I’ve been waiting on Qwen3-VL and finally ran the 4B on scanned tables, color-blind plates, UI screenshots, and small “sort these images” sets. For “read text fast and accurately,” ramp-up was near zero. Tables came out clean with headers and merged cells handled better than Qwen2.5-VL. Color perception is clearly improved—the standard plates that used to trip it now pass across runs. For simple ranking tasks, it got the ice-cream series right; mushrooms were off but the rationale was reasonable and still ahead of most open-source VL peers I’ve tried.
For GUI work, the loop is straightforward: recognize → locate → act. It reliably finds on-screen elements and returns usable boxes, so basic desktop/mobile flows can close. On charts and figures, it not only reads values but also doe…
I’ve been waiting on Qwen3-VL and finally ran the 4B on scanned tables, color-blind plates, UI screenshots, and small “sort these images” sets. For “read text fast and accurately,” ramp-up was near zero. Tables came out clean with headers and merged cells handled better than Qwen2.5-VL. Color perception is clearly improved—the standard plates that used to trip it now pass across runs. For simple ranking tasks, it got the ice-cream series right; mushrooms were off but the rationale was reasonable and still ahead of most open-source VL peers I’ve tried.
For GUI work, the loop is straightforward: recognize → locate → act. It reliably finds on-screen elements and returns usable boxes, so basic desktop/mobile flows can close. On charts and figures, it not only reads values but also does the arithmetic; visual data + reasoning feels stronger than last gen.
Two areas lag. Screenshot → HTML/CSS replication is weak in my tests; skeletons don’t match layout closely. Spatial transforms improved just enough to identify the main view correctly, but complex rotations and occlusions still cause slips. World knowledge mix-ups remain too: it still confuses Shanghai’s Jin Mao Tower with Shanghai Tower.
Variant behavior matters. The Think build tends to over-explain and sometimes lands wrong. The Instruct build stays steadier for perception, grounding, and “read + point” jobs. My pattern is simple: let 4B handle recognition and coordinates, then hand multi-step reasoning or code-gen to a larger text model. That stays stable.
Net take: big lift in perception, grounding, and visual math; still weak on faithful webpage replication and hard spatial transforms. As of today, it feels like the top open-source VL at this size.