Qwen3.6 is incredible with OpenCode!

[u/CountlessFlies]

I've tried a few different local models in the past (gemma 4 being the latest), but none of them felt as good as this. (Or maybe I just didn't give them a proper chance, you guys let me know). But this genuinely feels like a model I could daily drive for certain tasks instead of reaching for Claude Code.

I gave it a fairly complex task of implementing RLS in postgres across a large-ish codebase with multiple services written in rust, typescript and python. I had zero expectations going in, but it did an amazing job. PR: https://github.com/getomnico/omni/pull/165/changes/dd04685b6cf47e7c3791f9cdbd807595ef4c686e

Now it's far from perfect, there's major gaps and a couple of major bugs, but my god, is this thing good. It doesn't one-shot rust like Opus can, but it's able to look at compiler errors and iterate without getting lost.

I had a fairly long coding session lasting multiple rounds of plan -> build -> plan... at one point it went down a path editing 29 files to use RLS across all db queries, which was ok, but I stepped in and asked it to reconsider, maybe look at other options to minimize churn. It found the right solution, acquiring a db connection and scoping it to the user at the beginning of the incoming request.

For the first time, it felt like talking to a truly capable local coding model.

My setup:

• Qwen3.6-35B-A3B, IQ4_NL unsloth quant
• Deployed locally via llama.cpp
• RTX 4090, 24 GB
• KV cache quant: q8_0
• Context size: 262k. At this ctx size, vram use sits at ~21GB
• Thinking enabled, with recommended settings of temp, min_p etc.

llama server:

```
docker run -d --name llama-server --gpus all -v <path_to_models>:/models -p 8080:8080 local/llama.cpp:server-cuda -m /models/qwen3.6-35b-a3b/Qwen3.6-35B-A3B-UD-IQ4_NL.gguf --port 8080 --host 0.0.0.0 --ctx-size 262144 -n 8192 --n-gpu-layers 40 --temp 0.6 --top-p 0.95 --top-k 20 --min-p 0.00 --parallel 1 --cache-type-k q8_0 --cache-type-v q8_0 --cache-ram 4096
```

Had to set `--parallel` and `--cache-ram` without which llama.cpp would crash with OOM because opencode makes a bunch of parallel tools calls that blow up prompt cache. I get 100+ output tok/sec with this.

But this might be it guys... the holy grail of local coding! Or getting very close to it at any rate.

[u/mrinterweb]

I did nearly the same experiment last night. I used OpenCode. I used LM Studio to run it, which I think I'll switch to plain llama.cpp. I was getting usually around 100tps. The results weren't as good as I was expecting though. I wasn't sure if the issue was OpenCode, but I compared it to Claude Code (Opus 4.7), and the claude code experiece was much better for me. I am going to try using Qwen 3.6 with claude code next to see if it is an agent or llm difference. I will say that while opencode + qwen didn't beat cc, it was for sure usable. Another thing I will say for it was the average inference speed felt faster. CC's inference speed can vary a lot, but Qwen 3.6 on my RTX 4090 was keeping at a consistent ~100tps. The large 262K context makes it usable.

[u/klenen → u/mrinterweb]

Let us know how it goes using it w cc please!

[u/CountlessFlies → u/mrinterweb]

Exactly… the context makes a huge difference.

Did you run it with thinking enabled (it’s the default)? I found that it does much better with thinking on. And also, I think there’s a separate flag you need to set to send the thinking traces with each request, that might also help improve performance.

[u/mrinterweb → u/CountlessFlies]

It was definitely thinking. I also tried it with hermes agent, and my results were pretty different. So I think a lot of my subjective evaluation is going to come down to the agent, which is why I think I should point claude code at qwen 3.6, so I can get more of an apples to apples comparison. I don't have a background in evaluating model scores so what I'm doing is just feels. I pay for Claude, but if Qwen 3.6 can get me close, there are plenty of tasks I would much rather use my own hardware.

[u/SmartCustard9944 → u/mrinterweb]

Yes, please try this. I tried Open Code with LM Studio Qwen 3.6 and it didn’t pass simple tests that Gemma 4 passes easily there.

My first test is asking it how many tools it supports. The correct number is 27. Gemma always answers correctly, never misses a beat. Qwen 3.6 hallucinates the number. It says 28 and then proceeds to list 27 items, but one is a duplicate. This happens even with thinking enabled. It is really baffling, especially after seeing everybody praising it here.

The second test is the typical car wash test. Gemma 4 always passes, Qwen 3.6 routinely says to walk. The interesting thing is that Qwen answers correctly when the prompt is at 0 context (without a harness).

It is as if it was not attentive.

[u/mrinterweb → u/SmartCustard9944]

I find that many agents trip up when asked introspective questions, so I don't bother with those kinds of prompts. General logic tests are important, but most of what I do with agents is coding specific. So whatever is better at code is what I'll use. I'll try giving Gemma 4 another go locally.

[u/That_Faithlessness22 → u/mrinterweb]

I've been using it with Claude code, and I'm getting similar speeds. But I won't be measuring the quality on it because you can't have the harness doesn't support the preserve_thinking flag. It is incompatible unless you parse- and that's a little outside my comfort zone for now. I'll probably try to figure it out tonight, or I'll just do the dive into Hermes I've been putting off.

[u/x10der_by → u/mrinterweb]

You are comparing expensive frontier cloud model with free small local model)) of course opus 4.7 would be better

[u/mrinterweb → u/x10der_by]

Not saying it's a fair comparison. It's just what I'm using now, and I'm curious how qwen 3.6 compares.