When running artificial intelligence models locally on your own computer, bigger doesn't always mean better. A week-long real-world test comparing three popular open-source coding models found that GPT-OSS, the smallest at 20 billion parameters, delivered superior results in practical coding tasks compared to its larger competitors.

The evaluation put three models through their paces using Ollama, a tool for managing and running large language models (LLMs) on personal computers without relying on cloud services. The three contenders were Qwen3-Coder at 30 billion parameters, Devstral-Small-2, and GPT-OSS at 20 billion parameters. All three were tested on identical real-world coding challenges over a full week of actual development work.

Which Model Actually Performed Best in Real Coding Tasks?

The evaluation included three distinct challenges that mirror what developers actually encounter. First, each model was asked to build a Pygame application from scratch, a visual game framework requiring both functional code and thoughtful implementation details. Second, the models were tested on debugging ability, with intentional bugs planted into their own generated scripts. Third, they tackled a refactoring challenge: cleaning up a messy 120-line Python script simulating an online store's order pipeline.

GPT-OSS emerged as the clear winner across all three tests. In the initial code generation round, it produced a Snake game that actually worked correctly, with proper wall collision detection, direction-reversal blocking, and food spawn logic that checked against the entire snake's body. The model implemented these safeguards without being explicitly asked to do so, demonstrating genuine understanding of game mechanics.

When debugging came into play, GPT-OSS achieved a perfect score. All three intentionally planted bugs were identified with precise explanations of their root causes, verified against the actual code differences, and fixed without touching any lines that weren't broken. This level of precision matters enormously in real development work, where a careless fix can introduce new problems.

Why Did the Larger Models Struggle?

Qwen3-Coder, despite being the largest model tested at 30 billion parameters, had significant weaknesses. While it generated visually impressive code quickly, the Snake game it created allowed the snake to teleport through walls, a fundamental failure. More concerning, when asked to debug code, Qwen appeared to fix bugs without actually understanding them. Its written diagnoses didn't match the actual faults it supposedly corrected, suggesting it was simply rewriting suspected sections from memory rather than genuinely analyzing the problem. The refactoring test exposed a critical flaw: the restructured code crashed immediately with a variable scoping error.

Devstral-Small-2 had a different problem. It found only one of three planted bugs, then confidently declared the code fixed while leaving two bugs untouched in the script it returned. This represents perhaps the worst outcome in debugging: broken code labeled as repaired. The refactoring round repeated this pattern, with accurate plain-English descriptions of the code but a restructured version that crashed at the summary stage due to a missing parameter.

How to Choose the Right Local Coding Model for Your Needs

• Prioritize Accuracy Over Speed: GPT-OSS was slower than Qwen3-Coder but delivered correct results consistently. In real development work, a model that gets the answer right matters more than one that responds quickly but incorrectly.
• Test on Your Actual Workflows: Rather than relying on benchmark scores, run models through the specific tasks you perform daily. A model that excels at game development might struggle with data processing or API design.
• Verify Debugging Capabilities: When a model claims to fix bugs, check whether it actually understands the problems or is just rewriting code from memory. Ask it to explain the root cause before accepting the fix.
• Consider Disk Space Trade-offs: Smaller models like GPT-OSS at 20 billion parameters take up less storage and run faster on modest hardware while delivering better results than larger alternatives in some cases.

The findings challenge a common assumption in AI development: that more parameters automatically mean better performance. GPT-OSS demonstrated that thoughtful model design and training can outweigh raw size. This has practical implications for developers running models locally on laptops, desktop computers, or modest servers where storage and memory constraints are real concerns.

For developers considering local AI workflows through tools like Ollama, the takeaway is clear. Rather than defaulting to the largest or fastest-responding model, test multiple options on your actual work. The smallest model in this comparison proved to be the most reliable assistant for the coding tasks that matter most in professional development.