Can I run Sonar locally or download its weights?

No. Sonar is an API-only model from Perplexity; the weights are not public and there is no Hugging Face repository.

What does Sonar cost and how long is its context?

Pricing is $1.00 per million input tokens and $1.00 per million output tokens, with a context window of 127,072 tokens.

How does Sonar differ from Perplexity's other models?

Sonar is the lightweight, speed-optimised tier in Perplexity's lineup, intended for simple Q&A integrations; Perplexity also offers larger models for more demanding tasks, but Sonar trades capability for lower cost and faster responses.

Sonar

6329°

#llm #perplexity #vision #chat #research #fast

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Perplexity | 127,072 token context | $1.00/M input, $1.00/M output. 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 optimized for speed.

Guides for this model

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Captain's notes

Written by Captain, the house editor

Notes from 2026-10-13

Sonar is Perplexity's lightweight, API-accessible search-augmented language model built for fast question-and-answer retrieval with citations. It lets you customise which sources it draws from, making it practical for product teams that need to embed a Q&A layer without the overhead of a heavier model. At $1 per million tokens each way, it sits at the affordable end of hosted search-grounded models.

Known for

Returns cited answers drawn from live web sources
Lets you restrict or customise the source pool it searches
Fast response times suited to latency-sensitive integrations
Straightforward, flat pricing at $1/M input and $1/M output

Watch out for

Closed weights, API-only — you cannot run this locally or inspect the underlying model
Designed for Q&A retrieval; not positioned for deep reasoning, coding or long-form generation
Context window is 127,072 tokens, which falls short of the 200k threshold for long-context work

Best for

Prompts to try

Cited competitive overview

What are the main differences between PostgreSQL and MySQL for a startup building a SaaS product in 2025? Summarise the key trade-offs and cite your sources.

Recent event Q&A

What has happened with AI regulation in the European Union over the past six months? Give me a concise summary with citations so I can verify each claim.

Source-grounded product research

I'm evaluating open-source vector databases for a retrieval-augmented generation pipeline. Compare Qdrant, Weaviate and Milvus on scalability, ease of deployment and community support. Cite the sources you draw from.

Compare with

Mistral 7B Instruct v0.2
Open-weights model
32,768 token context · $0.06/M input, $0.06/M output
Mistral Small 3.2 24B Instruct 2506
Open-weights model
128,000 token context · $0.07/M input, $0.20/M output
Gemma 3 12B
Open-weights model
131,072 token context · $0.05/M input, $0.15/M output
Nemotron 3.5 Content Safety
Open-weights model
131,072 token context · $0.20/M input, $0.20/M output

More text models from perplexity

Questions people ask

Can I run Sonar locally or download its weights?: No. Sonar is an API-only model from Perplexity; the weights are not public and there is no Hugging Face repository.
What does Sonar cost and how long is its context?: Pricing is $1.00 per million input tokens and $1.00 per million output tokens, with a context window of 127,072 tokens.
How does Sonar differ from Perplexity's other models?: Sonar is the lightweight, speed-optimised tier in Perplexity's lineup, intended for simple Q&A integrations; Perplexity also offers larger models for more demanding tasks, but Sonar trades capability for lower cost and faster responses.

Captain's verdict
A sensible, cheap drop-in for any product that needs cited web Q&A fast — nothing more, nothing less.

Captain writes these notes from the model's own description and model card. Prices and context sizes are the provider's at the time of writing and can change.

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Updated Oct 22, 2026

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How this score adds up

This model has no popularity data from any source yet, so it carries a stand-in number below 7,000° that keeps the lists complete. It never ranks above a model with data. The breakdown appears here as soon as a source reports it.

Glossary: the terms used here

Heat score: One number for how much a model is used and wanted right now, from 0° to 9,999°. Every list on this site that says hottest is sorted by it and nothing else.
Floor: The 7,000° every model with real data starts from. The scale runs from 0° to 9,999°; the part below the floor is reserved for stand-ins (models without data), so that a model with data always ranks above one without. The floor is the same for everyone and decides nothing; the degrees above it are the measurement.
Download-equivalents: Everything the score treats as a download, added up: downloads themselves, each torrent seeder as 1,000, each AI Horde image and each run on a generation site as one.
Log scale: Counting by tens, not by ones: going from 1,000 to 10,000 downloads adds the same degrees as going from 10,000 to 100,000. It keeps giants from crowding out everything else.
Likes: Thumbs up and likes from every source, worth half a download each on the log scale.
Adoption rate: How many downloads a day a model gets right now, from the difference between two readings of a source. A quarter of the weight of the download term.
Measured and estimated: Measured means two readings of a source exist and the difference is real. Estimated means only today's total is known and the past is spread over the model's life; the site says which one it is showing.
Seeder: Someone who keeps a copy of the weights online for others to download over a torrent, or a hosted mirror doing the same. Each one counts as 1,000 downloads.
AI Horde: A volunteer network that generates images for free on donated computers. The number of images it has made with a model is public, and each counts as one download.
Generation sites: Sites that host their own copies of a model and let people generate with it, such as PixAI, SeaArt and Yodayo. Their downloads count like Civitai's and each generation run as one download.
OpenRouter tokens: A text model served through an API, such as Claude or GPT, has no weights to download, so its usage is measured in tokens: the pieces of text it read and wrote through OpenRouter, a gateway many apps use, over the last 30 days. Every 10,000,000 tokens count as one download. Source: OpenRouter (openrouter.ai/rankings), CC BY 4.0.
Full model (checkpoint): The weights themselves, the file that generates on its own. It gets a bonus because add-ons depend on it.
LoRA and embedding: Small add-on files that steer someone else's weights toward a style, a character or a concept. They cannot run alone.
Family and weights: The base a model is built on, such as SD 1.5, SDXL, Flux or Wan. Every model inherits the release date of its family's weights.
Age: Degrees taken off for how long ago the family's weights came out: 150° a year, up to 750°. It keeps old downloads that had years to pile up from freezing the top of the list.
Family boost: A small, bounded nudge for LoRAs of a family this site is pushing forward right now, always listed in the breakdown.
Stand-in score: A stable number below the floor given to a model no source reports on yet, so lists stay complete. It never ranks above a model with data.
New! (grace period): A model less than 7 days old shows New! instead of a score and holds no place in the rankings. Its score is computed from day one, but a model one day old grows faster than anything, so without the wait the week's #1 would always be whatever came out yesterday rather than what held up for a week.
Trend: How a model's growth over a period compares with the typical model's growth over the same period. Faster than typical reads as a rise, slower as a fall.
Typical model: The middle of the catalog: half the models with a figure grew faster over the period, half slower.

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