Claude 3.5 Sonnet vs. GPT-4o: React Benchmark
We asked both models to build an authenticated dashboard from scratch. Read our full methodology and see which model provided the most reliable code.
We asked both models to build an authenticated dashboard from scratch. Read our full methodology and see which model provided the most reliable code.
A review of autonomous coding agents. We tested Devin on a legacy codebase to evaluate its real-world performance.
Read article →Learn how to set up a local pipeline that reviews your pull requests before merging, running entirely on a local machine.
Read article →Can you upload a Figma screenshot and get a functional React component? We tested 50 diverse UI patterns to find out.
Read article →We asked both models to build an authenticated dashboard from scratch. Read our full methodology and see which model provided the most reliable code.
A review of autonomous coding agents. We tested Devin on a legacy codebase to evaluate its real-world performance.
Learn how to set up a local pipeline that reviews your pull requests before merging, running entirely on a local machine.
With the rapid release of new AI models, it can be difficult to determine which performs best for real-world tasks. We built an automated benchmark to test them.
The prompt was simple but demanding. We provided zero architectural constraints beyond requiring React, Tailwind, and a mock authentication flow. We wanted to see how the models structure a project from scratch when left entirely to their own devices.
We ran the exact same zero-shot prompt through the API of both models. Temperature was set to 0.2 to reduce variance while allowing slight creative freedom in UI design.
"GPT-4o gave us a polished UI but hallucinated a dependency. Claude 3.5 Sonnet gave us a functional UI, and the code worked flawlessly on the first paste."
GPT-4o writes code that looks visually impressive. Its Tailwind usage is sophisticated, utilizing complex grid layouts and modern utility combinations. However, it made a critical error in state management.
import { createContext, useContext, useReducer } from 'react';
import { fakeAuthApi } from '@mock/api'; // ERROR: Hallucinated import
const AuthContext = createContext(null);
export const AuthProvider = ({ children }) => {
const [state, dispatch] = useReducer(authReducer, initialState);
const login = async (email, password) => {
const user = await fakeAuthApi.login(email, password);
dispatch({ type: 'LOGIN_SUCCESS', payload: user });
};
// ... rest of the context
Notice line 2. It assumed the existence of a @mock/api package instead of building the mock function inline as requested. This caused an immediate build failure.
Claude took a different approach. It built the mock infrastructure inline, ensuring the file was self-contained and runnable immediately. It properly isolated concerns and even added JSDoc comments to the mock functions.
While its UI was slightly more basic (relying heavily on standard flexboxes rather than advanced CSS grids), it scored a perfect 1.0 on our strict compilation test.
For zero-shot scaffolding of complex React architecture without human intervention, Claude's prioritization of self-contained logic over UI flair makes it a more reliable choice for developers.
A review of autonomous coding agents. We tested Devin on a legacy codebase to evaluate its real-world performance.
Learn how to set up a local pipeline that reviews your pull requests before merging, running entirely on a local machine.
Can you upload a Figma screenshot and get a functional React component? We tested 50 diverse UI patterns to find out.