Claude 3.5 Sonnet: First Impressions and Practical Tests for Code Generation

Introduction

Anthropic has recently launched their new model, Claude 3.5 Sonnet, and it's generating quite a buzz. This blog post will delve into initial reactions and performance benchmarks, focusing particularly on its code generation capabilities. Instead of getting bogged down in hyper-technical benchmarks, we will focus on real-world applications and see how it can make you a better developer. We'll explore some of the online sentiments, and then perform some practical tests to understand its strengths and weaknesses. This article should take around 10-15 minutes to read and is aimed at developers and those interested in the practical impact of new AI models on their workflow. No special prior knowledge is needed, but a basic understanding of AI and code generation is beneficial.

Core Objectives

Analyze Claude 3.5 Sonnet's performance in coding tasks.
Compare it with previous models and competitors.
Conduct practical tests to assess real-world usefulness.
Examine feedback from the developer community.

Understanding the Context: Claude 3.5 Sonnet

The AER Leaderboard and Cost-Effectiveness

The Agent for coding (AER) leaderboard, a strong benchmark for coding ability, places Claude 3.5 Sonnet in fourth position with a 75% success rate, just behind Claude 3 Opus. It's important to note that Sonnet is not designed to outperform Opus. Instead, it is positioned as a more cost-effective alternative, similar to how OpenAI offers smaller, more affordable models like GPT-4-mini alongside their flagship models. While it is outperforming many other models, user feedback is reporting that the price isn't low enough considering the performance.

Reasoning Benchmarks and Community Feedback

In reasoning benchmarks, especially those involving Chain of Thought processes, Claude 3.5 Sonnet performs admirably. However, it still trails behind more expensive, flagship models. Initial feedback from the community, like that from Reet, highlights the model’s improved capabilities but also notes that it is significantly more expensive than alternatives like Gemini Flash. You can find some of these benchmarks using the links provided in the description.

Hands-On Testing: Pixel-Perfect Clone and Web App Creation

Let’s move beyond theoretical benchmarks and into real-world testing of this model.

Test 1: Component Copy

Our first test involves a "pixel-perfect clone" of a hero section from the V0 dev website. We will compare the output of Claude 3.5 Sonnet with that of another model, Hau.
Prompt: Create a pixel-perfect clone of the hero section from v0 dev.

Claude 3.5 Sonnet's Result

Claude 3.5 Sonnet successfully created a component with animations, showcasing its ability to follow complex instructions and replicate design elements. It appears to have leveraged framer for the animations, which is a solid approach for web development.

Hau's Result

In contrast, Hau failed to even begin the task. This is a strange result, given that Hau is a capable code-generating model. The inability of Hau to even start the task is quite concerning, and definitely puts Sonnet ahead in this test.

Test 2: Web App Development

The second test involves a more complex task: building a web app that displays food items and their ingredient changes over time. A previous attempt with Claude 3 Opus required about nine tries to get it to a usable state.

Methodology

We will approach this by first asking the model to act as a software architect, then breakdown steps, and then build the app. This workflow, inspired by AER, helps to structure the complex coding task.

Step 1: Act as Software Architect Prompt: "Act as a software architect"

Claude 3.5 Sonnet's Analysis

Sonnet provides a structured breakdown of the process, including steps like data modeling, API creation, and frontend development. It offers both code snippets and clear explanations.

Hau's Analysis

Hau takes a different approach, providing a markdown-formatted plan without diving into code or detailed explanations.

Step 2: Generate Step-by-Step Instructions Prompt: "Break out all the steps we need to take to implement this step by step. Your output is a guide for an editing or a programmer"

Claude 3.5 Sonnet's Response

Claude 3.5 Sonnet provides a detailed plan, outlining the need for a data model, an API, and a frontend with TypeScript, React, Tailwind CSS, and Superbase for the database. It suggests a clear roadmap but also departs from the original plan, which is not necessarily a bad thing, but it is different.

Hau's Response

Hau delivers a quicker response, but still primarily uses markdown and lacks the depth of code generation.

Step 3: Build the Web App Prompt: "Build it"

Claude 3.5 Sonnet's Code Generation

Claude 3.5 Sonnet attempts to generate the necessary components in TSX, omitting the next router and super base setup for simplicity. The speed of code generation is very impressive, however the final result was not functional.

Hau's Code Generation

Hau once again comes up short. The code is not as comprehensive and it is not functional either.

Overall Impressions

Based on these tests, Claude 3.5 Sonnet surpasses Hau in both code generation and adherence to instructions. Although the final result didn't work, Sonnet gave significantly more insight. Hau failed to even start many of the code generation tasks. Hau may be better for those that need documentation instead of code.

Practical Use Cases and Further Steps

Documentation Agent

Claude 3.5 Sonnet seems to excel in documentation. You could give it access to website URLs and have it extract and format the documentation into a well structured markdown file. This is valuable for developers and for those who have documentation to keep up-to-date.

Evaluation Workflow with make.com

A robust evaluation workflow can be established using make.com, a no-code platform. This allows non-developers to send a series of requests to models, iterate over tasks, and log results in a spreadsheet. This is an ideal solution for rapid evaluation, especially if you do not have access to Python programming.

Conclusion

Claude 3.5 Sonnet is a strong contender in the AI code generation space. While it may not outperform flagship models in every metric, its cost-effectiveness and solid performance in practical tests make it a useful tool for many use cases. It's clear that it has advantages in complex code generation and detailed explanations, which will make it useful to developers. Its speed is also impressive and the potential it has for documentation makes it a worthy model to check out.

Next Steps

Explore make.com for evaluations: Set up workflows to evaluate different AI models.
Documentation Agent: Test the capabilities of Claude 3.5 Sonnet as a documentation agent.
Deeper Dive: Conduct more specific tests tailored to your unique use cases.

Thank you for taking the time to read this article, please like and subscribe, and see you in the next one!