Your Mac can run more than you think. Here is exactly what.

This is the current go-to local coding model for a 32GB Mac, trading some raw code-quality against Qwen2.5-Coder-32B for much faster generation. Its 262K context window means it can hold a real codebase's worth of files in one session, which the dense coder can't do as cheaply.

The 32GB-Mac step up from the 7B vision model, with meaningfully better reasoning about multi-image and multi-step visual tasks. It's slower per image than the 7B, so batch document processing is where the extra memory cost pays off most.

Distilled from DeepSeek R1's reasoning traces onto a Qwen2.5 32B base, so on a 32GB Mac you get long chain-of-thought behavior without the 671B parent's memory bill. Expect verbose answers full of visible reasoning steps, which is either useful or annoying depending on the task.

One of the earliest MoE models to run well on Apple Silicon, needing a 32GB Mac for 4-bit despite having only 12.9B active parameters per token. It still runs faster than a dense 46B model would, but newer MoE designs like Qwen3-30B-A3B have mostly superseded it on quality.

Currently the strongest dense text model that a 32GB Mac can run at a usable quant, and its thinking mode holds up on genuinely hard reasoning benchmarks. If you have the memory budget, this outperforms Qwen3-30B-A3B on quality per token even though it's slower.

OpenAI's first open-weight release since GPT-2, shipped natively in 4-bit MXFP4 so it already fits a 16GB Mac without further quantization. It uses OpenAI's harmony chat format and a configurable reasoning effort setting rather than a simple thinking toggle.