OpenAI's smaller text-embedding-3 model, which OpenAI calls an improved, more performant version of its ada embedding model. It returns 1536-dimension vectors by default and costs less than its sibling text-embedding-3-large.
Pick it if
you want the hosted embedding model every Octomind plan includes, Free too
you want embeddings without loading model weights into your machine's memory
you want shorter vectors via the dimensions parameter to shrink your index
Skip it if
a single input runs past 8,192 tokens, the per-input limit
Voyage 4 Lite is the latency- and cost-optimized tier of Voyage AI's Voyage 4 embedding series, priced below voyage-4 and voyage-4-large. Its embeddings are compatible with the rest of the series, and it costs the same as voyage-3.5-lite.
Pick it if
you want the Voyage 4 embedding space at a third of voyage-4's price
you embed queries cheaply against documents indexed with voyage-4-large
you need 256-2048 dimension embeddings over a 32K-token input
Skip it if
retrieval quality outweighs cost; Voyage reports voyage-4-large 4.80% ahead of it
you are indexing source code; voyage-code-4 targets code retrieval
FAQ
Which is cheaper, OpenAI Embedding 3 Small or Voyage 4 Lite?
OpenAI Embedding 3 Small and Voyage 4 Lite cost the same, $0.02/M tokens.
Which handles longer documents?
Voyage 4 Lite takes longer inputs (32k tokens against 8k tokens), so it needs less chunking.
Can I switch from OpenAI Embedding 3 Small to Voyage 4 Lite without re-indexing?
No. Vectors from different embedding models are not comparable, so a switch means re-embedding the whole corpus.
Try either model
Switch between models inside the same Octomind session.