This study investigates the trade-offs between using large language models (LLMs) and dedicated embedding models for downstream NLP tasks. Researchers conducted a cost-aware comparison involving ten LLMs and 26 embedding models (ranging from 118M to 14B parameters) across 37 tasks, including semantic textual similarity (STS), clustering, and retrieval. The results indicate a performance parity between the two paradigms; the top-performing Gemini 3.1 Pro achieved a score of 77.6, while the best embedding model reached 77.2. However, this parity comes with significant overhead: an LLM can cost up to 1,431 times more than an embedding model (USD 154 vs. USD 0.11 per benchmark pass), and open-source LLMs process tokens 2.5 to 736 times slower on identical hardware. The research highlights task-specific advantages, noting that embedding models excel in classification, whereas LLMs outperform them in reasoning-heavy retrieval tasks. Interestingly, reasoning tokens constitute 28% to 81% of total LLM inference costs, and the authors found that reducing reasoning budgets can maintain or even improve retrieval quality. Ultimately, the authors suggest a strategic division of labor, recommending embedding models for similarity and clustering tasks while reserving LLMs for complex, reasoning-intensive retrieval scenarios.