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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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Frequently Asked Questions

  • What embedding dimensions does Embed v1 0.6b produce?

    1024 by default. Matryoshka representation learning lets you request shorter vectors through the dimensions parameter, trading a small amount of retrieval quality for less storage.

  • How does Embed v1 0.6b compare to pplx-embed-v1-4b?

    Embed v1 0.6b returns 1024-dimensional vectors against 2560 for pplx-embed-v1-4b, at a lower per-token price. The 4B tier reaches higher recall on Perplexity's published benchmarks and holds up better under binary quantization. Both share a context window of 32K tokens and need no instruction prefix.

  • How does Embed v1 0.6b compare to Qwen3-Embedding-0.6B?

    On Perplexity's PPLXQuery2Query benchmark over a 2.4 million document corpus, Embed v1 0.6b reaches 71.1% Recall@10 against 55.1% for Qwen3-Embedding-0.6B and 61.8% for BGE-M3. The qwen3-embedding-0.6b model is also available through AI Gateway if you want to compare on your own corpus.

  • Can Embed v1 0.6b replace a larger embedding model?

    Sometimes. 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. Test both on your own corpus before you decide.

  • Which similarity metric should I use with Embed v1 0.6b?

    Cosine similarity for INT8 embeddings and Hamming distance for binary embeddings. Embed v1 0.6b returns unnormalized vectors, so inner product and L2 distance give incorrect results unless you convert to float32 and L2-normalize first.

  • Does Embed v1 0.6b need an instruction prefix?

    No. You embed text directly, which removes the risk of a mismatch between indexing-time and query-time instructions.

  • Should I use binary quantization with Embed v1 0.6b?

    Test it first. Perplexity measures a 2 to 4 percentage point quality drop from binary output at the 0.6B scale, against under 1.6 points at 4B. INT8 is the default and already cuts storage 4x compared with FP32.

  • What is the context window for Embed v1 0.6b?

    32K tokens. Chunk documents longer than that before you embed them.

  • How do I call Embed v1 0.6b through AI Gateway?

    Use the AI SDK embed function for one value or embedMany for a batch, with the model identifier perplexity/pplx-embed-v1-0.6b. AI Gateway also serves embeddings through the OpenAI-compatible REST API.

  • Is Zero Data Retention available for Embed v1 0.6b?

    Zero Data Retention is not currently available for this model. Zero Data Retention is offered on a per-provider basis. See https://vercel.com/docs/ai-gateway/capabilities/zdr for details.