Per-call tool fees
web_search costs $0.0025 per call ($2.50 per 1,000), or $0.001 with Fast Search. fetch_url costs $0.0005 per call. A request that searches three times adds $0.0075 before any tokens.
Perplexity rows use official Agent API prices for Sonar, Perplexity's own search-grounded model. Token prices cover the model only; web_search and fetch_url calls bill per call on top, so search-heavy requests cost more than the token math suggests.
Choose a current Perplexity model from the same pricing rows, enter input tokens, output tokens, and request volume, then review the estimated monthly API cost.
Start with realistic input tokens, output tokens, model choice, request volume, and any cached input pricing rules that apply. Then compare the estimate against current model pricing and budget limits before launch.
These rows use Perplexity Agent API prices for perplexity/sonar. The older Sonar Chat Completions endpoint was retired on September 27, 2026, and its requests now bill at the Agent API rates.
web_search costs $0.0025 per call ($2.50 per 1,000), or $0.001 with Fast Search. fetch_url costs $0.0005 per call. A request that searches three times adds $0.0075 before any tokens.
Cache reads cost $0.0625 per 1M tokens, a quarter of the input rate. Sonar has no Flex or Priority tier, so the listed rate always applies.
Sonar Pro, Sonar Reasoning Pro, and Sonar Deep Research are no longer listed by Perplexity, so they are not priced here. Third-party models that the Agent API resells are listed under their own providers.
Open a model page for its verified source, workload examples, price history, lifecycle details, and nearby alternatives.
Null or unverified values are displayed as Not listed, never as zero.
All prices in USD per 1M tokens.
| Model | Input | Output | Cached Input | Verified |
|---|---|---|---|---|
SonarlatestModel ID: sonarDetailsProvider: PerplexityPrice shown: Standard | Input · USD per 1M input tokens$0.25 | Output · USD per 1M output tokens$2.50 | Cached input · USD per 1M cached input tokens$0.0625 | Evidence |
Use these Perplexity pricing rows as planning inputs: calculate your monthly API cost with realistic token counts, or compare this provider with other AI models before routing production traffic.
Perplexity API pricing is the usage-based cost of running Sonar through API requests. The practical estimate depends on the selected model, input tokens, output tokens, monthly request volume, retries, and any listed caching or discount rules.
Estimate one representative request first: input tokens, expected output tokens, selected Sonar, request volume, retry rate, and cached input pricing assumptions when they are listed. Then multiply by monthly traffic.
Open the Perplexity API cost calculatorUse the table on this page to compare available Perplexity rows per 1M tokens. Not listed values stay visible as Not listed, so unknown prices are not accidentally treated as zero.
Compare total workflow cost, not only the cheapest input token price. Output length, quality, retries, context size, and fallback routing can change the real monthly API bill.
Compare Perplexity with other providersUse cases are planning categories, not pricing promises. Estimate each workflow with its own token shape, expected answer length, quality requirement, and fallback behavior.
Sonar searches the web and cites sources in one call, so you do not need to wire a separate search API to a general model.
Each web_search call adds a per-call fee on top of tokens; count searches per request when you estimate monthly cost.
Sonar's token rates sit below most frontier models, which makes the per-call tool fees the bigger share of cost on short questions.
Compare Perplexity with OpenAI, Gemini, and Grok with web search by input price, output price, cached-input behavior, context needs, latency, quality, and cost per successful task. Always verify current provider pricing before launch or procurement decisions.
Model a normal, high, and worst-case month with expected requests, input tokens, output tokens, retries, and model routes before exposing a feature to real traffic.
Decide which app, workspace, customer tier, or internal team can spend against the provider budget. Review burn rate weekly during launches and pricing changes.
Shorten repeated instructions, cap output length, avoid sending full history when a summary works, and route routine tasks to lower-cost models when quality testing supports it.
The existing calculator route is linked directly without invented provider or model query parameters.
Pricing data is informational and may lag provider changes. Always verify final billing decisions against provider pricing pages, contracts, dashboards, and invoices.
Perplexity rows use official Agent API prices for Sonar, Perplexity's own search-grounded model. Token prices cover the model only; web_search and fetch_url calls bill per call on top, so search-heavy requests cost more than the token math suggests.
Perplexity API cost depends on the selected model, input tokens, output tokens, request volume, retries, and any cached input pricing or discount rules that apply. Use the pricing table and calculator with your own workload assumptions instead of relying on a single average price.
Estimate one request with input tokens, expected output tokens, model choice, and current per-1M-token pricing. Then multiply by requests per user, active users, days per month, retry rate, and fallback calls.
Sonar are usually evaluated for workflows such as answers with citations, search-heavy assistants, low token prices. The best route depends on quality, latency, context size, output length, and monthly budget.
Perplexity can be cheaper or more expensive depending on the exact model, prompt size, output length, cache eligibility, retry rate, and quality target. Compare total workflow cost across providers before switching production traffic.
Not listed means the current local pricing source does not expose a confirmed numeric value for that field. It is intentionally not treated as zero. Check the official Perplexity pricing page and billing dashboard before launch.
Control spend by setting monthly budget limits, measuring input and output tokens separately, capping output length, reducing repeated context, reviewing retry cost, and comparing model routes before high-volume traffic grows.