Anomaly　traffic　detection　offers　essential　technical　support　for　securing　Mobile　Edge　Computing　(MEC)　networks.　The　emerging　Large　Model　(LM)　has　attracted　much　attention　for　their　excellent　data　generation　and　processing　capabilities,　but　it　is　difficult　to　deploy　LM-based　detection　models　in　resource-constrained　MEC　networks.　Existing　solutions　usually　compress　large　models　into　tiny　ones,　but　they　tend　to　be　impacted　by　data　drift,　resulting　in　decreased　detection　accuracy.　To　address　this　key　challenge,　we　propose　CL4Det,　a　novel　multi-model　anomaly　traffic　detection　framework　with　LM-powered　continuous　learning,　where　the　tiny　models　deployed　in　MEC　networks　can　achieve　the　desired　performance　comparable　to　the　large　models　via　continuous　retraining.　Specifically,　CL4Det　periodically　evaluates　the　model　performance　degradation　caused　by　data　drift　in　MEC　networks　and　decides　whether　to　generate　retraining　tasks　and　their　configurations.　Meanwhile,　CL4Det　schedules　all　traffic　detection　and　retraining　tasks　with　proper　resource　allocation,　aiming　to　ensure　real-time　detection　and　maximize　model　accuracy.　A　case　study　with　real-world　traffic　datasets　verifies　the　effectiveness　and　superiority　of　CL4Det.　Finally,　we　outline　the　challenges　and　future　directions　to　fully　exploit　the　collaborative　potentials　of　MEC　networks　and　LM　in　anomaly　traffic　detection.