KnowledgeMay 11, 2026·5 min read

Perplexity Sonar API — Search-Grounded LLM in One Call

Perplexity Sonar API returns LLM answers grounded in real-time web search with citations. Tiers: sonar / sonar-pro / sonar-reasoning.

Perplexity
Perplexity · Community
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TokRepo exposes a universal CLI command, install contract, metadata JSON, adapter-aware plan, and raw content links so agents can judge fit, risk, and next actions.

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Asset
Universal CLI install command
npx tokrepo install 25b2aa98-cc43-4d6c-b654-5baa3f3c9f62
install contractmetadata JSONadapter planraw content
Intro

Perplexity's Sonar API is a one-call alternative to building search + scrape + chunk + RAG yourself — you send a question, Perplexity searches the web in real time and returns an LLM answer with inline numbered citations to the source URLs. Three tiers: sonar (fast/cheap), sonar-pro (better answer quality, more sources), sonar-reasoning (chain-of-thought, longer think time). Best for: news Q&A, fact-checking, anywhere you need a fresh answer with sources. Works with: OpenAI-compatible client (Python, JS), curl, LangChain. Setup time: 2 minutes.

Python (openai-compatible)

from openai import OpenAI

client = OpenAI(
    base_url="https://api.perplexity.ai",
    api_key=os.environ["PPLX_API_KEY"],
)

resp = client.chat.completions.create(
    model="sonar-pro",
    messages=[{"role": "user", "content": "What are the top 3 AI funding rounds this week?"}],
)
print(resp.choices[0].message.content)
# Response includes inline citations like [1][2][3]

# Read citation URLs separately
print(resp.citations)   # ["https://...", "https://...", "https://..."]

Filter sources by domain or recency

resp = client.chat.completions.create(
    model="sonar-pro",
    messages=[{"role": "user", "content": "What's the latest Anthropic announcement?"}],
    extra_body={
        "search_domain_filter": ["anthropic.com", "techcrunch.com"],   # whitelist
        "search_recency_filter": "week",                                # day | week | month | year
        "return_images": False,
        "return_related_questions": True,
    },
)

Model tiers (May 2026)

Model Use case Cost ($/1M) Latency
sonar Quick lookups, single-source Q&A $1 in / $1 out ~1–3s
sonar-pro Production answer quality, multi-source $3 in / $15 out ~3–7s
sonar-reasoning Hard reasoning, citations + thinking $1 in / $5 out ~10–25s
sonar-reasoning-pro Top quality reasoning $2 in / $8 out ~15–40s
sonar-deep-research Long research reports with 30+ sources $2 in / $8 out + per-search ~minutes

When NOT to use Sonar

If your data is private, not on the web, or in your own corpus — use a private RAG pipeline (e.g., Tavily + your vector store). Sonar searches public web only.

FAQ

Q: Sonar vs Grok Live Search vs Tavily? A: Grok bundles search into the same model call cheaply. Sonar gives stronger answer quality and richer citations. Tavily is search-only (you bring your own LLM). Use Sonar when answer quality matters; Tavily when you need control over the LLM stage.

Q: Are citations clickable? A: Citations come back as a citations array of URLs separately from the markdown answer. Render them as numbered footnotes in your UI. Sonar's content also embeds [1], [2] inline so you can map them visually.

Q: Rate limits? A: Standard tier: ~50 RPM on sonar, ~20 RPM on sonar-pro. Higher tiers in console.perplexity.ai. For production scaling beyond, talk to Perplexity Sales — they offer dedicated capacity.

Quick Use

  1. Get PPLX_API_KEY at perplexity.ai/settings/api
  2. OpenAI(base_url='https://api.perplexity.ai', api_key=PPLX_KEY)
  3. Use model='sonar-pro' and read resp.citations for source URLs

Intro

Perplexity's Sonar API is a one-call alternative to building search + scrape + chunk + RAG yourself — you send a question, Perplexity searches the web in real time and returns an LLM answer with inline numbered citations to the source URLs. Three tiers: sonar (fast/cheap), sonar-pro (better answer quality, more sources), sonar-reasoning (chain-of-thought, longer think time). Best for: news Q&A, fact-checking, anywhere you need a fresh answer with sources. Works with: OpenAI-compatible client (Python, JS), curl, LangChain. Setup time: 2 minutes.

Python (openai-compatible)

from openai import OpenAI

client = OpenAI(
    base_url="https://api.perplexity.ai",
    api_key=os.environ["PPLX_API_KEY"],
)

resp = client.chat.completions.create(
    model="sonar-pro",
    messages=[{"role": "user", "content": "What are the top 3 AI funding rounds this week?"}],
)
print(resp.choices[0].message.content)
# Response includes inline citations like [1][2][3]

# Read citation URLs separately
print(resp.citations)   # ["https://...", "https://...", "https://..."]

Filter sources by domain or recency

resp = client.chat.completions.create(
    model="sonar-pro",
    messages=[{"role": "user", "content": "What's the latest Anthropic announcement?"}],
    extra_body={
        "search_domain_filter": ["anthropic.com", "techcrunch.com"],   # whitelist
        "search_recency_filter": "week",                                # day | week | month | year
        "return_images": False,
        "return_related_questions": True,
    },
)

Model tiers (May 2026)

Model Use case Cost ($/1M) Latency
sonar Quick lookups, single-source Q&A $1 in / $1 out ~1–3s
sonar-pro Production answer quality, multi-source $3 in / $15 out ~3–7s
sonar-reasoning Hard reasoning, citations + thinking $1 in / $5 out ~10–25s
sonar-reasoning-pro Top quality reasoning $2 in / $8 out ~15–40s
sonar-deep-research Long research reports with 30+ sources $2 in / $8 out + per-search ~minutes

When NOT to use Sonar

If your data is private, not on the web, or in your own corpus — use a private RAG pipeline (e.g., Tavily + your vector store). Sonar searches public web only.

FAQ

Q: Sonar vs Grok Live Search vs Tavily? A: Grok bundles search into the same model call cheaply. Sonar gives stronger answer quality and richer citations. Tavily is search-only (you bring your own LLM). Use Sonar when answer quality matters; Tavily when you need control over the LLM stage.

Q: Are citations clickable? A: Citations come back as a citations array of URLs separately from the markdown answer. Render them as numbered footnotes in your UI. Sonar's content also embeds [1], [2] inline so you can map them visually.

Q: Rate limits? A: Standard tier: ~50 RPM on sonar, ~20 RPM on sonar-pro. Higher tiers in console.perplexity.ai. For production scaling beyond, talk to Perplexity Sales — they offer dedicated capacity.

Source & Thanks

Built by Perplexity. Sonar API docs at docs.perplexity.ai.

Official SDK pending; OpenAI-compatible client works today.

🙏

Source & Thanks

Built by Perplexity. Sonar API docs at docs.perplexity.ai.

Official SDK pending; OpenAI-compatible client works today.

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