Recent advances in Large Multimodal Models (LMMs) have showcased impressive visual understanding and vision-language reasoning capabilities, yet their computational cost hinders practical deployment, especially in resource-constrained settings. A key bottleneck is the large number of visual tokens generated by its vision encoders, which increases latency and memory demands. Existing token reduction methods often require costly fine-tuning or apply fixed token reduction ratios, ignoring image complexity and vision-language interactions. We propose AdaptMerge, a training-free, inference-time token merging strategy that adaptively reduces visual tokens by leveraging feature diversity and language-guided relevance. By dynamically adjusting to image complexity and ensuring multimodal coherence, AdaptMerge significantly lowers floating-point operations while improving performance. Extensive experiments on Google’s latest Gemma 3 models (4B and 12B parameters) across four challenging benchmarks demonstrate that AdaptMerge outperforms state-of-the-art token reduction techniques, achieving both reduced computational costs and improved performance, thereby providing a practical pathway to more efficient LMMs.