Computer Assisted Translation (CAT) tools revolutionized text-based localization by offering translation memories, terminology management, and automated QA. Yet, these workbenches rarely integrate image-centric features required for webtoon localization, forcing users to rely on manual graphic-editing solutions. This study applies NIST conformance testing to compare three mainstream CAT tools and three webtoon native platforms for the localization of a single webtoon episode into Spanish LATAM, bridging the gap in image localization evaluation and in-context editing. The results indicate that platform-native environments outperform CAT tools in OCR extraction, segmentation, and visual context support, whereas CAT tools remain stronger in glossary/TM management and QA automation. Overall, the finding suggests that a hybrid workflow combining both environments could provide the most effective approach to balancing linguistic consistency and image-based adaptation in webtoon localization.