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Perplexity
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Perplexity is a technology company that specializes in artificial intelligence and machine learning solutions.
AI Native
Yes
Number of tools
4
Number of models
11
Number of employees
247
Profitable
No
Valuation
$22.60BAI
Most popular AI tool
Tools
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An AI operating system that takes objectives, not instructions.OpenShare13,642 www.perplexity.ai
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Share10,354 www.perplexity.ai
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3,084 comet.perplexity.aiShareSai Teja PCV🙏 23 karmaAug 17, 2025@Perplexity Comethandy browser252 Reply Share Edit Delete Report
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55,822 www.perplexity.aiShare12953 Reply Share Edit Delete Report
Models
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pplx-embed is a family of text embedding models built for real-world, web-scale retrieval.TextReleased 5mo ago
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A 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 5mo ago
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A 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 5mo ago
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A 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 5mo ago
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A 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 5mo ago
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TextReleased 1y ago
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Deep Research is Perplexity’s premium reasoning assistant. It goes beyond quick Q&A by systematically searching across trusted sources, synthesizing information into a structured report, and exposing citations so you can verify or dive deeper. It’s built for longer, harder research questions that need accuracy and coverage rather than speed.TextReleased 1y ago
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Sonar Pro is Perplexity’s balanced reasoning model, sitting between the smaller Sonar options and the top-end Sonar Large. It’s tuned for strong multi-step reasoning, accurate retrieval grounding, and code assistance, with a practical context window for everyday copilots and research agents.TextReleased 1y ago
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Sonar is Perplexity’s family of reasoning models built on the R1 post-training architecture. They’re optimized for retrieval-augmented generation (RAG), accurate instruction following, and structured outputs, with multiple sizes (from Pro to Large 32K) to balance depth, speed, and cost.TextReleased 1y ago
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Perplexity Sonar Large 32K is the top-tier Sonar reasoning model, built on Perplexity’s R1 post-trained architecture. It supports a 32,000-token context window, excels at deep reasoning and coding, and is tuned for retrieval-augmented generation with precise, grounded answers.TextReleased 2y ago
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Advanced reasoning model combining multi-step Chain-of-Thought processing with real-time web search integration. Features a 128K context window. Successor to the deprecated sonar-reasoning model, built for complex problem-solving requiring deep multi-step analysis and live information retrieval.NewText
Papers
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How AI Agents Reshape Knowledge Work: Autonomy, Efficiency, and ScopePublished on: 2026-06-05 Venue: Kate Zyskowski 1 author
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The Adoption and Usage of AI Agents: Early Evidence from PerplexityHarvard UniversityPublished on: 2025-12-01 1 author
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Optimizing Robustness and Accuracy in Mixture of Experts: A Dual-Model ApproachPublished on: 2025-05-27 1 author
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Representation Engineering for Large-Language Models: Survey and Research ChallengesPublished on: 2025-04-21 1 author
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FlashInfer: Efficient and Customizable Attention Engine for LLM Inference ServingPublished on: 2025-04-21 1 author
Fundraises
View all-
Series DAnnounced 10 Sep 2025 · Leads: IVP$200M Raised $200M Round $20B Post-money 5 investors Announced
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Growth RoundAnnounced 10 Sep 2025$200M Round $20B Post-money Closed
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Series E+Announced 18 Jul 2025$100M Raised $100M Round $18B Post-money 4 investors Closed
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Growth RoundAnnounced 17 Jul 2025$100M Raised $100M Round $18B Post-money 4 investors Announced
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Series EAnnounced 25 Jun 2025$500M Raised $500M Round $14B Post-money 5 investors Announced
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Series EAnnounced 15 Jun 2025 · Leads: Accel Partners$500M Raised $500M Round $14B Post-money 1 investors Closed
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Series DAnnounced 15 Dec 2024 · Leads: IVP$500M Raised $500M Round $9B Post-money 5 investors Closed
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Series CAnnounced 24 Apr 2024 · Leads: Daniel Gross$62.70M Raised $62.70M Round $1B Post-money 5 investors Closed
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Series BAnnounced 04 Jan 2024 · Leads: IVP$73.60M Raised $73.60M Round $520M Post-money 7 investors Closed
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Series AAnnounced 28 Mar 2023 · Leads: New Enterprise Associates$25.60M Raised $25.60M Round $150M Post-money 6 investors Closed
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SeedAnnounced 15 Sep 2022$3.10M Raised $3.10M Round 5 investors Closed
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