Text Generation Model

Sonar

Sonar is a text generation model from Perplexity that combines LLM chat with real-time web search and citation support.

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Publisher

Perplexity

TypeText

Context Window128,000 tokens

ReleasedJanuary 2025

Input$1.00/MTok

Output$1.00/MTok

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About Sonar

Web search with inline citations and sources

Sonar is a text generation model developed by Perplexity, released in January 2025. It is built around real-time web search integration, meaning responses can draw on live information from the web rather than relying solely on a static training dataset. The model supports a 128,000-token context window and can return up to 32,768 tokens per response. It is designed for use cases where up-to-date information and source attribution matter.

What distinguishes Sonar from standard chat models is its configurable search behavior: users can toggle citation return, image return, and search context size as explicit inputs. This makes it well-suited for research assistance, fact-checking workflows, and any application where grounding responses in verifiable sources is a requirement. The model is offered as a first-party Perplexity product and is available on MindStudio without requiring separate API key management.

Capabilities

What Sonar supports

Real-Time Web Search

Retrieves live information from the web at inference time, allowing responses to reflect current events and recent data rather than a fixed training cutoff.

Citation Return

Optionally returns inline citations alongside generated text, linking claims back to the source URLs retrieved during search.

Image Return

Can return relevant images sourced from the web as part of a response, configurable via the Return Images input toggle.

Configurable Search Context

Exposes a Search Context Size setting that controls how much retrieved web content is incorporated into the model's context before generating a response.

Large Context Window

Supports a 128,000-token context window, allowing long documents or extended conversation histories to be processed in a single request.

Long-Form Text Generation

Generates responses up to 32,768 tokens, suitable for detailed reports, summaries, or multi-step explanations.

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Performance

Benchmark scores

Scores represent accuracy — the percentage of questions answered correctly on each test.

Benchmark	What it tests	Score
MMLU-Pro	Expert knowledge across 14 academic disciplines	68.9%
GPQA Diamond	PhD-level science questions (biology, physics, chemistry)	47.1%
MATH-500	Undergraduate and competition-level math problems	81.7%
AIME 2024	American math olympiad problems	48.7%
LiveCodeBench	Real-world coding tasks from recent competitions	29.5%
HLE	Questions that challenge frontier models across many domains	7.3%
SciCode	Scientific research coding and numerical methods	22.9%

FAQ

Common questions about Sonar

What is the context window size for Sonar?

Sonar supports a context window of 128,000 tokens, which allows it to process long documents or extended conversations in a single request.

How large can Sonar's responses be?

Sonar can generate responses up to 32,768 tokens in a single output.

Does Sonar have a knowledge cutoff date?

Because Sonar integrates real-time web search, it can access current information beyond a static training cutoff. However, the exact training data cutoff date is not specified in the available metadata.

What pricing applies to Sonar on MindStudio?

Pricing information for Sonar is not published in the available metadata. You should check MindStudio's current pricing page or Perplexity's API documentation for up-to-date cost details.

Can Sonar return citations and images alongside its responses?

Yes. Sonar exposes configurable inputs for returning citations and images, both of which can be toggled when building an application on MindStudio.

Does Sonar support image or video input?

Based on the available metadata, Sonar does not have confirmed support for image or video analysis as inputs. It is classified as a text generation model.

Community Discussion

What people think about Sonar

Community discussion around Sonar on Reddit is limited in volume, but the available thread reflects interest in how it performs relative to other search-augmented models on structured benchmarks like FRAMES. Users in the LocalLLaMA community engaged with comparisons between Sonar and competing search models, generating 792 upvotes and 75 comments.

The primary concern raised in community threads is whether Sonar's benchmark performance holds up against open-source alternatives, with at least one highly upvoted post highlighting an open-source search repository that outperformed Sonar Reasoning Pro on the FRAMES benchmark. Practical use cases discussed include developer integrations and real-time search applications.

r/LocalLLaMA792 pts75 comments

Open-source search repo beats GPT-4o Search, Perplexity Sonar Reasoning Pro on FRAMES

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Resources