Qwen3 vs Qwen3.5 Benchmark Showdown

🔑 Enhanced Key Takeaways

• •Qwen3.5-397B-A17B employs a hybrid architecture combining Gated DeltaNet linear attention with high-sparsity MoE, activating only 17B parameters for efficiency while supporting 1M-token context and 201 languages[1].
• •Qwen3-Max-Thinking integrates reinforcement learning and adaptive tools for dynamic search, memory, and code use, enhancing multi-stage reasoning over prior Qwen3 models[1][6].
• •Qwen3-Coder-Next (80B total, 3B active) outperforms larger rivals like DeepSeek V3.2 on coding via Gated DeltaNet + Gated Attention hybrid and 262k native context[3].
• •Qwen3.5 adopts the hybrid attention from Qwen3-Next series into mainline models, boosting agentic coding performance to match GLM-5 and MiniMax M2.5[3].

🛠️ Technical Deep Dive

• •Qwen3.5 flagship: 397B total params, 17B active MoE; Gated DeltaNet linear attention + high-sparsity experts; FP8 precision, heterogeneous parallelism[1].
• •Qwen3-30B-A3B: 30.5B total, 3.3B active; dual-mode (thinking/non-thinking) for reasoning/math/coding vs. dialogue; 100+ languages, agent tool integration[2].
• •Qwen3-Coder-Next / Qwen3-Next: 80B total, 3B active; 4x experts + shared expert; Gated DeltaNet + Gated Attention hybrid for 262k ctx (vs. 32k prior)[3].
• •Qwen3-Max-Thinking: RL-enhanced reasoning, adaptive tools (search/memory/code); outperforms GPT-5.2-Thinking/Claude-Opus-4.5/Gemini 3 on 19 benchmarks[6].

🔮 Future ImplicationsAI analysis grounded in cited sources

⏳ Timeline