2026 Qwen Model Comparison: Choose the Right Qwen for Your Mac Studio

1. Evolution of the Qwen family

From the dense‑parameter Qwen2.5 (72 B) released early 2025, through the hybrid dense‑plus‑MoE Qwen3 (235 B‑A22 B) in mid‑2025, to the 2026 Qwen3.5 series that uses a fully optimized MoE architecture and native multimodal support, each generation improves performance, efficiency and Mac compatibility.

Key points:

Qwen2.5: dense, 72 B flagship, high VRAM usage, slow inference.

Qwen3: dense+MoE, 235 B‑A22 B flagship, better performance but still sub‑optimal for Mac.

Qwen3.5: MoE‑only, four main variants, best cost‑performance for Mac Studio.

2. Detailed specs of the four Qwen3.5 variants

• Qwen3.5‑Plus (397 B total, 17 B active, context 262 k → 1 M tokens). After INT4 quantisation it occupies ~22 GB VRAM on a Mac Studio, delivering GPT‑4‑level reasoning, math, coding and professional Q&A.

• Qwen3.5‑122B‑A10B (122 B total, 10 B active, 262 k context). INT4 quantisation uses ~13 GB VRAM, 20 % faster than the Plus model, suitable for 64 GB‑memory Macs.

• Qwen3.5‑35B‑A3B (35 B total, 3 B active). Inference speed reaches 30‑40 tok/s, INT4 quantisation needs only 5 GB VRAM, runs comfortably on 32 GB‑memory Macs.

• Qwen3.5‑27B (dense, 27 B parameters, no activation‑parameter fluctuation). INT4 quantisation ≈14 GB VRAM, targets 64 GB‑plus Macs and users who prefer stable dense models.

3. Comparative ranking on Mac Studio (MLX INT4 quantisation)

Performance (high → low): Qwen3.5‑Plus > Qwen3‑235B‑A22B ≈ Qwen3.5‑122B‑A10B > Qwen3.5‑27B > Qwen3‑32B > Qwen2.5‑72B > Qwen3.5‑35B‑A3B.

Speed (fast → slow): Qwen3.5‑35B‑A3B > Qwen3.5‑122B‑A10B > Qwen3.5‑Plus > Qwen3‑32B > Qwen3‑235B‑A22B > Qwen2.5‑72B.

VRAM friendliness (low → high): Qwen3.5‑35B‑A3B (5 GB) < Qwen3.5‑122B‑A10B (13 GB) < Qwen3.5‑Plus (22 GB) < Qwen3‑32B (24 GB) < Qwen2.5‑72B (36 GB).

Key conclusion: Traditional 70‑72 B dense models consume more memory, run slower, and lag behind Qwen3.5‑Plus in performance; the MoE‑based Qwen3.5 series is the optimal choice for Mac Studio.

4. Model selection guide by Mac Studio memory

32 GB RAM – primary: Qwen3.5‑35B‑A3B; alternative: Qwen3‑8B/14B.

64 GB RAM – primary: Qwen3.5‑122B‑A10B; alternative: Qwen3.5‑Plus (may lag on long context).

96‑128 GB RAM (golden config) – primary: Qwen3.5‑Plus; delivers 15‑28 tok/s, smooth long‑context and multitask inference.

256 GB+ RAM – primary: Qwen3.5‑Plus; reaches 25‑45 tok/s, supports massive context and parallel models for enterprise‑grade use.

5. Real‑world inference speed on different Mac Studio configurations (MLX INT4)

M3 Max 64 GB – 8‑12 tok/s (barely usable, light chat).

M3 Max 96 GB – 15‑22 tok/s (smooth, productivity‑ready).

M4 Max 96 GB – 20‑28 tok/s (≈30 % faster than M3 Max).

M3 Ultra 256 GB – 25‑35 tok/s (near cloud‑level GPT‑4 experience).

M3 Ultra 512 GB – 30‑45 tok/s (professional‑grade, no latency).

6. Final recommendation

• For all‑round performance on a Mac Studio with ≥96 GB RAM, choose Qwen3.5‑Plus.

• For 64 GB RAM, pick Qwen3.5‑122B‑A10B.

• For low‑end Macs or scenarios demanding speed, pick Qwen3.5‑35B‑A3B.

• For users who insist on a dense architecture, select Qwen3.5‑27B.

The Qwen3.5 series eliminates the “high‑VRAM, slow‑speed” problem of earlier dense models, enabling local AI on Mac Studio with privacy and responsiveness comparable to cloud services.