Context: ChatGPT and other large language models (LLMs) are widely used across healthcare, business, economics, engineering, and software engineering (SE). Despite their popularity, concerns persist about their reliability, especially their error rates across domains and the software development lifecycle (SDLC). Objective: This study synthesizes and quantifies ChatGPT's reported error rates across major domains and SE tasks aligned with SDLC phases. It provides an evidence-based view of where ChatGPT excels, where it fails, and how reliability varies by task, domain, and model version (GPT-3.5, GPT-4, GPT-4-turbo, GPT-4o). Method: A Multivocal Literature Review (MLR) was conducted, gathering data from academic studies, reports, benchmarks, and grey literature up to 2025. Factual, reasoning, coding, and interpretive errors were considered. Data were grouped by domain and SE phase and visualized using boxplots to show error distributions. Results: Error rates vary across domains and versions. In healthcare, rates ranged from 8% to 83%. Business and economics saw error rates drop from ~50% with GPT-3.5 to 15-20% with GPT-4. Engineering tasks averaged 20-30%. Programming success reached 87.5%, though complex debugging still showed over 50% errors. In SE, requirements and design phases showed lower error rates (~5-20%), while coding, testing, and maintenance phases had higher variability (10-50%). Upgrades from GPT-3.5 to GPT-4 improved reliability. Conclusion: Despite improvements, ChatGPT still exhibits non-negligible error rates varying by domain, task, and SDLC phase. Full reliance without human oversight remains risky, especially in critical settings. Continuous evaluation and critical validation are essential to ensure reliability and trustworthiness.