As a key technique in clinical diagnosis, multimodal medical image fusion (MMIF) integrates functional and metabolic information to assist diagnosis and enhance disease analysis reliability. However, existing methods typically rely on a single optimization objective, failing to meet clinical demands for flexible, on-the-fly result adjustments. To address this, we propose FreeMMIF, an interactive framework integrating a vision-language model (VLM)-based pseudo-labeling strategy and an instruction