Flow Matching (FM) has recently been introduced into generative collaborative filtering due to its potential to capture user preferences. However, existing FM-based methods are often trained with only the standard regression objective in flow matching and lack explicit inter-user supervision. This can drive the model toward an averaged preference pattern and generate indistinguishable predictions across users. A natural remedy is to introduce contrastive learning for inter-user separation, yet naive designs face two challenges: (1) In FM-based collaborative filtering, the output is a user's predicted interaction scores over items. Applying contrastive learning directly on this output forces different users to be separated even when they share the same preferences, leading to false repulsion. (2) Flow matching is highly sensitive to its input, so input-level perturbations for view construction can change the generated outcome, making positive pairs semantically mismatched. To address these issues, we propose CoFlowCF, a contrastive flow matching framework for collaborative filtering. Specifically, CoFlowCF introduces hidden contrastive learning by applying inter-user constraints before the flow model's final outputs. We decompose the flow model into a flow encoder and a prediction head, and apply contrastive regularization to the encoder output representations to avoid false repulsion in the prediction space. To build semantically consistent views, we keep the input unchanged and use independent dropout inside the encoder instead of input-level perturbations. Extensive experiments and ablation studies on multiple real-world datasets demonstrate that CoFlowCF consistently improves recommendation performance and robustness. The code is available at https://github.com/DevByQian/CoFlowCF.