Perplexity API: models, pricing and limits
A practical guide to perplexity, using data from the AI & LLM Index. The goal is a shortlist and a decision, not a tour of every option.
The Perplexity lineup
Perplexity has 14 models in the index. Answer engine and API built on Sonar models. The lineup spans 2 categories, from multimodal to mid-range.
The strongest model by average score is sonar-pro-max. The cheapest by input price is Sonar Nano.
Pricing
Across the lineup, the average input price is $10.58 per million tokens and the average output price is $48.10. That average hides a wide range: individual models run from $4.37 to $14.01 on input.
Use the average as a starting point, then check the specific model you plan to deploy. Output price and cached input pricing can change the ranking for long or repetitive workloads.
Strengths and limits
0 of the 14 models are open weights. That gives you a self-hosting path for part of the lineup, while the proprietary models remain API-only.
The best benchmark result in the lineup is 81.1 on the index average. Compare that with the best model from other providers before treating Perplexity as the default.
How to choose
- Start with the task, not the brand.
- Pick the cheapest model that clears your evaluation set.
- Keep a second provider as a fallback for outages and price changes.
- Check licence and data-handling terms for every model you shortlist.
Perplexity models at a glance
| Model | Input / 1M | Context | Avg score |
|---|---|---|---|
| Sonar Pro Max | $14.01 | 128,000 | 81.1 |
| Sonar Pro Reasoning | $13.94 | 128,000 | 80.5 |
| Sonar Pro Pro | $13.59 | 200,000 | 79.7 |
| Sonar Pro | $13.01 | 128,000 | 79.1 |
| Sonar Pro Turbo | $13.55 | 128,000 | 79.0 |
| Sonar Max | $11.60 | 32,768 | 77.8 |
| Sonar Pro | $11.25 | 65,536 | 77.1 |
| Sonar Reasoning | $11.48 | 200,000 | 76.2 |
| Sonar | $10.57 | 200,000 | 76.0 |
| Sonar Turbo | $10.71 | 65,536 | 75.7 |
Related
FAQ
Which Perplexity model should I start with?
Start with sonar-pro-max for quality-sensitive work and Sonar Nano for high-volume tasks.
What is the most important factor in perplexity?
Fit to the task beats raw benchmark rank. Define the failure modes, pick the benchmarks that resemble them, then test a small shortlist on your own examples.
How many models should I evaluate?
Three is usually enough: a quality candidate, a value candidate and a fallback. More candidates add work without changing the decision.
How do I control cost?
Compare blended 3:1 price, watch output tokens, use caching where available and route simple requests to a cheaper model.