Qwen3.5 vs Qwen3 Benchmarks Visualized

🔑 Enhanced Key Takeaways

• •Qwen3.5 models utilize Mixture-of-Experts (MoE) architecture, such as Qwen3.5-35B-A3B activating only 3B of 35B total parameters per token, enabling superior performance to larger predecessors at lower inference cost[1][3].
• •Qwen3.5-397B-A17B achieves 19x faster decoding on 256k token contexts compared to Qwen3-Max while matching reasoning and coding benchmarks[2].
• •Models are released open-weight under Apache 2.0 license on platforms like Hugging Face, supporting unrestricted fine-tuning and deployment[1].
• •Qwen3.5 excels in multimodal tasks, scoring 90.8% on OmniDocBench v1.5 (outperforming GPT-5.2 and Claude Opus 4.5) and 67.5 on ERQA embodied reasoning[2].

🛠️ Technical Deep Dive

• •Qwen3.5-35B-A3B: 35B total parameters, 3B active (8.6% activation), MoE with hybrid attention, runs on 8GB+ VRAM GPUs via GGUF quantization[1].
• •Qwen3.5-397B-A17B: 397B total, 17B active, fuses Gated DeltaNet linear attention with high-sparsity MoE, supports FP8 precision and heterogeneous parallelism[3].
• •Qwen3.5-122B: Activates 10B of 122B parameters, leads in agentic benchmarks like BFCL-V4 (72.2), fits on NVIDIA DGX Spark[1].
• •Multimodal capabilities via native training for image/video/document tasks, expanded to 201 languages with larger vocabulary[3].

🔮 Future ImplicationsAI analysis grounded in cited sources

⏳ Timeline