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Perplexity

Embed v1 0.6b

Embed v1 0.6b is the compact tier of Perplexity's pplx-embed-v1 text embedding family. It returns 1024-dimensional vectors quantized to INT8 natively, requires no instruction prefix, and accepts inputs up to 32K tokens. Your use is subject to Perplexity's Terms & Privacy Policies.

Use with AI GatewayView docs
import { embed } from 'ai';


const result = await embed({
  model: 'perplexity/pplx-embed-v1-0.6b',
  value: 'Sunny day at the beach',
})
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About Embed v1 0.6b

Embed v1 0.6b is the smaller of Perplexity's two standard embedding models, released February 26, 2026. It targets high-volume embedding, where per-token cost rather than the last point of recall sets the ceiling on how much you can index. Like the 4B tier, it uses bidirectional attention with mean pooling and returns unnormalized INT8 vectors by default.

The size difference shows up in two places. Embed v1 0.6b returns 1024-dimensional vectors against 2560 for pplx-embed-v1-4b, so each vector takes less room in the index. Binary quantization is also less forgiving at this scale: Perplexity measures a 2 to 4 percentage point quality drop from binary output at 0.6B, against under 1.6 points at 4B. Test binary output on your own queries before you binarize a large index.

Retrieval quality holds up better than the parameter count suggests. On Perplexity's PPLXQuery2Query benchmark over a 2.4 million document corpus, Embed v1 0.6b reaches 71.1% Recall@10, against 61.8% for BGE-M3 and 55.1% for Qwen3-Embedding-0.6B. On the BERGEN end-to-end retrieval-augmented generation benchmark, Embed v1 0.6b outperforms the larger Qwen3-Embedding-4B on three of five question-answering tasks. Perplexity also reports that Embed v1 0.6b outperforms its Qwen3 counterpart at the same parameter scale on MTEB(Multilingual, v2).