Hacker News Reader

Comment by ActorNightly | Hacker News Reader

Qwen is still better that Gemma though. Also you can tune it more for different tasks, which means that you can prioritize thinking and accuracy versus inference speed.

View on HN

Qwen is better at some things (code, in particular), but Gemma has better prose and better vision. At least, it feels that way to me.

gemma is also just way faster. i dont wanna wait 10min to get a 5-10% better answer (and sometimes, actually worse answer).

best is to use your own model router atm, depending on the task

I'm pretty sure Qwen is faster? The MoE version of Qwen is 3B active, while Gemma 4 is 4B active. Similarly, the dense Qwen is 27B while Gemma is 31B. All else being equal (though I know all else isn't equal), Qwen should be faster in both cases. I haven't actually measured with any precision, but on my AMD hardware (Strix Halo or dual Radeon Pro V620) they seem quite similar in both cases...both MoE models are fast enough for interactive use, both dense models are notably smarter but much slower, long time to first response and single-digit tokens per second once it starts talking.

I'm using both incessantly and having a great time.

qwen-3.6 is really interesting. The dense 27B model is pretty slow for me whereas the sparse 31B is blazingly fast but it also needs to be since it's so chatty. It produces pages and pages of stream of consciousness stuff. 27B does this to a lesser extent but slow enough that I can actually read it whereas 31B just blasts by.

I haven't yet compared either to Gemma 4. I tried that out the day after it came out with the patched llama.cpp that added support for it but I couldn't make tool calling work and so it was kind of useless. I should try again to see if things have changed but judging by what people say, qwen-3.6 seems stronger for coding anyway.

I had the same experience with 31B. Runs well on 4090 too!

Qwen without thinking is just as fast. I have 4 parameter settings based on recommendation. If you want a good coding problem, the thinking coding mode works well, but takes a while to arrive at an answer. If you want faster turn around time, instruction mode works without thinking.

Genuine question: how do you tune it?

I thought "fine-tuning" meant training it on additional data to add additional facts / knowledge? I might be mistaking your use of the word "tune", though :)

You can fine-tune relatively easily in Unsloth Studio.

Parameter settings are here. https://huggingface.co/Qwen/Qwen3.6-35B-A3B

Most clients that support ollama support passing extra body options where you can set those.

It’s a heck of a lot faster too.

Yes I would just go with qwen.

Reply on news.ycombinator.com