Language models can generate fluent, confident-sounding text even for languages they barely cover, but fluency alone reveals little about whether that text preserves situated meaning, cultural reference, and communicative intent. We study this question for Colombian Indigenous languages through two complementary probes of language understanding: (1)zero-shot machine translation across five languages (Arhuaco, Inga, Kamentsá, Nasa Yuwe, Wayuunaiki) and (2)multimodal cultural grounding on a community-built Inga image-description dataset that, to our knowledge, is the largest available for Inga. We additionally document and release this culturally grounded multimodal dataset as a reusable resource for the Inga community and future research. We evaluate GPT-5.2, GPT-4o-mini, Gemini 2.5 Flash-Lite, Qwen2-VL-72B-Instruct, NLLB-200, and T5 variants without fine-tuning. GPT-5.2 achieves the highest translation scores (chrF 21.36, BLEU 3.42), yet absolute performance remains uniformly low. No evaluated model is suitable for direct deployment, placing Colombian Indigenous languages among the most underserved in NLP even relative to other low-resource languages. For multimodal cultural grounding, semantically aligned visual context consistently improves preservation of community-provided cultural ideas, while mismatched images cause sharp degradation, a distinction that standard similarity metrics fail to capture. These findings show that fluent generation can mask failures of situated language understanding and motivate purpose-built evaluation for Indigenous language NLP.