Nomic Embed Text v1.5
137M-parameter Apache 2.0 embedder with 8K context and shrinkable dimensions
About
Nomic Embed Text v1.5 is a 137M-parameter open embedding model that punches above its weight: 768-dimensional vectors, an 8192-token context window via RoPE scaling, and Matryoshka training so you can truncate dimensions down to 64 without re-embedding. It uses task prefixes (search_query: / search_document:) for asymmetric retrieval, which the model card requires for best results. Released in 2024 under Apache 2.0, it became a favorite default for local RAG stacks and ships as an official Ollama and llama.cpp model. The limits: it is English-focused, and a missing prefix silently degrades retrieval quality — the most common footgun reported by users.
Specs
Context window
Pricing
Free quota
API access
Weights
Released
Benchmarks
MTEB average ~62.3 (reported in third-party comparisons)
Provider-reported unless a source says otherwise. Benchmarks are marketing-adjacent by nature — treat them as directional.
Use cases
Trust score — 7/10
Scored 7/10 (verified tier, metrics-v1). Evidence: 137M params, 768 dims, 8192 context, Apache 2.0, 2024 release corroborated across three independent RAG-stack writeups read this session.; MTEB ~62.3 from third-party tables — approximate.; Task-prefix requirement documented in multiple deployment guides..