How much does Perplexity: Sonar Pro cost?

Perplexity: Sonar Pro costs $3.00 per 1M input tokens and $15.00 per 1M output tokens.

What is the context length of Perplexity: Sonar Pro?

Perplexity: Sonar Pro has a 200,000 token context window.

What inputs and outputs does Perplexity: Sonar Pro support?

Perplexity: Sonar Pro accepts text and image as input and returns text.

What other models does Perplexity have?

Perplexity: Sonar, Perplexity: Sonar Deep Research and Perplexity: Sonar Reasoning Pro are other models from Perplexity.

When was Perplexity: Sonar Pro released?

Perplexity: Sonar Pro was released on Mar 7, 2025.

Sonar Pro

PerplexityReleased Mar 7, 2025perplexity/sonar-pro

Compare

Sonar Pro is an advanced non-reasoning search model designed for handling complex, multi-step queries that demand deeper content understanding and high citation accuracy. With a large context length of 200k, it excels at retrieving and synthesizing information from extensive text sources. Sonar Pro delivers in-depth answers supported by twice as many citations as the standard Sonar model, thanks to its enhanced information retrieval architecture. Optimized for multi-step tasks, it’s particularly useful for applications such as academic literature reviews, competitor and industry research, and generating curated content like restaurant catalogs with reviews. Its ability to surface comprehensive, well-cited responses makes it a powerful tool for users needing detailed and reliable information.

Context

200K

Max output

Input /1M

$3.00

Output /1M

$15.00

Blended /1M

$6.00

AcceptsTextImageProducesTextTokenizer Other

Providers

FastRouter routes your requests to this provider. You can also bring your own key.

ProviderInput /1MOutput /1MContextMax outputLatencyThroughput

Perplexity AI

perplexity

Input /1M$3.00

Output /1M$15.00

Context200K

Max output8K

Latency—

Throughput—

Supported parameters

Request parameters you can send with this model.

Core (Sampling & basic generation)

max_tokens: Maximum tokens to generate.
min_p: Minimum probability threshold for sampling.
seed: Seed for deterministic sampling.
temperature: Controls randomness of output.
top_k: Limits sampling to top K tokens.
top_p: Nucleus sampling threshold.

Penalties (Token Penalties)

frequency_penalty: Penalizes frequent tokens.
presence_penalty: Penalizes tokens already present.
repetition_penalty: Penalizes repeated tokens.

Reasoning (Reasoning Controls)

include_reasoning: Include reasoning trace in the response.
reasoning: Provider-specific reasoning configuration.

Output & Format (Response Formatting)

logit_bias: Bias values for specific token IDs.

Other

web_search_options

Performance

Median throughput and time to first token per provider over the last week.

Throughput

Latency

Make your first API call

OpenAI-compatible. Point your SDK at FastRouter, or call the API directly.

Full API docs

Replace <FASTROUTER_API_KEY> with your key · Get your key →Install

OpenAI SDKHTTP

from openai import OpenAI

client = OpenAI(

base_url="https://api.fastrouter.ai/api/v1", # FastRouter base URL

api_key= "<FASTROUTER_API_KEY>", # Replace with your FastRouter API key

)

completion = client.chat.completions.create(

model="perplexity/sonar-pro", # Replace with your model ID

messages=[

{ "role": "user", "content": "What is the meaning of life?" }

]

)

print(completion.choices[0].message.content)

Frequently asked questions

More models from Perplexity

Sonarperplexity/sonar

Sonar is a lightweight, cost-effective non-reasoning search model built for delivering fast, grounded answers through real-time web search. With a context length of 128k, it is optimized for quick lookups and straightforward Q&A tasks, making it ideal for users who need immediate, reliable information without extensive processing. Sonar provides answers backed by citations, ensuring transparency while maintaining speed and low cost. Typical use cases include summarizing books, TV shows, or movies, looking up definitions or quick facts, and browsing up-to-date content across categories like news, sports, health, and finance. Its efficiency and accuracy make it well-suited for everyday information needs.

Sonar Deep Researchperplexity/sonar-deep-research

Sonar Deep Research is a powerful deep research and reasoning model designed for conducting thorough investigations and generating expert-level insights. With a context length of 128k, it excels at synthesizing information from hundreds of sources to produce highly detailed, structured reports. Ideal for exhaustive research tasks, Sonar Deep Research offers in-depth subject analysis and is capable of crafting content with precision and authority. Common use cases include writing white papers for industry thought leadership, developing comprehensive go-to-market (GTM) strategies, and creating advanced educational material for universities or training programs. It's the ideal tool for users who need depth, clarity, and rigor in their research output.

Sonar Reasoning Properplexity/sonar-reasoning-pro

Sonar Reasoning Pro is a high-performance reasoning model designed to tackle complex topics that require advanced multi-step thinking and deep content understanding. With a context length of 128k, it combines enhanced Chain-of-Thought (CoT) reasoning with a powerful information retrieval architecture to deliver well-structured, thoughtful responses. It provides twice as many citations on average compared to the standard Sonar Reasoning model, ensuring greater transparency and grounding. Ideal for tasks like competitive product analysis, exploring intricate scientific subjects, or creating detailed travel plans, Sonar Reasoning Pro excels in scenarios that demand both reasoning and reliable, well-sourced information.

Model scores sourced from ArtificialAnalysis