Gemma 4 beats Qwen 3.5 (UPDATE), and Qwen 3.6 27B + MiniMax M2.7 is the best OpenCode setup

Reddit r/LocalLLaMA / 4/23/2026

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The author reports using Gemma 4 E4B to replace Qwen 3.5 4B for semantic routing and classification, claiming it performs better for routing and English understanding while being less coding-smart than Qwen 3.5.
After Qwen 3.6 released, they found Qwen 3.6 (30B/27B) generally outperforms Gemma 4 (26B/31B) and Qwen 3.5 (30B/27B), and they now use Gemma 4 only for semantic routing.
A “breakthrough” comes from using downloaded MiniMax M2.7 MXFP4 weights to replace very large Qwen 3.5 models (122B Q8 and 397B Q2), describing it as an effective middle-ground without major issues.
They installed OpenCode, configured a llama-swap server to switch between Qwen 3.6 30B and MiniMax M2.7 (using a GGML unified memory trick), and found it yields strong results for planning/editing workflows, with Qwen 3.6 showing good tool/command compatibility.
The post frames the setup as a meaningful step toward replacing paid coding assistants (e.g., Claude Code Pro), emphasizing that recent open-source model progress makes practical “daily driver” coding with local models increasingly viable.

Hi all! I recently made a post about how Gemma 4 managed to replace Qwen 3.5 for me, for semantic routing and a lot of coding stuff and ultimately it was my new daily driver.

The next day, Qwen 3.6 released and I've been using it a lot this week. Here's my ultimate comparison:

Gemma 4 E4B > Qwen3.5 4B for routing and other classification tasks, I think it might be better at English understanding but might not have super technical smarts like coding

Qwen 3.6 30B & 27B > Gemma 4 26B and 31B (both)> Qwen 3.5 30B & 27B

Specifically, my light/fast model went through the following changes

Qwen 3.5 30B --> Gemma 4 26B -> Qwen 3.6 30B

Gemma 4 26B also temporarily replaced my use for Qwen 3.5 27B (dense), until 3.6 came out (now I use them interchangeably)

The only Gemma model I use now is E4B for semantic routing.

NOW, here's a new breakthrough:

I recently downloaded weights to MiniMax M2.7 MXFP4 and used it to replace Qwen 3.5 122B Q8 and Qwen3.5 397B Q2. It's the perfect middle ground and I haven't had any issues.

I'm trying to break away from my Claude Code Pro subscription, I normally use Sonnet 4.7 for all of my projects (never bother with Opus as it burns up my usage) and I rarely touch Haiku unless it's a stupid easy task.

This morning I installed OpenCode and set up my llama-swap server to swap between Qwen 3.6 30B, and Minimax M2.7 (with the GGML unified memory trick) and it's been AMAZING and I'm going to continue testing further. You do need to handhold it a bit, but it's been giving great results.

I haven't set up any agents yet, I've just been manually switching between the models but I've found that Qwen 3.6 30B is great for the planning mode, and have MiniMax M2.7 lay all the groundwork. Then back to Qwen 3.6 30B for edits.

I'm using the Q_8 unsloth quant of Qwen 3.6 30B and I have yet to have it give me any tool/command issues whatsoever through open code. MiniMax M2.7 tried to manually tell me what to do until I gently reminded it that it had the power to do it itself. Whatever tuning happened between 3.5 and 3.6 seemed to really make it do better with tool calling and knowing when to use tools.

It's a very good day to code with open source models! 2-3 years ago I remember struggling to replace ChatGPT with CodeLlama 34B, the amount of progress we've made is amazing.

Any questions lmk!

2x RTX 3090 + 1 P40 and 128GB of DDR4

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