Purpose: Patients increasingly utilise artificial intelligence (AI) for medical advice. While socioeconomic inequality in vascular healthcare is well-documented, the disparity in AI health literacy remains a critical gap in literature. This novel study compares the clinical accuracy, readability, and safety of free versus premium large language models (LLMs) in peripheral arterial disease (PAD) to evaluate this emerging digital divide. Methodology: Fifteen standardised PAD queries were inputted into three free LLMs (ChatGPT, Claude 4.6 Sonnet, Gemini 3.5 Flash) and their premium counterparts (GPT-5.5, Claude 4.8 Opus, Gemini 3.1 Pro). Clinical quality was graded using the validated 16-item DISCERN instrument. Readability was assessed via the Flesch-Kincaid Grade Level (FKGL). The frequency of universal safety-netting (explicitly advising medical review) was tracked across all 15 prompts. Results: Premium LLMs demonstrated superior clinical reliability (mean DISCERN 55.0 vs 50.1) and nuanced differential diagnosis. Crucially, safety alerts advising urgent medical review were markedly higher in premium models (up to 93% via hard-coded disclaimers) compared to free versions (47%–60%). Readability was universally poor (FKGL 8.5–12.6, requiring up to a university reading level), vastly exceeding the Grade 6 health literacy target. Notably, one free model exhibited algorithmic hallucination by leaking its internal system prompt, demonstrating the unpredictability of unregulated AI. Conclusion: Premium AI provides significantly safer PAD education than free versions. For lower socioeconomic patients, reliance on unregulated free AI risks compounding existing vascular inequities and delaying presentation for chronic limb-threatening ischaemia (CLTI). Vascular surgeons must actively mitigate this digital wealth gap by prescribing verified resources. Future research should focus on developing equitable, open-access medical AI featuring mandatory triage safety-netting and low-literacy adaptations.