The idea of using AI to help with computer programming has become a contentious issue. On the one hand, coding agents can make horrific mistakes that require a lot of inefficient human oversight to fix, leading many developers to lose trust in the concept altogether. On the other hand, some coders insist that AI coding agents can be powerful tools and that frontier models are quickly getting better at coding in ways that overcome some of the common problems of the past.
To see how effective these modern AI coding tools are becoming, we decided to test four major models with a simple task: recreating the classic Windows game Minesweeper. Since it’s relatively easy for pattern-matching systems like LLMs to play off of existing code to re-create famous games, we added in one novelty curveball as well.
Our straightforward prompt:
Make a full-featured web version of Minesweeper with sound effects that 1) Replicates the standard Windows game and
2) implements a surprise, fun gameplay feature. Include mobile touchscreen support.
Ars Senior AI Editor Benj Edwards fed this task into four AI coding agents with terminal (command line) apps: OpenAI’s Codex based on GPT-5, Anthropic’s Claude Code with Opus 4.5, Google’s Gemini CLI, and Mistral Vibe. The agents then directly manipulated HTML and scripting files on a local machine, guided by a “supervising” AI model that interpreted the prompt and assigned coding tasks to parallel LLMs that can use software tools to execute the instructions. All AI plans were paid for privately with no special or privileged access given by the companies involved, and the companies were unaware of these tests taking place.
Ars Senior Gaming Editor (and Minesweeper expert) Kyle Orland then judged each example blind, without knowing which model generated which Minesweeper clone. Those somewhat subjective and non-rigorous results are below.
For this test, we used each AI model’s unmodified code in a “single shot” result to see how well these tools perform without any human debugging. In the real world, most sufficiently complex AI-generated code would go through at least some level of review and tweaking by a human software engineer who could spot problems and address inefficiencies.