The only reason Qwen is more popular is because it can run decently fast on MLX, while Gemma needs a discrete GPU with 24gb of ram to run at usable speeds.

Also, theoretically speaking, any small model can be as good as a big model if all they have to do is summarize text. Back before agents were a thing, we had prototype coders running of local inference models from Mistral for ai dev. The workflow was to generate llm summaries of the codebase, and a summary of changes to be made. Then we would grab search results from the web, feed it to an llm to summarize, and then finally combine all of that context into a final inference run which would generate code, then the codebase would be run automatically, any errors would be llm analyzed, and an llm would generate the next change to be made in the above cycle.

Nowdays, we can do that with pi coding agent and Gemma4:31b. But we still have plenty of custom harnesses for things.