the gap continues to close. If today’s free chatbots can already produce working plugins, intelligently debug logic, and handle partial automation, what will next year bring? One thing is certain: the momentum of progress is accelerating beyond all earlier expectations.
For developers, entrepreneurs, and curious technologists alike, this era represents both challenge and opportunity. The landscape of AI-assisted programming is reshaping how we think about productivity, creativity, and code itself. Stay tuned—the next wave of innovation is already on the horizon.
Sourse: https://www.zdnet.com/article/the-best-free-ai-for-coding-in-2025-now-only-three-make-the-cut-while-five-fall-flat/
