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+This paper presents PaLI-3, a smaller, faster, and stronger vision language model (VLM) that compares favorably to similar models that are ten times larger. As part of arriving at this strong performance, we compare Vision Transformer (ViT) models pretrained using classification objectives to contrastively pretrained ones (SigLIP). We find that, while slightly underperforming on standard image classification benchmarks, SigLIP-based PaLI shows superior performance across various multimodal benchmarks, especially on localization and visually-situated text understanding. We scale the SigLIP image encoder up to two billion parameters and achieves a new state-of-the-art on multilingual cross-modal retrieval. We hope that PaLI-3, at only five billion parameters, rekindles research on fundamental pieces of complex VLMs, and could fuel a new generation of scaled-up models.