I Tested Claude Sonnet 4.5 vs ChatGPT-5 vs Opus 4.1

Comparative Analysis: Evaluating Claude Sonnet 4.5, ChatGPT-5, and Opus 4.1 Through Practical Testing

Introduction

Recent advancements in artificial intelligence have introduced several cutting-edge generative models, each claiming superior performance across various tasks. Notably, Anthropic’s release of Claude Sonnet 4.5 positions itself as “the best coding model in the world,” prompting industry analysts and enthusiasts to question the comparative capabilities of these emerging models. This article presents a detailed, hands-on evaluation of Claude Sonnet 4.5, OpenAI’s ChatGPT-5, and Opus 4.1, conducted through a series of practical implementations to assess their real-world effectiveness.

Methodology

The assessment comprised three core tasks performed uniformly across all models:

• Developing a functional version of the classic game Angry Birds from scratch.
• Designing conversion-optimized landing pages.
• Utilizing consistent prompts across multiple attempts to ensure fairness.

The goal was to analyze their strengths and limitations in diverse contexts, providing insights into their suitability for different use cases.

Results and Findings

Game Development

In the domain of game development, Opus 4.1 demonstrated exceptional proficiency, delivering a playable and coherent Angry Birds implementation with well-functioning physics. Conversely, Claude Sonnet 4.5 produced an aesthetically pleasing interface but failed to deliver a playable experience, with issues such as broken physics and crashes. ChatGPT-5’s attempt was partially functional but lacked stability to be considered a viable solution.

Landing Page Creation

When tasked with generating conversion-focused landing pages, Claude Sonnet 4.5 outperformed expectations, showcasing superior design consistency, minimal errors, and compelling copywriting. Opus 4.1, despite its ambitious approach, displayed inconsistency and some design flaws, whereas GPT-5 offered competent but less polished results.

Expert Analysis

The outcome underscores the notion that there is no universally “best” model; rather, suitability depends on specific applications. For complex or creative logic tasks, Opus 4.1 appears advantageous. Structured design work benefits from Claude Sonnet 4.5’s stability and consistency, especially for long-term projects. In scenarios involving vague or broad prompts, Opus 4.1 demonstrates flexibility, while Claude Sonnet 4.5 excels with detailed, precise instructions.

Future Directions

Further testing involving highly detailed prompts is planned to evaluate the models’ performance under more demanding conditions. Additionally, understanding the impact of model stability and output