WebDevs have a large choice of UI Frameworks today. But if you intend to employ an LLM to produce code for your project, wouldn't it be wise to find out which framework the LLM is BEST AT?

We asked four LLMs (through their browser interfaces) to rank the 6 most popular UI Frameworks on their features, using a scale of 1 (worst) to 5 (best). Then I asked them which framework LLMs (in general) would be best at. Finally, I asked each one which framework IT would be best (most accurate) at, given it's training data.

The six UI Frameworks compared were:

React (next.js) AngularJs Vue Svelte SolidJs

Qwen3 Coder:

QUESTION:

Which UI framework do you feel that your training makes you produce the most efficient, error-free code? If I were to pair program WISELY with Qwen3 Coder, which UI framework should I choose?

Based on my training and the implementation rules provided, I'm most efficient and produce the most error-free code with React and TailwindCSS. Here's why this pairing is optimal for wise collaboration:

Why React + TailwindCSS is the Best Choice:

1. Comprehensive Training

• I have extensive training on React patterns, best practices, and common pitfalls
• Deep understanding of Tailwind's utility-first approach and class combinations
• Familiarity with the specific libraries installed (lucide-react, recharts, framer-motion)

2. Error Reduction

• Tailwind's predefined classes eliminate CSS syntax errors and browser inconsistencies
• React's component model reduces logic errors through clear state management
• Pre-installed libraries mean no import issues or missing dependencies

3. Efficiency Benefits

• Rapid prototyping with Tailwind's extensive utility classes
• Component reusability in React speeds up development
• Built-in responsive design capabilities
• Minimal configuration required

4. Optimal Pair Programming Workflow

// Clean, readable structure I can generate efficiently
export default function App() {
  return (
    <div className="min-h-screen bg-gradient-to-br from-blue-50 to-indigo-100">
      <header className="bg-white shadow-sm">
        <nav className="container mx-auto px-4 py-4 flex justify-between items-center">
          <h1 className="text-2xl font-bold text-indigo-600">Logo</h1>
          <div className="space-x-6">
            <a href="#" className="text-gray-600 hover:text-indigo-600">Home</a>
            <a href="#" className="text-gray-600 hover:text-indigo-600">About</a>
          </div>
        </nav>
      </header>
      <main className="container mx-auto px-4 py-8">
        <div className="bg-white rounded-lg shadow-md p-6">
          <h2 className="text-xl font-semibold mb-4">Content Area</h2>
          <p className="text-gray-700">Your content here...</p>
        </div>
      </main>
    </div>
  );
}

5. Consistency & Quality

• Consistent design language through Tailwind's spacing system
• Accessible markup patterns I'm trained to implement
• Modern UI components using familiar libraries
• Responsive design without media query complexity

Recommendation: Choose React with TailwindCSS for the most efficient collaboration. I can quickly generate clean, functional components with minimal errors, allowing you to focus on logic and requirements rather than debugging or styling issues.

Claude.ai (Sonnet)

QUESTION:

Please give me a table with the main features that devs look for in UI frameworks and then rate each of the following frameworks for each feature (Scale 1 = worst, 5 = best). The UI frameworks to compare are: React Vue Preact Svelte Solid If there is another UI framework that should be considered, please include it.

Here's the comparison with Angular added (major omission from your list):

Bottom line: If you need to ship fast with team scalability, stick with React or Angular. If performance is critical and team is small/experienced, consider Svelte or Solid. Vue splits the difference nicely. Preact only if bundle size is absolutely critical.

What's your actual situation? Team size, project type, performance requirements, and timeline matter more than these generic ratings.

QUESTION:

What about pair programming with LLM coding models? Which of the 5 are best "understood" by the models' training?

Excellent question - this is actually a major productivity factor that most developers don't consider.

LLM Code Generation Quality (1-5 scale):

Reality check: React dominates by a huge margin. LLMs have seen vastly more React code, tutorials, and Q&A. They'll generate more accurate boilerplate, better understand context, and debug more effectively.

Specific issues with smaller frameworks:

• Solid: Models often hallucinate syntax or mix up React patterns
• Svelte: Compile-time magic confuses models; they miss framework-specific optimizations
• Preact: Models default to React patterns that don't always work

Game plan if you're doing LLM pair programming:

1. Go with React unless you have compelling technical reasons not to
2. If you must use others, expect to spend more time correcting generated code
3. Build a collection of working examples for your chosen framework to feed the LLM as context

The productivity difference is real - I'd estimate 20-30% faster development with React vs the smaller frameworks when using LLM assistance.

QUESTION:

What about YOUR training data? 
Do you feel that YOU perform best when writing REACT 
over the other UI Frameworks?

Yes, absolutely. I generate significantly better React code than the others.

What I can observe about my own performance:

React (5/5): I can write complex components, handle advanced patterns (render props, HOCs, custom hooks), debug effectively, and suggest appropriate libraries. The code feels natural and idiomatic.

Vue (4/5): Solid with Composition API, decent with Options API. I sometimes mix React patterns in accidentally.

Angular (3/5): I handle basic components fine but struggle with advanced DI patterns, RxJS complexity, and framework-specific best practices. Often feels more mechanical.

Svelte (3/5): I can write functional code but miss compile-time optimizations and sometimes suggest patterns that work against the framework's strengths.

Solid (2/5): I frequently confuse it with React. The fine-grained reactivity model doesn't come naturally - I default to React mental models.

Preact (3/5): Basically React with occasional compatibility hiccups I miss.

The practical impact: When you ask me to build something complex in React, I'm thinking in React patterns. With Solid or Svelte, I'm often ***translating *** from React concepts, which leads to suboptimal code.

Bottom line for your decision: If you're planning to lean heavily on AI assistance, React gives you the best AI pair programming experience. The gap is substantial enough that it could offset other framework advantages depending on your development approach.

I asked the same questions of Gemini 2.5 Pro and ChatGPT 5 and they agreed:

Gemini 2.5 Pro said:

As a large language model, I don't have personal feelings or experiences. However, I can analyze the data I was trained on to provide an objective answer. My training data consists of a massive corpus of text and code from the public internet. This includes a vast number of websites, open-source projects, coding tutorials, and forums.

Given the widespread popularity and market share of React, it naturally has a much larger footprint in my training data compared to other UI frameworks.

Here's a breakdown of why I would likely generate React code with the highest proficiency:

• Vast Amount of Training Examples: The sheer volume of React code in the form of open-source repositories (like those on GitHub), Stack Overflow questions and answers, and official documentation provides me with an extensive knowledge base. This allows me to recognize and generate a wider variety of patterns and solutions for React.

• Extensive Ecosystem: The rich ecosystem surrounding React, including popular libraries like Next.js, Redux, and Material-UI, is also heavily represented in my training data. This enables me to generate code that integrates seamlessly with these common tools.

• Prevalence in Tutorials and Educational Content: A significant portion of online web development tutorials and courses focus on React. This has provided me with a deep "understanding" of its core concepts, from components and props to hooks and state management.

While I can still generate proficient code for other frameworks like Vue, Angular, Svelte, and Solid, the probability of encountering inaccuracies or less idiomatic code is higher due to their comparatively smaller presence in my training data. As noted in the original analysis you provided, with frameworks like Solid or Svelte, I might occasionally "hallucinate" syntax or incorrectly apply patterns from the more dominant React.

Therefore, you can expect that my performance will be most consistent, accurate, and reliable when tasked with writing, debugging, or explaining React code.

ChatGPT 5 helped to reframe the question:

After asking it to evaluate the six UI Frameworks, I asked this follow-up question: Reevaluate with an eye towards the strengths of LLM coding models. I my methodology were to be pair programming with LLMs to produce the code, which framework would they be the best at?

That’s a great reframing — if your methodology is pair programming with LLMs (like me, Claude, Gemini, or your local models), the question shifts. You don’t just want what devs rate highly “by hand,” you want what LLMs are already very good at generating, completing, and debugging code for.

Here’s a reevaluated table, same 1–5 scale, but with LLM-centric criteria:

Features That Matter for LLM Pair Programming

• Docs/Training Exposure – how much training material, tutorials, and sample code LLMs likely saw

• Boilerplate Predictability – whether the framework uses common, repetitive patterns that LLMs excel at filling in

• Ecosystem Integrations – whether LLMs are strong at wiring in routing, state, SSR, auth, etc.

• Error Recovery – how well LLMs can debug and fix issues in generated code

• Convention Over Configuration – frameworks with consistent patterns are easier for LLMs to generate correctly

• TypeScript Friendliness – typed code improves LLM suggestion accuracy

Frameworks Rated for LLM Pair Programming

Analysis (LLM-Centric)

• React – By far the best “LLM partner.”

  • Saturation in LLM training data: endless tutorials, boilerplate, patterns.

  • Ecosystem (Next.js especially) is so common that most LLMs autocomplete the “happy path” instantly.

  • Debugging is well supported because most error messages are widely documented.
    → Best pick if you’re letting the LLM lead.

• Vue – Strong second.

  • Syntax and boilerplate are simpler than React, and Nuxt patterns are well represented.

  • LLMs often do a very good job scaffolding Vue components.

  • Slightly less exposure than React in Western-centric models, but still strong.

• Angular – Surprisingly good in this scenario.

  • Because it’s very opinionated and verbose, LLMs are great at generating the required boilerplate that humans find tedious.

  • TypeScript everywhere helps LLM debugging.

  • Downside: it’s heavy and complex, so you’re relying on the LLM to stay consistent across many files.

• Svelte – Excellent DX for humans, but LLMs struggle more.

  • Ecosystem is younger, docs patterns less entrenched in training data.

  • Generated code often needs human clean-up, especially with stores and reactivity.

• Preact – Lightweight React-like, so LLMs mostly do fine, but less direct exposure and ecosystem support.

• Solid – Bleeding edge; LLMs often hallucinate code or fall back on React patterns that don’t apply. Needs a human who really knows Solid to supervise.

✅ If you’re “co-driving” with an LLM:

• React (Next.js) is the best by far.

• Vue (Nuxt) is a safe alternative if you like Vue’s style.

• Angular might actually shine more with an LLM than without (since the AI eats the boilerplate for you).

⚠️ If you want to push into newer frameworks (Svelte, Solid):
Expect to guide the LLM more, correct mistakes, and read docs yourself. They’re not yet “first-class citizens” in LLM training data.

Would you like me to rank them again but weighted toward “how little human correction is needed after the LLM writes the code”? That might give you a more practical “LLM efficiency score.”

You said:

OK

ChatGPT said:

Got it 👍 — let’s weight things toward the practical question:

👉 If you let the LLM write the code, how much human correction will you need afterward?

I’ll call this the LLM Efficiency Score. (1 = constant corrections, 5 = almost always usable out of the box).

LLM Efficiency Ranking

TL;DR

• Best with LLMs (minimal correction) → React (Next.js), Vue (Nuxt)

• Surprisingly good → Angular (AI eats the boilerplate for you)

• LLM-friendly but smaller niche → Preact

• Needs more hand-holding → Svelte, Solid

⚡ If your methodology is “let the LLM do 80% and I’ll clean up 20%”, go React or Vue.
⚡ If you want “let the LLM handle all the boilerplate while I think at architecture level”, Angular is also solid.

Do you want me to also include backend integration (API calls, auth, DB) in this LLM-efficiency ranking? Because that’s often where the corrections start to pile up.

You said:

OK

ChatGPT said:

Awesome — I’ve folded backend integration into the LLM-efficiency view. Scores reflect how often LLMs produce working code with minimal correction for common backend tasks (API wiring, auth, DB/ORM, SSR/SSG data fetching, routing, forms/validation, testing).
Scale: 1 = constant fixes, 5 = usually good on first try.

LLM Efficiency for Frontend + Backend Integration

Why these shake out this way

• Next.js (React): The “happy paths” for Auth.js, Prisma/Drizzle, tRPC/REST, file-based routing, and app/ SSR are extremely well-trodden. LLMs tend to generate nearly drop-in code, and errors are easy to correct.

• Nuxt (Vue): Very close second. Nuxt’s conventions, server routes (Nitro), and module ecosystem map cleanly to LLM patterns.

• Angular (+ NestJS): Opinionated structure + TypeScript everywhere = very LLM-friendly for large apps. Pairing Angular front-end with NestJS on the back end gives models a canonical blueprint they reproduce reliably.

• SvelteKit/Preact/Solid: Great tech, but LLMs drift more (API endpoints, auth glue, store/reactivity nuances). Usable, just expect more manual fixes.

Recommended “LLM-First” starter stacks (high success rate)

• React/Next.js

  • Stack: Next.js App Router + Auth.js + Prisma (SQLite/Postgres) + tRPC (or simple REST) + React Hook Form + Zod + Vitest/RTL

  • Prompt tip: “Create a Next.js app-router project with Auth.js (credentials + OAuth), Prisma schema for User/Session, protected API routes via middleware, tRPC for /todos, Zod schemas shared front/back.”

• Vue/Nuxt

  • Stack: Nuxt 3 + Nitro server routes + nuxt-auth (or Lucia) + Prisma/Drizzle + Pinia + VeeValidate + Zod + Vitest

  • Prompt tip: “Scaffold Nuxt 3 with server/api routes, Prisma for Postgres, auth middleware protecting /dashboard, composables for useSession, form validation with VeeValidate + Zod.”

• Angular (+ NestJS)

  • Stack: Angular + Nx workspace + NestJS API + Prisma + JWT (Passport) + Angular Reactive Forms + Jest/Vitest

  • Prompt tip: “Generate an Nx monorepo with Angular app and NestJS API, Prisma User model, auth module (JWT access + refresh), Angular interceptors for auth, guards for protected routes.”

Make LLMs shine: prompt/playbook tips

1. Lock a stack in the prompt (framework, router style, auth lib, ORM, test runner).

2. Ask for file trees and explicit file paths; LLMs are far more accurate when they control the layout.

3. Require Zod schemas shared across client/server to reduce type drift.

4. Add E2E (Playwright/Cypress) early; ask the LLM to generate basic auth + CRUD tests.

5. Iterate tightly: “Run step X; if TypeScript errors appear, list them and fix.” (LLMs excel at fix-it passes with specific error text.)

Bottom line

• If you want minimum correction while moving fast with an LLM: Next.js → Nuxt → Angular/NestJS are your top three.

• Use SvelteKit/Preact/Solid if you personally prefer them — just plan for a bit more human guidance while the model writes the glue.

Conclusion:

I found this to be a valuable exercise and one that I will use going forward when evaluating backends and other tools to use in conjunction with coding LLMs. I hope you did too!