I have been testing and using Qwen3.6 27B (running from my 3090) since it dropped and I genuinely think this is the first consumer hardware-grade model that can actually replace frontiers for a lot of workloads.

I ran 8 tests on a variety of open-weights models, and opus 4.7 (1mil ctx version) and the little dense model was right behind it: https://github.com/sleepyeldrazi/llm_programming_tests/tree/... Of note is that opus was the only model to push back against the spec on the hardest challenge, saying 'thats not possible', when there are links in the spec to examples of it being done.

There may be problems with the mlx versions, as i haven't had any looping in all the testing i've done, which is all my agentic and coding work the last couple of days (since it dropped). I have had tool_call misses 4 or 5 times so far, which isn't ideal but no looping. First I used it in pi-mono and later when i realized it's a serious model switched to opencode.

My setup is llama.cpp running on a 3090 in WSL, unsloth IQ4_NL with those flags: --ctx-size 128000 \ --jinja \ --temp 0.6 \ --top-p 0.95 \ --top-k 20 \ --min-p 0.0 \ --repeat-penalty 1.0 \ --presence-penalty 0.0 \ --threads 12 \ --gpu-layers 99 \ --no-warmup \ --no-mmap \ -fa on

Running it on a Macbook Pro M5 48GB:

        -hf unsloth/Qwen3.6-27B-GGUF:UD-Q6_K_XL \ 
        -c 128000 \
        --parallel 1 \
        --flash-attn on \
        --no-context-shift \
        --cache-type-k q8_0 \
        --cache-type-v q8_0 \
        --temp 0.6 \
        --top-p 0.95 \
        --top-k 20 \
        --min-p 0.0 \
        --presence_penalty 0.0 \
        --reasoning on \
        --jinja \
        --chat-template-kwargs "{\"preserve_thinking\": true}" \
        --spec-type ngram-simple \
        --draft-max 64 \
        --timeout 1800