Qwen 3.5 Local Run Size Poll

🔑 Enhanced Key Takeaways

• •Qwen3.5 includes sparse models like 35B-A3B (3B active parameters) and 122B-A10B (10B active parameters), outperforming larger predecessors through improved architecture and data quality.
• •The series features a flagship 397B-A17B model with 17B active parameters, positioning it as the smallest in the Open-Opus class while competing with models like Kimi's 400B.
• •Qwen3.5-27B dense model achieves 72.4 on SWE-bench Verified, tying GPT-5 mini, and excels in agentic benchmarks like BFCL-V4 (72.2) for the 122B variant.
• •Hosted Qwen3.5-Plus offers a 1M context window and built-in tools via Alibaba Cloud, with pricing starting at $0.10 per million tokens for Flash.

🛠️ Technical Deep Dive

• •Qwen3.5-35B-A3B: 35B total parameters, 3B active (sparse MoE-like routing per token), runs on 8GB+ VRAM GPUs with GGUF quantization.
• •Qwen3.5-122B-A10B: 122B total, 10B active parameters, leads in agentic tasks (BFCL-V4: 72.2, BrowseComp: 63.8, Terminal-Bench 2: 49.4).
• •Qwen3.5-397B-A17B: 397B total, 17B active parameters, ~4.3% sparsity ratio, native multimodality and spatial intelligence features.
• •Qwen3.5-27B: Dense model, competitive in coding (tops charts in local benchmarks) and medium-sized evaluations.

🔮 Future ImplicationsAI analysis grounded in cited sources

⏳ Timeline