Can LLMs Replace Embedding Models? Costs Are 1431x Higher
kalyan_kpl · x · 2026-08-29
This paper compares 10 LLMs and 26 embedding models across 37 tasks, finding that while overall performance is tied, their strengths differ. LLMs lead in reasoning-heavy retrieval, while embedding models excel at classification; they match on clustering and semantic similarity.
Key findings:
- Performance: The best LLM (Gemini 3.1 Pro, 77.6) and best embedding model (77.2) differ by only 0.4 points.
- Cost: For comparable quality, an LLM can cost up to 1,431x more than an embedding model ($154 vs $0.11 per benchmark pass).
- Recommendation: Use embedding models for similarity, classification, and clustering; reserve LLMs for reasoning-intensive retrieval.
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