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This page showcases comparative performance analyses of various Large Language Models (LLMs) specifically focused on their code-related capabilities, evaluating their effectiveness in understanding, generating, debugging, and documenting code across different programming languages and frameworks.

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Method

For the first round of this test, I’ll use useCoookies, a composable from one of my personal Nuxt 3 projects. It’s a TypeScript composable that fetches data from an endpoint that provides browser cookies in a JSON format. The code is working but is far from being scalable or maintainable:

import { useCookiesData, normalizeString } from "#imports";

export const useCookies = (): Cookies => {
	const get = async (id: string): Promise<Record<string, any>> => {
		try {
			id = normalizeString(id);
			return await useCookiesData(`/${id}`, {
				headers: {
					"Content-Type": "application/json",
					Authorization: "Bearer " + process.env.COOKIES_API_KEY!,
				},
			});
		} catch (error) {
			console.error(error);
			return {};
		}
	};

	return {
		get,
	};
};

Alternatively, you can download the code file here:

cookies.ts

Table of Contents

Models

The following models are used for each test with their default parameter:

OpenAI

  1. o1
  2. o1-pro-mode
  3. o3-mini
  4. o3-mini-high

Groq

  1. deekseep-r1-distill-qwen-32b