嵌入者的困境：大语言模型更优，但代价为何？

摘要

您是否应该用大语言模型取代您的文本嵌入流水线？我们通过一项受控且考虑成本的比较来回答这一问题，该比较涵盖六个家族的十种大语言模型和26种嵌入模型（参数规模从1.18亿到140亿），在涵盖分类、语义文本相似度（STS）、聚类、配对分类和检索的37项任务上进行了评估。总体而言，两种范式基本打成平手：最佳大语言模型（Gemini 3.1 Pro，77.6）与最佳嵌入模型（77.2）仅相差0.4分。两者的优势因任务而异：大语言模型在推理密集型的检索任务上领先，嵌入模型在分类任务上领先，而两者在聚类、STS和配对分类上表现相当。达到这种同等水平代价高昂。大语言模型的成本是同等质量嵌入模型的最多1,431倍（每次基准测试通过需154美元，而嵌入模型仅需0.11美元），且在相同GPU上，所测试的开源大语言模型处理词元的速度慢2.5至736倍。推理词元占大语言模型推理成本的28%至81%；在我们的消融实验中，较低的推理预算对大多数模型的检索质量具有保持或改善作用。帕累托前沿包含领先的嵌入模型和一个大语言模型——Gemini 3.1 Pro。这些结果支持一种分工模式：在相似度、分类和聚类任务中使用嵌入模型，将大语言模型保留用于推理密集型的检索任务。我们的代码、数据集和结果公开于 https://github.com/embeddings-benchmark/embedders-dilemma。

English

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.