Mini toolspplx models
Browse all models from this model family.
ID
Model
Company
Type
Primary task
Open source
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By Perplexitypplx-embed is a family of text embedding models built for real-world, web-scale retrieval.TextReleased 4mo ago
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By PerplexityA 4B-parameter text embedding model built on diffusion-pretrained Qwen3. Produces 2560-dimensional dense embeddings with INT8 and binary quantization, 32K context window, and Matryoshka Representation Learning (MRL) support. Designed for semantic search and web-scale retrieval. Multilingual. Available via API, SentenceTransformers, ONNX, and Text Embeddings Inference.TextReleased 4mo ago
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By PerplexityA 0.6B parameter multilingual text embedding model built on a diffusion-pretrained Qwen3 backbone. Produces 1024-dimensional unnormalized embeddings with int8 and binary quantization support, a 32K token context window, and Matryoshka Representation Learning. Optimized for web-scale retrieval and semantic search.TextReleased 4mo ago
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By PerplexityA 0.6B-parameter contextual text embedding model for RAG pipelines. Unlike standard embedding models, it takes full document chunks together so each chunk's embedding reflects surrounding context. Produces 1024-dimensional int8-quantized vectors with a 32K token context window. Supports binary quantization and MRL. Multilingual and instruction-free. Available via Perplexity API and as open weights.TextReleased 4mo ago
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By PerplexityA 4B-parameter contextual text embedding model built on diffusion-pretrained Qwen3, designed for RAG pipelines where document chunks benefit from surrounding context. Produces 2560-dimensional INT8/BINARY-quantized embeddings with a 32K context window and MRL support. No instruction prefixes required. MIT-licensed with open weights.TextReleased 4mo ago
