LANGUAGE MODEL Perplexity Last updated:

Perplexity Sonar

Search-Native Generation

Perplexity AI's family of LLM variants explicitly tuned for "answer this question and cite your sources." Released as paid API endpoints (Sonar Small, Large, Huge) in 2024. The underlying technique — Llama 3.1 fine-tuned for citation quality and integrated tightly with Perplexity's web search backend — became the default architecture behind any consumer "AI search" product.

Try Perplexity API Docs ↗
Cost
Moderate
$1.00 in / $1.00 out
Context
128K
Up to 128,000 tokens
How are Intelligence, Speed & Cost bucketed?
Intelligence and Speed buckets are percentile ranks on Artificial Analysis. Cost buckets are fixed dollar thresholds keyed off output-token price ($/M out).
Intelligence
  • Top 1%≤ 1%
  • Top 5%≤ 5%
  • Top 10%≤ 10%
  • Good≤ 25%
  • Medium≤ 50%
  • Below avg> 50%
Speed
  • Top 1%≥ 345 tok/s
  • Top 5%≥ 237 tok/s
  • Top 10%≥ 196 tok/s
  • Good≥ 146 tok/s
  • Medium≥ 90 tok/s
  • Slow< 90 tok/s
Cost
  • Freeopen weights · self-host
  • Low< $1 / M out
  • Moderate$1–5 / M out
  • High≥ $5 / M out
Official ↗

Why it matters

Perplexity Sonar represents the "search-grounded LLM" as a distinct model category — a specialization that generic LLMs (GPT, Claude) approach but don't own as a primary product surface. Whether AI search remains a separate category or gets absorbed by general-purpose chat is the open question.

Core Capabilities

Long Documents
Handles entire codebases, books, and multi-doc RAG.
Generative
Produces images, video, audio, or other media.
Agent Workflows
Built for tool use and autonomous tasks.

Context Window

128k tokens
≈ 98 pages
4k Chat 聊天
32k Long docs 长文档
128k This model 本模型
400k Multi-doc 多文档
1M Codebase 整个代码库
10M

Availability

API
Available
Product / App
Not available
Open Source
Not released
Enterprise
Contact sales

Pricing Model

Pay per token
Input and output billed separately.
Pay-per-token

Capability / Performance

Where this model sits relative to the middle 60% of models in the tree. All scores are 0–10 (higher is better).

Lower 20% Upper 80% This model
Context / memory
Context window size · log-scaled
6.0
9.0
6.0
Cost efficiency
Input price ($/M tokens) · cheaper scores higher
6.2
10.0
8.1
Lower 20% 20th percentile — 20% of models score below this This model Where the current model lands Upper 80% 80th percentile — only 20% of models score above this Percentile boundaries are computed across every model in the tree that reports the underlying benchmark for each capability.

What it feels like

  • Language model from Perplexity — see the linked sources below for benchmark and review coverage
  • Tool-use and agent loops are the typical fit per the published model card

Best use cases

  • Agent / tool-use workflows that match the model's published benchmarks
  • See the model spec and sources block for benchmarked use cases

Tools to try

Perplexity Perplexity API

Not ideal for

  • Tasks far outside the modalities listed in this model's spec
  • Workflows where a more recent successor in the same family scores higher