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Sonar Deep Research

Sonar Deep Research is a research-focused model designed for multi-step retrieval, synthesis, and reasoning across complex topics. It autonomously searches, reads, and evaluates sources, refining its approach as it gathers...

Sonar

Sonar is lightweight, affordable, fast, and simple to use — now featuring citations and the ability to customize sources. It is designed for companies seeking to integrate lightweight question-and-answer features...

Sonar Pro Search

Exclusively available on the OpenRouter API, Sonar Pro's new Pro Search mode is Perplexity's most advanced agentic search system. It is designed for deeper reasoning and analysis. Pricing is based...

Sonar Reasoning Pro

Note: Sonar Pro pricing includes Perplexity search pricing. See details here Sonar Reasoning Pro is a premier reasoning model powered by DeepSeek R1 with Chain of Thought (CoT). Designed for...

Sonar Pro

Note: Sonar Pro pricing includes Perplexity search pricing. See details here For enterprises seeking more advanced capabilities, the Sonar Pro API can handle in-depth, multi-step queries with added extensibility, like...

pplx-decider-v1.1-27b

pplx-decider-v1.1-27b is an update of our pplx-decider-v1-27b model, that boost the Decision Index score from 56.4 to 61.56, now outperforming Jev by more than 3.5 points, while using the same backbone. Most of the gains are driven by the lifting of the causal mask and training on more data, including data from tasksource. This is the **native decision-checkpoint layout**: a `Qwen3_5Model` backbone plus `readout.safetensors` containing a BF16 `[255, 5120]` decision head. The Transformers class name does not change the base model identity, Qwen3.8-27B. Use Python 3.12+, authenticated Hugging Face access to this private repository, and a CUDA GPU with room for roughly 49 GiB of weights plus working memory. Download the repository and install its pinned `requirements.txt` dependencies.

pplx-embed-context-v1-0.6b

`pplx-embed-v1` and `pplx-embed-context-v1` are state-of-the-art text embedding models optimized for real-world, web-scale retrieval tasks. > [!IMPORTANT] > `pplx-embed-v1` and `pplx-embed-context-v1` natively produce *unnormalized* int8-quantized embeddings. Ensure that you compare them via *cosine similarity*. Many modern embedding models rely on instruction tuning, where users prepend an instruction string to the text being embedded. This can yield a 2%-3% lift on benchmarks, but it also introduces prompt-selection overhead and can make indexing pipelines brittle (small instruction changes can shift embedding space). We deliberately **avoid** this requirement: you can embed the text you want to index directly, without having to choose or maintain an instruction prefix.

pplx-decider-v1.2

pplx-embed-context-v1-4b

`pplx-embed-v1` and `pplx-embed-context-v1` are state-of-the-art text embedding models optimized for real-world, web-scale retrieval tasks. > [!IMPORTANT] > `pplx-embed-v1` and `pplx-embed-context-v1` natively produce *unnormalized* int8-quantized embeddings. Ensure that you compare them via *cosine similarity*. Many modern embedding models rely on instruction tuning, where users prepend an instruction string to the text being embedded. This can yield a 2%-3% lift on benchmarks, but it also introduces prompt-selection overhead and can make indexing pipelines brittle (small instruction changes can shift embedding space). We deliberately **avoid** this requirement: you can embed the text you want to index directly, without having to choose or maintain an instruction prefix.

PII-Tracer-GGUF

This repository provides PII-Tracer, the detector introduced in PII-TRACE. GGUF packaging of `perplexity-ai/PII-Tracer`, a PII masking model for conversational data. The model uses a ~600M-parameter bidirectional Qwen3 encoder with a `perplexity-ai/pplx-embed-v1-0.6b` backbone and two heads: - **Token classification head** (1024 → 37): BIOES tags over 9 PII categories (`private_person`, `account_number`, `private_url`, `private_date`, `private_address`, `private_email`, `private_phone`, `other_pii`, `secret`), decoded with a constrained Viterbi decoder. - **Sensitivity head** (1024 → 1): conversation-level sensitivity classifier on mean-pooled hidden states.