Anthropic recently released its new model, Claude Sonnet 4.5. The company claims it is the best AI model available.

I decided to put this claim to the test. I used the model to build five different applications, ranging from a simple scraper to a full-fledged booking system.

This review details my experience, the model's performance, and what you can expect when using it for your own development projects.

Key Takeaways

  • Claude Sonnet 4.5 shows significant performance improvements over previous models, including Opus 4.1.
  • The model excels at generating complete applications from a single, detailed prompt.
  • It demonstrates strong capabilities in creating simulations, web scrapers, games, landing pages, and complex backend systems.
  • The model's ability to create a project structure, documentation, and even demo data reduces development time.
  • While highly capable, some outputs required minor adjustments, such as connecting button functionality or updating image paths.

Anthropic Launched Claude Sonnet 4.5

Anthropic introduced Claude Sonnet 4.5 as a new leader in AI capabilities. The launch also included several product upgrades.

These updates feature a Chrome extension, new features within the Claude interface, and a refreshed VS Code extension. The model is positioned as the top choice for developers and complex AI tasks.

Comparison with Previous Models

claude 4.5 sonnet vs gpt
  • Internal benchmarks show Claude Sonnet 4.5 outperforms more expensive models like Opus 4.1.
  • On the SWE-bench benchmark, it scores higher than competing models like GPT-5 Codex.
  • The model delivers high-end performance at a more accessible cost.

Capabilities

  • Code Generation: It can create complex codebases for various applications with a single prompt. It handles HTML, CSS, JavaScript, TypeScript, and Python.
  • Project Scaffolding: The model generates entire project structures, including folders, files, and configuration.
  • User Interaction: It produces code optimized for user interaction, providing clear feedback during processes like web scraping.
  • Documentation: It creates helpful files like README.md and installation guides to simplify setup.

Tech Stack I Used

  • For this review, I used the Claude Sonnet 4.5 (thinking model).
  • All development occurred within the Windsurf IDE.
  • The model created new folders for each project, keeping the workspace organized.
  • Prompts which i used can be found here.

Apps I Built

I developed five distinct applications to test the model's versatility and power. Each project started with a single, detailed prompt.

1. Predator-Prey Simulation App

I tasked the model with creating a simulation of a predator-prey ecosystem.

  • Action: Using prompt, the model generated the necessary HTML, CSS, and JavaScript files.
  • Findings:
    • The application launched instantly.
    • It featured a dynamic chart that updated the populations of plants, prey, and predators in real time.
    • Controls for simulation speed and grid size were functional and responsive.
    • The output was statistically accurate and visually clear, surpassing the performance of older models on similar tasks.

2. Python Web Scraper

The next test was to build a Python scraper to extract data from a website and save it to a .txt file.

  • Action: A single prompt initiated the process.
  • Findings:
    • The model created a scraper.py file, a requirements.txt file, and a README.md.
    • After installing dependencies, the script prompted for a URL.
    • It successfully scraped the specified website, providing real-time updates in the terminal.
    • The final output was a correctly formatted .txt file, created without any follow-up instructions.

3. 3D Glass Shooter Game

To test its creative and complex coding abilities, I asked for a 3D first-person game.

  • Action: Based on the prompt, the model generated a complete game using TypeScript.
  • Findings:
    • The game loaded and was playable immediately.
    • Mouse controls for looking and clicking to shoot worked as intended.
    • The environment and glass-shattering effects were well-implemented.
    • This result from a single prompt shows a significant leap in AI coding for game development.

4. Next.js Landing Page

I provided a very detailed "{prompt}" for a high-converting landing page, specifying the tech stack and every section.

  • Action: The model used the prompt to generate a full Next.js project.
  • Findings:
    • The project structure was organized into components and sections.
    • It correctly used the logo URL provided in the prompt.
    • Features like a countdown timer and floating background icons were implemented.
    • While some buttons were not functional and images needed to be added, the overall structure, animations, and layout were impressive for a single prompt.

5. Full-Stack BNB Booking System

The final and most complex test was a full-stack booking system with a frontend and backend.

  • Action: A comprehensive prompt outlined the tech stack, features, and user roles.
  • Findings:
    • The model generated a large and complex project structure with Next.js, including a database schema and API routes.
    • It created a functional landing page, sign-in/sign-up pages, and separate user dashboards for admins, managers, and members.
    • Demo data for user accounts was included, along with an .env file for configuration.
    • The admin dashboard included features for user management, booking approvals, and system configuration, including Stripe integration settings and booking rules.
    • While some minor features like creating a booking as a member did not work, the core application was astonishingly complete.

Findings

  • Speed and Efficiency: Claude Sonnet 4.5 generates complex applications in minutes. The landing page took seven minutes, and the full booking system was also completed quickly.
  • One-Prompt Power: The model's ability to understand and execute large, detailed prompts without needing follow-up is its greatest strength.
  • Completeness: It does not just write code; it builds entire applications with project structures, documentation, and even demo data.
  • Areas for Improvement: Some outputs required manual intervention. This included connecting frontend buttons, adding image assets, and debugging minor functionality. However, these are small tasks compared to building the entire application from scratch.

Best Practices to Use This Model

  • Be Specific: Provide detailed prompts. The more information you give the model about the tech stack, features, structure, and desired outcome, the better the result will be.
  • Use the "Thinking" Model: The thinking version of Sonnet 4.5 appears to provide more thorough and complete results.
  • Structure Your Prompts: Break down your request into logical sections within the prompt. Define the tech stack, implementation details, and reasoning.
  • Expect to Tweak: While powerful, the model is a tool to accelerate development, not replace it entirely. Be prepared to make minor adjustments to the final code.

Final Thoughts

Claude Sonnet 4.5 is a huge step forward for AI-assisted coding. Its ability to generate nearly complete applications from a single prompt is a game-changer for developers. It dramatically reduces the time spent on initial setup, boilerplate code, and project structuring. While not perfect, it handles about 90% of the work, leaving developers to focus on refinement and unique features. Based on these tests, Claude Sonnet 4.5 is currently one of the best coding assistants available. It has the potential to transform development workflows and increase productivity.