Can Local AI Replace Your $20 Subscription? Gemma 4 Put to the Test

Google just dropped Gemma 4, a new multi-model you can run locally, and I wanted to answer the question every developer is asking: can you finally ditch that $20/month subscription to Claude, Copilot, or ChatGPT?

I'll save you the suspense — the answer is a pretty resounding no. But there's a caveat, and it's a big one: Gemma 4 has gotten really, really good.

What We're Working With

We're talking about a 27 billion parameter model versus state-of-the-art frontier models hosted in massive data centers. Locally, Gemma cannot compete with that. But let's see how close it gets.

Hugging Face has a full breakdown of everything new in Gemma 4. There are several sizes available — I went with the Gemma 4 27B A4B, a mixture-of-experts model with 4 billion parameters activated at once. The bigger 31B dense model was pushing my system limits, so this was the sweet spot.

My setup:

• LM Studio running the 8-bit quantized version
• VS Code with Cline for the coding workflow
• A fresh folder called pong-gemma

The Test: Build Pong From Scratch

I kicked Cline into plan mode and gave it a straightforward prompt:

Build a simple game of Pong where the player competes against CPU. Use Node to implement this. Game should start when npm start is run and serve on localhost:3000. There should be a selection of easy, medium, hard modes, and the ball movement should not be tied to the frame rate.

The model started thinking, wrote up a plan, created its own task list, and then I switched it to act mode. It created a package.json, ran npm install, implemented server.js, wrote the HTML, pulled in styles, and built out game.js — all on its own.

Watching it work honestly reminded me of the GPT-3.5 days, sitting there watching code scroll by line-by-line, except now this is all running locally on my hardware. Nothing leaving my machine. Fully private. That's genuinely wild.

It even ran npm start when it was done.

The Result

I clicked the link and... Pong loaded. Difficulty selection screen with easy, medium, and hard modes. The ball moved, the paddle followed my mouse, and the game played. It wasn't perfect — I didn't specify mouse vs. keyboard controls, and the ball physics felt a bit off when hitting the paddle edges — but it worked.

After one follow-up prompt about paddle deflection angles (hit the edge of the paddle, change the ball direction), Gemma fixed the math and the game felt noticeably better. You could actually aim shots by catching the ball on the paddle's edge.

The Real Test: Code Review by Frontier Models

Here's where it gets interesting. I asked Claude to evaluate Gemma's implementation, and the findings were revealing:

• AI difficulty only partially implemented — the code computes difficulty using an error margin but never actually uses it. The CPU follows the ball directly regardless of difficulty setting. Easy, medium, and hard only differ by paddle speed, not tracking quality.
• Paddle collision only checks center against paddle height
• Wall bounces reverse velocity but don't clamp the ball position back inside bounds
• Signs of unfinished cleanup throughout the code

Can Gemma Find Its Own Bugs?

I went back to Gemma and asked it to analyze its own code. It found some things — collision tunneling risks, CPU predictability, some performance concerns — but it didn't catch the actual bugs that Claude found, like the unused difficulty logic that made the CPU unbeatable.

Only when I specifically called it out — "is there unused code making the CPU unbeatable?" — did it recognize the issue.

Claude Code, when asked the same question cold, immediately found the inverted paddle bounce angle, the unused CPU error margins, and the unclamped wall collisions. Same findings as the first review, no hand-holding needed.

The Verdict

You're not cancelling your $20 subscription. Not for serious work. The gap between a local 27B model and frontier models is real and obvious, especially when it comes to self-evaluation and catching subtle logic bugs.

But here's the thing — for a locally-running model that keeps everything on your machine, this is genuinely impressive. It built a playable game from a single prompt and iterated on feedback. With the right workflow — run an implementation pass, clear the context, come back with targeted queries for specific categories like physics, AI behavior, or edge cases — you could iteratively build something that feels pretty solid.

The fact that we have this type of intelligence boxed and locally accessible on our own hardware is amazing.

Gemma 4 isn't replacing your frontier model subscription. But it's a remarkably capable tool for private, local development — and it's only going to get better.