Although recent large multimodal models (LMMs) demonstrate impressive progress on vision language tasks, their alignment with human centered (HC) principles, such as fairness, ethics, inclusivity, empathy, and robustness; remains poorly understood. We present HumaniBench, a unified evaluation framework designed to characterize HC alignment across realistic, socially grounded visual contexts. HumaniBench contains 32,000 expert-verified image question pairs derived from real world news imagery and spanning seven evaluation tasks: scene understanding, instance identity, multiple-choice visual question answering (VQA), multilinguality, visual grounding, empathetic captioning, and image resilience testing. Each task is mapped to one or more HC principles through a principled operationalization of metrics covering accuracy, harmful content detection, hallucination and faithfulness, coherence, cross lingual quality, empathy, and robustness.We evaluate 15 state-of-the-art LMMs under this framework and observe consistent cross model trade offs: proprietary systems achieve the strongest performance on ethics, reasoning, and empathy, while open-source models exhibit superior visual grounding and resilience. All models, however, show persistent gaps in fairness and multilingual inclusivity. We further analyze the effect of inference-time techniques, finding that chain of thought prompting and test-time scaling yield 8 to 12 % improvements on several HC dimensions. HumaniBench provides a reproducible, extensible foundation for systematic HC evaluation of LMMs and enables fine-grained analysis of alignment trade-offs that are not captured by conventional multimodal benchmarks. https://vectorinstitute.github.io/humanibench/