Perplexity API
APISearch-augmented LLM API — built-in web search, real-time citations, Sonar models.
Capabilities11 decomposed
search-augmented llm inference with real-time web grounding
Medium confidencePerplexity's Sonar models integrate web search directly into the inference pipeline, automatically retrieving and synthesizing real-time web data without requiring separate tool invocations. The models operate at configurable search context depths (Low/Medium/High), trading latency and cost for search comprehensiveness. Responses include inline citations mapping claims to source URLs, enabling fact-checking and source attribution without post-processing.
Sonar models embed web search directly into inference rather than treating it as a separate tool call, eliminating latency from multi-step tool orchestration. Search context is configurable per-request (Low/Medium/High), allowing dynamic cost/quality tradeoffs. Citation tokens in Deep Research variant provide explicit source attribution without requiring post-hoc citation extraction.
Faster than OpenAI/Anthropic + external search APIs because search is native to the model, not a separate tool invocation; cheaper than Perplexity's Agent API for search-heavy workloads because search cost is bundled into request pricing rather than per-invocation tool fees.
multi-provider llm inference with optional web search tools
Medium confidenceThe Agent API provides a unified interface to third-party LLM providers (OpenAI, Anthropic, Google, xAI) with optional web search and URL fetching tools. Models can invoke tools autonomously or be constrained to specific tools. Tool invocations are metered separately ($0.005 per web_search, $0.0005 per fetch_url) and billed on top of provider token rates with no Perplexity markup. The API claims OpenAI compatibility, enabling drop-in replacement of OpenAI client libraries.
Unified API gateway to multiple LLM providers with transparent, no-markup pricing (pay provider rates directly) plus metered tool invocations. Tools (web_search, fetch_url) are optional and billed separately, allowing cost-conscious applications to avoid search overhead. OpenAI API compatibility claim suggests drop-in replacement capability without client code changes.
Cheaper than using each provider's API separately because no Perplexity markup on tokens; more flexible than single-provider APIs because tool availability is decoupled from model choice, enabling cost optimization (cheap model + expensive search vs. expensive model with built-in search).
dual pricing model combining token costs and request fees
Medium confidenceSonar models use a dual pricing model: token-based pricing (per 1M input/output tokens) plus request-based pricing (per 1K requests, varying by search context depth). This creates two independent cost dimensions that compound: a query with 1K input tokens and 1K output tokens on Sonar Pro costs $3 (input tokens) + $15 (output tokens) + $6-$14 (request fee based on search context). The dual model enables fine-grained cost tracking but creates complexity in cost estimation.
Sonar models use a dual pricing model combining token-based costs (per 1M tokens) and request-based costs (per 1K requests, varying by search context depth). This enables fine-grained cost tracking but creates complexity in cost estimation because total cost depends on multiple independent variables.
More transparent than opaque pricing models because costs are explicitly documented per dimension; more complex than single-dimension pricing (e.g., OpenAI's token-only model) because total cost requires calculating multiple components.
raw web search api with advanced filtering and ranking
Medium confidenceThe Search API returns ranked web search results without LLM processing, operating as a standalone search engine. Results include real-time data with advanced filtering capabilities (inferred from documentation structure). Pricing is flat-rate ($5 per 1K requests), independent of result count or query complexity, making it suitable for high-volume search applications where LLM synthesis is not needed or is handled separately.
Standalone search API with flat-rate pricing ($5 per 1K requests) decoupled from LLM inference, enabling cost-effective search-only applications. Results are real-time and support advanced filtering, but no LLM processing is applied, leaving synthesis to the caller.
Cheaper than Sonar API for search-only use cases because no token costs or LLM processing overhead; more flexible than Google Search API because results can be combined with any LLM provider, not locked into Perplexity models.
reasoning-focused llm with multi-step web search integration
Medium confidenceSonar Reasoning Pro combines chain-of-thought reasoning with integrated web search, designed for complex research tasks requiring multiple search iterations. The model automatically decomposes queries into sub-questions, performs targeted web searches for each step, and synthesizes results into coherent answers. Reasoning tokens are metered separately ($3 per 1M tokens), and search context depth (Low/Medium/High) controls how many web searches are performed per request.
Sonar Reasoning Pro integrates multi-step web search into the reasoning process itself, allowing the model to iteratively refine searches based on intermediate findings. Reasoning tokens are metered separately, providing transparency into reasoning cost. Search context depth controls search comprehensiveness per-request, enabling cost/quality tradeoffs.
More thorough than standard Sonar models for complex research because reasoning is explicitly optimized for multi-step decomposition; more cost-effective than manually orchestrating multiple API calls because search iteration is native to the model, not implemented via external tool loops.
deep research with explicit citation tokens and source attribution
Medium confidenceSonar Deep Research is optimized for research-grade outputs with explicit citation tokens ($2 per 1M tokens) that map claims to source URLs. The model performs comprehensive web searches (configurable via search context depth) and generates structured citations enabling fact-checking and source verification. Citation tokens are billed separately from input/output tokens, allowing applications to budget for citation overhead independently.
Sonar Deep Research explicitly meters citation tokens ($2 per 1M tokens), separating citation cost from content generation cost. This enables applications to budget for citation overhead independently and provides transparency into the cost of source attribution. Citations are integrated into responses, enabling one-click source verification.
More transparent than Sonar Pro for citation costs because they are metered separately; more credible than LLM-only responses because citations are native to the model, not post-hoc additions that may hallucinate sources.
pro search with automated multi-step tool orchestration
Medium confidenceSonar Pro with Pro Search enhancement enables automated, multi-step reasoning with web search and URL fetching. The model autonomously decides when to search, what to search for, and when to fetch full page content, orchestrating tools without explicit user prompting. This is distinct from basic search integration because the model controls tool invocation strategy, not the user. Pro Search is available on Sonar Pro and higher tiers.
Sonar Pro's Pro Search enhancement gives the model autonomous control over tool invocation strategy (when to search, what to search for, when to fetch full pages), rather than requiring explicit user prompting or external orchestration. The model learns to use tools strategically based on query complexity.
More autonomous than Agent API because tool decisions are made by the model, not external code; more cost-effective than manual tool orchestration because the model optimizes tool usage, avoiding redundant searches or unnecessary fetches.
configurable search context depth for cost/quality tradeoffs
Medium confidenceAll Sonar models support three search context depths (Low/Medium/High) that control how comprehensively the model searches the web before responding. Low context is fastest and cheapest, performing minimal searches; High context performs exhaustive searches for maximum coverage. Search context is configured per-request, enabling dynamic cost optimization based on query complexity. Pricing varies by depth ($5-$12 per 1K requests for base Sonar, $6-$14 for Pro variants).
Search context depth is a per-request parameter, not a model-level setting, enabling dynamic cost/quality tradeoffs without changing models or making multiple API calls. Pricing scales linearly with depth ($5/$8/$12 per 1K requests for base Sonar), making cost impact transparent and predictable.
More flexible than fixed-depth search because depth can be tuned per-request; more cost-effective than always using High context because simple queries can use Low context at 58% cost savings ($5 vs. $12 per 1K requests).
vector embeddings generation with standard and contextualized variants
Medium confidenceThe Embeddings API generates vector embeddings for text, supporting both standard embeddings (context-agnostic) and contextualized embeddings (context-aware). Contextualized embeddings adjust vector representations based on surrounding context, improving semantic search and retrieval accuracy for domain-specific applications. Specific model details, embedding dimensions, and pricing are not documented.
Perplexity offers both standard and contextualized embedding variants, with contextualized embeddings adjusting representations based on surrounding context. This is distinct from OpenAI embeddings, which are context-agnostic. Implementation details and quality metrics are unknown.
unknown — insufficient data on embedding quality, pricing, dimensions, and contextualization mechanism compared to OpenAI, Cohere, or other embedding providers.
transparent third-party model pricing with no api markup
Medium confidenceThe Agent API passes through third-party model pricing (OpenAI, Anthropic, Google, xAI) without adding Perplexity markup, enabling cost-transparent multi-provider access. Pricing varies by provider and model; Perplexity charges only for tool invocations (web_search $0.005, fetch_url $0.0005) on top of provider rates. This pricing model is distinct from typical API gateways that add 20-50% markup.
Agent API passes through third-party model pricing without Perplexity markup, charging only for tool invocations ($0.005 per web_search, $0.0005 per fetch_url). This is distinct from typical API gateways that add 20-50% markup on token costs.
Cheaper than using each provider's API separately if you need multi-provider access because unified authentication and endpoint reduce integration overhead; more transparent than other multi-provider platforms because no hidden markup on token costs.
metered tool invocation with separate billing for web search and url fetching
Medium confidenceThe Agent API meters tool invocations separately from token costs: web_search costs $0.005 per invocation, fetch_url costs $0.0005 per invocation. Tools are optional and can be disabled per-request, allowing applications to avoid search overhead when not needed. Tool costs are billed independently from token costs, enabling separate budget tracking and cost optimization.
Tool invocations (web_search, fetch_url) are metered separately from token costs and billed independently, enabling applications to track and optimize search spend separately from LLM costs. Tools are optional and can be disabled per-request, avoiding search overhead for queries that don't need it.
More cost-transparent than Sonar models because search costs are explicit and separate; more flexible than fixed-search models because tools can be disabled per-request, avoiding unnecessary search overhead.
Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.
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Price Per Token
Compare LLM API pricing across 300+ models from OpenAI, Anthropic, Google, and 30+...
Best For
- ✓teams building research assistants, fact-checking tools, or news aggregation systems
- ✓applications requiring real-time information (stock prices, breaking news, product availability)
- ✓developers who want citations built-in rather than post-processing search results
- ✓teams evaluating multiple LLM providers and wanting unified cost tracking
- ✓applications requiring web search as an optional capability (not always needed)
- ✓developers building multi-model agents where tool availability varies by provider
- ✓teams with variable query complexity (some queries are short, others are long)
- ✓applications with high request volume where request fees dominate token costs
Known Limitations
- ⚠Search context depth is request-level, not model-level — cannot optimize globally across sessions
- ⚠Citation accuracy depends on web source quality; no guarantee of factual correctness despite citations
- ⚠High context searches incur $12 per 1K requests + token costs, making cost unpredictable for variable-complexity queries
- ⚠Sonar Deep Research citation tokens add $2 per 1M tokens, creating separate billing dimension beyond standard token pricing
- ⚠OpenAI compatibility is claimed but implementation details unknown — may not support all OpenAI API features (streaming, vision, function_calling schema variations)
- ⚠Tool costs are per-invocation, not per-token — web_search at $0.005 per call can exceed token costs for simple queries on cheap models
Requirements
Input / Output
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About
Search-augmented LLM API. Models have built-in web search — responses include citations from real-time web data. Sonar models for online and offline inference. Ideal for applications needing up-to-date, grounded responses.
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