u/AIForOver50Plus

Pub (brewery) stops in between rides. I did Rt 29 to 216, to Lime Kiln to Browns Bridge Rd, to New Hampshire Ave to 108, stopped off at Sapwood Brewery & then back to 29 to home! Great ride on the 821 Monster

Will they tell us if find out if our kids information is compromised?

Spent two days benchmarking three Qwen3.6 variants against gpt-oss:120b on my dev rig MBP M3 Max with Ollama. A few findings worth sharing for anyone running Ollama in production-shaped workflows.

Speed (temp 0.2, --think=false, structured-output research-brief workload):

qwen3.6:35b-a3b-coding-nvfp4    6s   (21 GB)
qwen3.6:35b-a3b-q8_0 (MoE)     22s   (38 GB)
qwen3.6:27b-q8_0 (Dense)       67s   (29 GB)
gpt-oss:120b                   61s   (65 GB)

Ollama-specific findings:

• --think=false is honored by all three Qwen3.6 variants. It is silently ignored by gpt-oss. Same flag, same Ollama version, different runtime behavior. gpt-oss still runs full reasoning and dumps it to stdout. If you pipe Ollama output to anything that parses it, you have to engineer around the trace bleed for gpt-oss. Qwen3.6 just works.
• Modelfile overlays cost zero disk. I tuned each model with FROM model + PARAMETER temperature 0.2. ollama create reuses content-addressable layers — only a tiny manifest is new. Confirmed by watching ollama create reuse 50+ existing layer hashes. Disk-free tuning is a real feature.
• MoE 35B-A3B beats 27B dense by 3x on the same workload. Active-parameter count drives per-token speed once the model fits. On Apple Silicon unified memory, this matters a lot.

Operational gotcha I almost missed:

The text-only coding-NVFP4 will hallucinate image descriptions silently when given an image via the API. Not error, not refuse — fluent, confident, completely fabricated description. Build a routing-layer allowlist for which models can take images: input. Do not rely on the model to refuse on its own. It will not.

Full methodology, Bash benchmark script, all model outputs, and chart:

https://www.fabswill.com/blog/replacing-gpt-oss-with-qwen3-6-on-macbook-pro/?utm_source=reddit&utm_medium=social&utm_campaign=qwen-vs-gptoss

Disclosure: my blog. AI-assisted writeup, methodology and findings are mine.