Perplexity Embed v1 4B
Perplexity Embed v1 4B is a 4-billion-parameter embedding model from Perplexity with a 32,000-token context window.
PerplexityText embedding model with 32K context
Perplexity Embed v1 4B is a text embedding model developed and published by Perplexity, released in March 2026. It is a 4-billion-parameter model designed to convert text into dense vector representations, which can then be used for tasks such as semantic search, retrieval-augmented generation, clustering, and similarity comparison. The model supports a context window of 32,000 tokens, allowing it to encode long documents in a single pass.
The model carries ACCURATE and QUANTIZED tags, indicating it has been optimized through quantization techniques to reduce memory and compute requirements while maintaining embedding quality. This makes it suitable for production deployments where resource efficiency matters. Perplexity provides first-party access to the model through its API, and developers can get started via the official embeddings quickstart documentation.
What Perplexity Embed v1 4B supports
Long Context Encoding
Encodes text inputs up to 32,000 tokens into a single embedding vector, enabling full-document representation without chunking.
Quantized Inference
Uses quantization to reduce model size and memory footprint, making deployment more efficient without a full-precision model.
High-Accuracy Embeddings
Tagged as ACCURATE, the model is optimized to produce embeddings that closely reflect semantic relationships between text inputs.
Semantic Search Support
Generates dense vector representations suitable for nearest-neighbor search and retrieval-augmented generation (RAG) pipelines.
API Integration
Available via Perplexity's first-party API with a documented quickstart, allowing straightforward integration into existing applications.
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Get Started FreeCommon questions about Perplexity Embed v1 4B
What is the context window for Perplexity Embed v1 4B?
Perplexity Embed v1 4B supports a context window of 32,000 tokens, meaning it can encode documents up to that length into a single embedding vector.
What is this model used for?
It is an embedding model, meaning it converts text into vector representations. Common use cases include semantic search, document retrieval, clustering, and building retrieval-augmented generation (RAG) pipelines.
What does the QUANTIZED tag mean for this model?
The QUANTIZED tag indicates the model has been quantized, a technique that reduces numerical precision to lower memory usage and improve inference speed compared to a full-precision version.
Is pricing available for Perplexity Embed v1 4B?
Pricing information is not listed in the available metadata. You should consult Perplexity's official documentation or pricing page for current rates.
When was Perplexity Embed v1 4B released?
Perplexity Embed v1 4B was released in March 2026 by Perplexity.
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