换掉你的文本嵌入模型？先看这笔账
大模型和专用嵌入模型在37项任务上打了个平手：最强的大模型(Gemini 3.1 Pro)得分77.6，最强的嵌入模型77.2，差距0.4分。但代价悬殊：跑一遍基准，大模型要花154美元，嵌入模型只要0.11美元，贵了1431倍；在同样的GPU上，大模型处理token慢2.5到736倍。更扎心的是，大模型28%到81%的成本花在推理token上，而削减这部分预算对多数模型的检索质量不降反升。结论很清晰：相似度、分类、聚类这类任务，嵌入模型又快又便宜；只有推理密集的检索才值得动用大模型。这不是让你明天就换工具，而是告诉你：当大家都在追大模型时，小模型在多数场景下才是更聪明的选择。
📄 原文摘要(英文)
Should you replace your text-embedding pipeline with a large language model? We answer this with a controlled, cost-aware comparison of ten LLMs across six families and 26 embedding models (118M to 14B parameters) on 37 tasks spanning classification, semantic textual similarity (STS), clustering, pair classification, and retrieval. In aggregate the two paradigms are effectively tied: the best LLM (Gemini 3.1 Pro, 77.6) and the best embedding model (77.2) differ by 0.4 points. Their strengths differ by task: LLMs lead on reasoning-heavy retrieval, embedding models lead on classification, and the two match on clustering, STS, and pair classification. Reaching that parity is expensive. An LLM costs up to 1,431x more than an embedding model of comparable quality (USD 154 vs. USD 0.11 per benchmark pass), and the open LLMs tested process tokens 2.5 to 736x more slowly on the same GPU. Reasoning tokens account for 28 to 81% of LLM inference cost; lower reasoning budgets preserve or improve retrieval quality for most models in our ablation. The Pareto frontier contains the leading embedding models and one LLM, Gemini 3.1 Pro. These results support a division of labour: use embedding models for similarity, classification, and clustering, and reserve LLMs for reasoning-intensive retrieval. Our code, datasets, and results are publicly available at https://github.com/embeddings-benchmark/embedders-dilemma.