AIMv2, released in late 2024, is a family of open-vision encoders that has revolutionized multimodal learning with its novel multimodal autoregressive method. This approach treats image patches and text tokens as part of a unified sequence, using a causal multimodal decoder to predict elements sequentially. AIMv2 differs from CLIP in that it processes data as one continuous sequence, predicting the next step in the series, and deliberately puts image information first, followed by text. This sequential, image-first approach provides dense supervision, rich contextual understanding across modalities, efficient training with fewer samples, better multimodal synergy, and achieves stronger vision encoder capabilities. AIMv2 integrates into FiftyOne, enabling feature extraction from visual data, visualization of high-dimensional embeddings, zero-shot classification on diverse datasets, and streamlined multimodal analysis workflows. Its technical architecture uses a unified framework, prefix attention mask, SwiGLU activations, and RMSNorm normalization layers. The model is trained on 12 billion image-text samples, balancing human-written alt-text and synthetically generated captions from diverse sources.