Can I run Sonar Pro Search locally?

No. The weights are not released and the model is available only via the OpenRouter API.

What does Sonar Pro Search actually cost?

You pay $3.00 per million input tokens, $15.00 per million output tokens, plus $18.00 per thousand requests — the per-request charge is on top of, not instead of, the token costs.

How is Sonar Pro Search different from standard Sonar Pro?

Standard Sonar Pro runs one query and synthesises a response; Sonar Pro Search adds autonomous multi-step planning and tool execution, working through a full research workflow before answering.

Sonar Pro Search

5710°

#llm #perplexity #vision #reasoning #research #agents

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Perplexity | 200,000 token context | $3.00/M input, $15.00/M output. 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 on tokens plus $18 per thousand requests. This model powers the Pro Search mode on the Perplexity platform. Sonar Pro Search adds autonomous, multi-step reasoning to Sonar Pro. So, instead of just one query + synthesis, it plans and executes entire research workflows using tools.

Guides for this model

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Captain's notes

Written by Captain, the house editor

Notes from 2026-10-13

Sonar Pro Search is Perplexity's most capable search model, built to run multi-step research workflows rather than a single query-and-synthesise pass. It plans, executes and reasons across several tool calls autonomously before returning an answer. If you need a model that can investigate a topic end-to-end rather than just retrieve a page, this is the Perplexity tier to reach for.

Known for

Autonomous multi-step research workflows, not just single-query retrieval
200,000-token context window for long, complex research sessions
Powers the Pro Search mode on the Perplexity platform directly
Deeper reasoning and analysis than standard Sonar Pro

Watch out for

Pricing stacks token costs ($3/$15 per million) on top of $18 per thousand requests, so heavy use adds up fast.
Closed weights, API-only via OpenRouter — you cannot run this locally or inspect the model.
No model card published; internal architecture and training details are not disclosed.

Best for

Prompts to try

Deep competitive research

Research the current state of solid-state battery commercialisation. Identify the three leading companies, their production timelines, key technical barriers each faces, and any partnerships announced in the last twelve months. Summarise your findings with sources.

Multi-source policy analysis

Investigate how the EU AI Act's requirements for high-risk AI systems differ from the US Executive Order on AI. Cover enforcement mechanisms, compliance deadlines and which industries face the strictest obligations under each framework.

Structured market landscape

Map the current open-source LLM landscape for code generation: list the leading models, their licences, benchmark standings where published, and which are viable for on-premise deployment. Present the result as a comparison table.

Compare with

Sonar Pro
API-only model
200,000 token context · $3.00/M input, $15.00/M output
Sonar Reasoning Pro
API-only model
128,000 token context · $2.00/M input, $8.00/M output
DeepSeek V4.1 Flash
Open-weights model
1,048,576 token context · $0.04/M input, $0.68/M output
GLM 5.3 Flash
Open-weights model
1,048,576 token context · $0.02/M input, $0.72/M output

More text models from perplexity

Questions people ask

Can I run Sonar Pro Search locally?: No. The weights are not released and the model is available only via the OpenRouter API.
What does Sonar Pro Search actually cost?: You pay $3.00 per million input tokens, $15.00 per million output tokens, plus $18.00 per thousand requests — the per-request charge is on top of, not instead of, the token costs.
How is Sonar Pro Search different from standard Sonar Pro?: Standard Sonar Pro runs one query and synthesises a response; Sonar Pro Search adds autonomous multi-step planning and tool execution, working through a full research workflow before answering.

Captain's verdict
The right call when you need a model to run a research workflow, not just fetch a page — but watch the per-request fee.

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.
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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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