When developers run artificial intelligence models locally on their own computers, every megabyte of memory matters, and only the models that actually work deserve to stay. A recent hands-on test pitted three of the most capable open-source large language models (LLMs) against each other in a real-world coding task, and the results revealed a stark divide between models that sound impressive on paper and those that deliver functional code.

Which Local AI Model Actually Produces Working Code?

To find out which model deserved precious system resources, developer Abhinav Raj tested Meta's Llama Scout 17B, Qwen3-Coder 30B, and Gemma 4 26B by asking each one to build the same Python game using Pygame, a popular game development library. The task was deliberately demanding: create a side-scrolling shooter where a player controls a ship, fires projectiles, and avoids enemies. The same prompt was given to all three models to ensure a fair comparison.

The reasoning behind choosing a game rather than a simple coding script was revealing. Games represent the most demanding everyday software most people actually use, because mistakes in code become immediately obvious. A sluggish menu is annoying, but inverted controls or misaligned collision detection makes a game feel broken the instant you play it. This makes games a far better test of whether a model can follow instructions and make sound judgment calls in areas the instructions didn't explicitly cover.

How Did Each Model Perform on the Coding Challenge?

Meta's Llama Scout 17B stumbled badly. The game crashed on launch due to a single misplaced dictionary in the code, referencing a player position value that was never created. Beyond that critical bug, the movement controls were inverted, with the left arrow sending the ship right and vice versa. Enemies that collided with the player weren't removed from the game, leaving no breathing room after a hit. The result was a game that was, in the developer's assessment, impossibly broken.

Google's Gemma 4 26B delivered a more polished attempt visually. The model added a parallax effect to the starfield, making distant stars appear smaller, dimmer, and slower while nearby stars moved faster and brighter, creating a sense of depth. However, the gameplay contained two significant bugs. The first allowed a single projectile to destroy two overlapping enemies and award points for both, since the collision detection loop didn't terminate after the initial hit. The second bug was worse: enemy projectiles could hit the player's ship without reducing lives at all, effectively removing the fail state from the game. This meant players could play indefinitely with no stakes and nothing to lose, fundamentally breaking the game experience.

Qwen3-Coder 30B produced something unexpected: a game that ran correctly without needing a single fix. Every part of the game state, from enemy and projectile lists to the score counter and collision loops, was initialized and integrated into a functional gameplay loop. The model had clearly been designed specifically for coding tasks, and the results reflected that specialization.

The finer details of Qwen3-Coder's implementation revealed careful attention to edge cases. When the developer deliberately forced two projectiles to hit the same enemy during the same frame, only one registered, preventing duplicate scoring. This was a problem that another equally capable model had failed to handle. The controls behaved as expected, collisions made logical sense, and the gameplay loop had actual stakes. The developer noted spending several minutes actually enjoying the game rather than hunting for bugs, which itself proved the quality of the implementation.

How to Choose the Right Local AI Model for Your Workflow

• Test with Real Tasks: Don't rely on benchmark scores alone; run models on actual work you need done, whether that's coding, writing, or analysis, to see which one produces usable output without requiring extensive fixes.
• Consider Resource Efficiency: A model that produces working code on the first try saves not just debugging time but also system memory and storage space, which are finite resources on local machines.
• Match Model Design to Your Use Case: Models trained specifically for coding tasks, like Qwen3-Coder, will outperform general-purpose models on specialized work, even if the general models have similar or larger parameter counts.
• Evaluate Edge Case Handling: Beyond basic functionality, test how models handle boundary conditions and overlapping scenarios that real-world code must handle, not just the happy path.

The three models tested are among the frontrunners in the local AI race, yet only one produced software that didn't immediately send the developer hunting for bugs. Qwen3-Coder was undeniably the most capable, but astonishingly, it also happens to be the least demanding in terms of system resources. The model's 30 billion parameters proved more efficient and accurate than both Llama Scout's 17 billion and Gemma 4's 26 billion parameters.

This finding challenges a common assumption in the local AI community: that bigger models always perform better. In reality, a model's training and specialization matter as much as its size. When it comes to coding tasks, there's an obvious answer, at least when you're running local AI on your own hardware.

For developers and professionals considering which local LLM to keep on their machines, the lesson is clear. Free access to powerful AI models is transformative, but storage, system memory, and especially graphics card memory are finite resources. Every model occupying your machine is competing for that space. Testing models on real work, not just benchmarks, reveals which ones actually deserve to stay.