WeVisOne-VL-4B : nouveautés, matériel requis, comment l'utiliser gratuitement

WeVisOne-VL-4B is a single model that can detect objects, count them, read text, and even produce full-resolution alpha mattes. It does all that with 4.8 billion parameters and a tiny 9.6 M-parameter pixel decoder.

According to the model card, WeVisOne-VL-4B is a vision-language model built on Qwen3-VL. It can understand images at the level of objects, detecting, counting, pointing, and reading text, and also at the pixel level, providing segmentation and matting. The model uses a 256 K token context, which lets it handle long prompts and detailed visual information. All of this runs on a single 4.8 billion-parameter network, so you only need one set of weights to cover many visual tasks.

The model introduces dedicated coordinate tokens that encode normalized bounding boxes and points directly in the token stream. After each predicted box, it emits a special instance token (ID 152678) to separate dense predictions. A lightweight pixel decoder with 9,609,764 parameters handles the heavy lifting for segmentation and alpha matting, using a visual feature adapter, a box-guided query decoder, and an instance-conditioned dense head. These pieces let the model switch between object-level and pixel-level outputs without a separate network.

According to Tencent, the model excels at a range of visual grounding tasks: open-vocabulary object detection, referring localization, counting, pointing, OCR, referring segmentation, and promptable alpha matting. It generates structured outputs directly, which makes inference efficient for single-turn image perception. However, the card notes that fine alpha boundaries can be fragile when the image resolution is low, when there is severe occlusion, complex transparency, or when the prompt phrasing is ambiguous. Video grids are also rejected by the pixel decoder, limiting it to still images.

The data sheet lists token-generation speeds for several consumer GPUs. On an RTX 3060 with 12 GB VRAM and 32 GB RAM, the model runs at about 64 tokens per second when fully loaded into GPU memory. A higher-end RTX 4090 reaches roughly 160 tokens per second, and the newest RTX 5090 pushes that to about 253 tokens per second. These numbers give a sense of how quickly the model can respond to prompts on typical home machines.

The model is aimed at research in visual grounding, referring localization, counting, OCR, segmentation, and matting. Because it runs on a RTX 4060 as the smallest machine that can hold it fully, hobbyists with mid-range GPUs can experiment without expensive hardware. Anyone looking for a free, Apache-2.0 licensed vision-language model that covers many tasks in one package will find it a useful addition to their toolkit.

Que faut-il pour le faire tourner chez vous ? En 4 bits, il prend environ 6 gigaoctets de mémoire. La plus petite machine personnelle qui le contient entièrement est RTX 4060. Sur RTX 3060 12 GB avec 32 gigaoctets de RAM, il écrit environ 64 tokens par seconde. Sur RTX 4090 avec 64 gigaoctets de RAM, il écrit environ 160 tokens par seconde.

Il n’existe pas encore d’API gratuite pour cela. Les poids sont ouverts : vous pouvez les télécharger depuis Hugging Face sous licence Apache 2.0. Arena ne l’a pas encore classé : nous ajouterons le score dès que les votes arriveront.

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