sonar

sonar is a efficient-tier language model from Perplexity. sonar is a proprietary model available exclusively through Perplexity's API. The Sonar family is Perplexity's model series for this capability tier.

sonar is priced at $1/1M input tokens and $1/1M output tokens. Output tokens are 1× more expensive than input tokens — this ratio reflects the sequential nature of autoregressive generation, which requires a separate forward pass per output token versus parallel processing of all input tokens simultaneously. At a typical usage pattern of 1,000 input tokens and 500 output tokens per call, sonar costs approximately $1.50 per 1,000 API calls. Perplexity does not publish a prompt caching rate for this model, so repeated long contexts are billed at the standard $1/1M input rate.

Its 128K-token context window handles most long-document tasks — roughly 75,000 words or a full novel — without requiring chunking or retrieval augmentation. Median latency (P50 time-to-first-token) is approximately 400ms — suitable for interactive applications where sub-second response is acceptable. Throughput is approximately 80 tokens/second — adequate for streaming at comfortable reading speed. Maximum output length is 8K tokens — adequate for most generation tasks including essays, code functions, and detailed responses.

sonar is best suited to search, chat, cost. Supported capabilities include streaming token output for real-time UIs. When evaluating this model for a specific workload, the most important factors are: context window (does your input fit?), output price (how long are your generations?), and latency (is this interactive or batch?). The pricing table above shows how sonar compares to similar models across these dimensions.

sonar sits in the efficient tier — optimized for speed and cost while maintaining solid quality, ideal for high-volume or latency-sensitive workloads. Unlike open-weights alternatives, sonar is only available via Perplexity's API — there is no self-hosted option, which simplifies deployment but creates vendor dependency. When comparing sonar to alternatives, use the similar models section on this page to find models at comparable price points with different capability tradeoffs. The intelligence score (70/100) reflects aggregate benchmark performance — a useful proxy for general capability, though real-world performance varies significantly by task type. Always test with your specific prompts and use case before committing to a model for production.