Qwen 3 VL 235B-A22B

Overview

Alibaba's flagship Qwen 3 vision model: 235B MoE with 22B active parameters and a native 256k context that extends to 1M. The current open-weight vision leader.

When to pick this model

• Best-in-class open vision performance
• Long-context multimodal analysis (256k native, 1M extended)
• Document, chart, and video understanding at scale
• Apache-licensed alternative to closed multimodal APIs

VRAM requirements by quantization

Quantization	VRAM required
Q4_K_M (recommended)	142 GB
Q5_K_M	170 GB
Q8_0	250 GB
FP16 (no quantization)	470 GB

VRAM figures include model weights plus a typical 8k KV cache and ~600 MB runtime overhead (Ollama / llama.cpp baseline). Add headroom for higher context lengths.

Strengths

• Top open-weight vision model as of May 2025
• 262k native context, extensible to 1M tokens
• Apache 2.0 license
• Only 22B active parameters keeps inference tractable

Limitations

• Around 142 GB VRAM at Q4 — multi-GPU required
• Heavier operational lift than dense alternatives
• Overkill for simple captioning workloads

Architecture & training

Architecture: MoE vision · 235B total / 22B active · Qwen3-VL flagship

Training: Qwen3-VL 235B — text, images, video, 262k native context.

Quick start

ollama run qwen3-vl:235b

Or use the open-source MCP server to query this model from Claude Desktop, Cursor, or any MCP-compatible client.