Sonar Reasoning Pro
Perplexity's premier reasoning model combining DeepSeek R1-powered Chain-of-Thought reasoning with real-time web search for comprehensive, citation-rich answers.
Perplexity Real-time web search with chain-of-thought reasoning
Sonar Reasoning Pro is a text generation model developed by Perplexity AI, built on top of DeepSeek R1 and augmented with Perplexity's proprietary real-time web search capabilities. It uses Chain-of-Thought reasoning to work through problems step by step before producing a final answer, making it distinct from models that rely solely on static training data. The model supports a 128,000-token context window and multiple languages, and was made available in February 2025.
Sonar Reasoning Pro is designed for tasks where accuracy, source transparency, and up-to-date information are important. Because it actively queries the web during inference, it can surface current information and provide citations alongside its responses. It is best suited for in-depth research, complex multi-step analytical questions, and scenarios where users need a well-reasoned explanation grounded in verifiable, recent sources.
What Sonar Reasoning Pro supports
Chain-of-Thought Reasoning
The model works through problems in explicit reasoning steps before producing a final answer, based on the DeepSeek R1 architecture. This makes it suitable for multi-step analytical and technical questions.
Real-Time Web Search
During inference, the model actively queries the web to retrieve current information and includes citations in its responses. This allows it to answer questions about events and data beyond its training date.
Large Context Window
Supports a 128,000-token context window, enabling processing of lengthy documents, detailed system prompts, and extended multi-turn conversations in a single request.
Multilingual Support
The model can process and generate text in multiple languages, broadening its usability across international and multilingual workflows.
Citation-Rich Output
Responses include inline citations sourced from live web results, providing traceable references for the information returned in each answer.
Ready to build with Sonar Reasoning Pro?
Get Started FreeBenchmark scores
Scores represent accuracy — the percentage of questions answered correctly on each test.
| Benchmark | What it tests | Score |
|---|---|---|
| MATH-500 | Undergraduate and competition-level math problems | 95.7% |
| AIME 2024 | American math olympiad problems | 79.0% |
Common questions about Sonar Reasoning Pro
What is the context window size for Sonar Reasoning Pro?
Sonar Reasoning Pro supports a context window of 128,000 tokens, which allows it to handle long documents, detailed instructions, and extended conversations within a single request.
Does Sonar Reasoning Pro have a knowledge cutoff?
The model's training data has a cutoff of January 2025. However, because it performs real-time web searches during inference, it can retrieve and cite information published after that date.
What underlying model is Sonar Reasoning Pro built on?
Sonar Reasoning Pro is built on top of DeepSeek R1, combined with Perplexity's proprietary real-time web search infrastructure.
What types of tasks is Sonar Reasoning Pro best suited for?
It is designed for in-depth research queries, complex multi-step reasoning tasks, and scenarios where source transparency and up-to-date information are important. It provides citations alongside its answers.
Where can I find pricing information for Sonar Reasoning Pro?
Pricing details are available through the Perplexity AI official documentation at docs.perplexity.ai and on the OpenRouter model card at openrouter.ai/perplexity/sonar-reasoning-pro.
What people think about Sonar Reasoning Pro
Community discussion around Sonar Reasoning Pro is relatively limited in the threads found, but it appears as a reference point in benchmark comparisons. In one thread with 793 upvotes on r/LocalLLaMA, it was cited alongside GPT-4o Search as a model that an open-source search repository outperformed on the FRAMES benchmark.
Users appear to reference Sonar Reasoning Pro primarily in the context of search-augmented reasoning evaluations rather than general conversational use. The second thread, from r/ClaudeAI, includes it in a broad multi-model comparison test focused on practical reasoning scenarios.
Open-source search repo beats GPT-4o Search, Perplexity Sonar Reasoning Pro on FRAMES
Car Wash Test on 53 leading AI models incl. 9 Claude models: "I want to wash my car. The car wash is 50 meters away. Should I walk or drive?
Parameters & options
Determines whether or not a request to an online model should return citations.
Determines whether or not a request to an online model should return images.
Controls how much web information is retrieved. Higher context provides more comprehensive results but costs more per request.
Explore similar models
Perplexity Sonar Deep Research
Perplexity's exhaustive deep research model that autonomously searches hundreds of sources to deliver expert-level analysis and comprehensive reports.
Perplexity Sonar
Perplexity's lightweight, real-time AI search model that delivers fast, citation-backed answers by connecting directly to the live web.
Perplexity Sonar Pro
Perplexity's advanced search model delivering deeper reasoning, richer citations, and multi-step query handling for complex research tasks.
Google Gemini 3.1 Pro
Google's frontier reasoning model delivering enhanced software engineering, agentic reliability, and multimodal intelligence across a 1M-token context window.
OpenAI GPT‑5.2 Pro
The most capable model series yet for professional knowledge work.
Nvidia Nemotron 3 Nano 30B
NVIDIA's compact open-weight hybrid MoE model with 30B total / 3.5B active parameters, delivering strong reasoning and coding performance up to 1M context.
Start building with Sonar Reasoning Pro
No API keys required. Create AI-powered workflows with Sonar Reasoning Pro in minutes — free.