Multimodal image matching establishes accurate correspondences between images captured by different sensors or imaging modalities. However, this task poses significant challenges due to substantial variations in radiometric properties, structural patterns, and texture distributions across modalities. To address these issues, this paper proposes FFMatch, a FilterFormer-based network designed for accurate multimodal image matching. Specifically, the Multi-Receptive Field Feature Aggregation module is introduced to enhance the model’s perception of multi-scale structural information and its capability for context modeling. The Token Filtering module is designed to dynamically model the spatial importance of features, enabling effective compression and filtering of redundant tokens. The Cross-Modal Alignment module is constructed based on a dual-stage cross-attention mechanism to improve structural consistency and information exchange across modalities. Furthermore, the Multi-Expert Fusion module is incorporated to enhance the model’s adaptability and discriminative ability in handling non-rigid deformations and local texture variations through multi-path collaborative modeling. Extensive experiments on several representative benchmark datasets demonstrate that FFMatch outperforms existing methods regarding matching accuracy, robustness, and generalization capability.