Vision-Language Models (VLMs) are increasingly central to generative AI applications, offering developers scalable and customizable solutions. This blog compares three open-source VLMs—Gemma-3-4B, MiniCPM-o 2.6, and Qwen2.5-VL-7B-Instruct—using benchmarks to assist in selecting the right model based on output quality, latency, throughput, and infrastructure costs. The benchmarks, performed using Clarifai's Compute Orchestration on NVIDIA L40S GPUs, reveal that each model excels in different areas: Gemma-3-4B is ideal for text-heavy tasks with some image input, MiniCPM-o 2.6 offers balanced performance across modalities, and Qwen2.5-VL-7B-Instruct is optimized for tasks requiring precise visual and textual understanding. The blog emphasizes the importance of considering specific workload needs and deployment environments when choosing a model, noting that performance may vary with different hardware configurations. Additionally, it invites users to explore these models through a new AI Playground for hands-on experience and offers support for scalable deployment on dedicated compute resources.