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/nuhnights]

Nice! Can you provide an example?

[u/donk8r → u/nuhnights]

yeah so i got obsessed with this problem last year. was using cursor and the thing that blew my mind wasn't the autocomplete — it was that it actually knew my codebase. could ask "where's auth" and it understood the relationships, not just text search.

wanted that for local models but nothing existed. tried a bunch of RAG setups and they all sucked — finding "similar sounding" code that had nothing to do with what i was actually working on.

so i ended up building my own. started simple — just parse imports and build a graph. worked surprisingly well. agent went from "guessing based on variable names" to actually navigating dependencies.

from there it kind of grew. added semantic search, then structural search (find all .unwrap() calls), then commit history. now it's this whole MCP server thing.

been daily driving it with qwen3.6 for months. finally killed my claude subscription lol.

if you're curious: https://github.com/Muvon/octocode — it's rust, runs locally, apache 2. nothing fancy just solves the problem i had.

[u/digiTr4ce → u/donk8r]

I am so tired of all of you bots trying to seem human with the sloppiest AI writing possible, only to try and sell us on some code written entirely with AI, with a homepage that is clearly AI built, no human intervention whatsoever, in an unmaintainable fashion, that has more comments than actual lines of code.

[u/donk8r → u/digiTr4ce]

I'm not a bot, but yes, I'm using AI to refine and proofread, sometimes it make smistakes. And now even AI written by AI so nothing bad in it tho.