- Home
- AI Models
- Perplexity
- pplx-embed-v2-context-0.6b
PerplexitySpecialist modelOpen weights
pplx-embed-v2-context-0.6b
Summary
Perplexity announced pplx-embed-v2-context with open 0.6B and 9B weights on November 3, 2026. The multilingual vision-language model supports retrieval across text, images, and visual documents, including PDF pages.
Contextual mode embeds text chunks with the rest of the document in view. Text also has a non-contextual mode, and image pages can be split into horizontal chunks. The 0.6B checkpoint produces int8 vectors with 1,024 dimensions and shares this embedding space with the 9B model.
The model card recommends encode_queries for searches and encode for documents because they use separate trained prefixes. The weights are public, but the card states no license for this checkpoint. The reviewed sources do not establish a hosted API or price.
On this page
Similar models
More models of the same kind as pplx-embed-v2-context-0.6b, chosen by model line, developer, size, price and release date.
- pplx-embed-v2-late-0.6bPerplexity · same developer
- pplx-embed-v2-context-9b-previewPerplexity · same developer
- pplx-embed-v2-context-9bPerplexity · same developer
- pplx-embed-v2-late-9bPerplexity · same developer
- EmbeddingGemma 2Google DeepMind · similar size
- TeleOCRTeleAI · similar size
- LFM2.5-VL-3B-DSparkLiquid AI · similar size
- ManiFlow-tbAi2 · similar size
All specifications
Architecture
Input and output
- Modalities
- Text, images, and visual documents. Supports contextual and non-contextual text inputs as well as contextual image chunks.
- Source: Modalities
Watermarking and provenance
AI watermarking refers to invisible signals embedded in the output. Visible marks and provenance metadata are listed separately. Unknown means there is insufficient evidence for that particular field.
- AI watermarking
- Unknown
- no source
- Watermarking technique
- Not known
- no source
Licence and access
- Shutdown
- None announced
- no source
Further details
- Embedding output
- One 1,024-dimensional int8 vector per document or document chunk. Use encode_queries for queries and encode for documents.
- Source: Embedding output
- Shared embedding space
- Shares its 1,024-dimensional embedding space with the 9B variant.
- Source: Shared embedding space