Sonar Reasoning Pro
Sonar Reasoning Pro is a text generation model from Perplexity that combines live web search with extended reasoning in a 128K context window.
PerplexityReal-time web search with chain-of-thought reasoning
Sonar Reasoning Pro is a text generation model developed by Perplexity, released in January 2025. It is designed to combine real-time web search retrieval with chain-of-thought reasoning, allowing it to ground responses in current information while working through multi-step problems. The model supports a 128,000-token context window and can return up to 8,000 tokens per response.
What distinguishes Sonar Reasoning Pro from standard chat models is its built-in search integration, which can be configured to return citations and images alongside responses, and its adjustable search context size. This makes it well-suited for tasks that require up-to-date factual accuracy, such as research summarization, fact-checking, and answering questions about recent events. Developers can control citation output and search depth directly through the model's input parameters.
What Sonar Reasoning Pro supports
Live Web Search
Retrieves real-time information from the web during inference, grounding responses in current data rather than a static training snapshot.
Citation Return
Optionally returns source citations alongside generated text, configurable via the return_citations input parameter.
Image Results
Can return relevant images from search results alongside text responses, toggled through the return_images input parameter.
Search Context Control
Allows developers to adjust the volume of web search context fed into the model via the search_context_size parameter, balancing depth against latency.
Chain-of-Thought Reasoning
Applies extended reasoning steps before producing a final answer, useful for multi-step research and analytical tasks.
Large Context Window
Supports up to 128,000 tokens of context, enabling long document analysis or extended multi-turn conversations.
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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, with a maximum response size of 8,000 tokens.
Does Sonar Reasoning Pro have access to real-time information?
Yes. The model integrates live web search at inference time, so its responses can include information beyond its training cutoff date.
Can I control whether citations are returned in responses?
Yes. The model exposes a return_citations input parameter that lets you toggle citation output on or off per request.
What is the pricing for Sonar Reasoning Pro on MindStudio?
Pricing information is not published in the available metadata. Check the MindStudio platform or Perplexity's official documentation for current pricing details.
Does Sonar Reasoning Pro support image inputs?
Image input support is not confirmed in the available metadata. The model does support returning images from search results via the return_images parameter, but this is distinct from accepting image uploads as input.
What types of tasks is Sonar Reasoning Pro best suited for?
The model is designed for tasks requiring up-to-date factual information combined with multi-step reasoning, such as research summarization, fact-checking, and answering questions about recent events.
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?
Documentation & links
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.
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