Anyone tried these two for coding or agentic tasks? How do they compare? I know 27b is a dense one and very smart, but what is your experience and opinion?
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You’re comparing apples and oranges. Qwen3.5 27B is a dense model. Gemma4 26B is a mixture of experts model with 4B parameters activated at once. The equivalent would be Gemma4 31B, which is the Gemma4 dense model.
Both dense models are EXTREMELY good. From my testing, they can code and work agenticly with similar performance as a cutting edge model (Gemini, ChatGPT, Claude Sonnet) from 4-5 months before their release. Usually, Gemma models are better at prose and the Qwen model has scores a little bit better on coding and logic tests. These models being dense, they require more computation and memory bandwidth than mixture of experts (moe) models, which means they’re slower or more expensive to run.
If you purely are comparing the models you originally listed, the Qwen model will crush Gemma4 26B but it will run at a quarter of the speed. :)
Thank you for the reply Yes moe models are much faster and usually proportionately dumber… I run some tests and the 26b results are unexpectedly good, hence was the question .
https://www.glukhov.org/ai-devtools/opencode/llms-comparison/
Apples and oranges- don’t care how they are organised internally when they deliver quality output. Faster better, but if they don’t deliver - who cares.
That’s a cool write up! I suspect a lot of this will have to do with quantization, how much quality is getting lost, and how each model behaves under different quants.
I’ve been impressed with Qwen 3.5 122B - can’t speak for the lower size one though. Very good for agentic, on par with Sonnet IMO. Excited for the 3.6* release.
You mean 3.6? I tried it with opencode, with the model hosted on opencode zen - very smart and fast… Hopefully they will release its smaller variant
