Qwen models lead local vision AI benchmark across hardware tiers

A recent benchmark update for local vision models reveals Qwen3.6 27B (nothink) at Q4 quantization as the top performer for systems with 24GB+ VRAM, achieving a score of 79.6/100. For mid-tier hardware (12-16GB VRAM), Qwen3-VL 8B at Q8 quantization is recommended, while smaller setups (4-8GB VRAM) benefit most from Qwen3.5 4B (nothink) at Q4. The benchmark also highlighted that "thinking" modes generally degrade vision performance, and Mixture-of-Experts (MoE) models do not offer advantages for visual tasks compared to dense models of similar size. AI

IMPACT Provides clear recommendations for optimal local vision model deployment across different hardware configurations.

RANK_REASON New benchmark results for local vision models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on r/LocalLLaMA →

• Gemma 4
• llama.cpp
• Qwen3.5 4B
• Qwen3.6 27B
• Qwen3-VL 8B

• model release
• product

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