Perplexity Sonar Online: The Search-Augmented LLM API for Real-Time Data

What Sonar Does Differently

Standard LLM APIs return text generated from training data with a knowledge cutoff. Perplexity Sonar returns answers grounded in live web search results, with citations in the response body. The model is not just searching and summarizing - it integrates search results into coherent prose with numbered references.

This replaces a common pattern: LLM + search tool + citation extraction + response formatting. Sonar does all of it in one API call.

Sonar vs Sonar Pro

For most use cases, Sonar is sufficient. Use Sonar Pro when you need maximum coverage on a fast-moving topic or when citation accuracy is critical (legal research, medical information, financial news).

OpenAI-Compatible API

from openai import OpenAI

# Drop-in swap from OpenAI
client = OpenAI(
    api_key=os.environ["PERPLEXITY_API_KEY"],
    base_url="https://api.perplexity.ai",
)

response = client.chat.completions.create(
    model="sonar",
    messages=[
        {
            "role": "system",
            "content": "You are a research assistant. Cite your sources.",
        },
        {
            "role": "user",
            "content": "What are the latest benchmark results for GPT-4.1 vs Claude 3.7?",
        },
    ],
)

answer = response.choices[0].message.content
# Citations are available in response.citations (Perplexity extension)
citations = getattr(response, "citations", [])

print(answer)
for i, citation in enumerate(citations, 1):
    print(f"[{i}] {citation}")

The search_recency_filter Parameter

For time-sensitive queries, the search_recency_filter parameter constrains Sonar to only return sources from a specific time window:

response = client.chat.completions.create(
    model="sonar",
    messages=[
        {"role": "user", "content": "What AI models were released this week?"},
    ],
    extra_body={
        "search_recency_filter": "week",  # "month", "week", "day", "hour"
        "return_images": False,
        "return_related_questions": True,
    },
)

Setting search_recency_filter: "day" ensures you only get information from the past 24 hours - critical for market monitoring, breaking news summarization, or competitive intelligence.

Use Cases

Competitive analysis: "Summarize the key announcements from [competitor] in the last 30 days" - run this on a schedule and store results for trend tracking.

News monitoring: "What are the top stories about AI regulation this week?" - replaces a custom pipeline of news API + LLM summarization + deduplication.

Live data extraction: "What is the current valuation of [company] according to recent news?" - works where data is too recent for training data.

Research grounding: When building a RAG system on proprietary documents, use Sonar to supplement with real-time web context that your documents may not cover.

When to Use Sonar vs Standard RAG

Use Sonar when:

• Data changes frequently (news, prices, regulations, product releases)
• You do not have a corpus to index
• Setup time matters (Sonar is ready in one API call, RAG requires indexing infrastructure)

Use standard RAG when:

• Data is proprietary and cannot be sent to a third-party search index
• You need exact retrieval from specific documents
• Volume is high enough that $5/1000 searches is cost-prohibitive
• You need strict citation to specific internal documents

Links

• Perplexity API pricing