Meet new Sonar: A blazing fast model optimized for Perplexity search

Perplexity has made its latest in-house Sonar model available to Pro users, positioning it as a fast, factual, and readable default option for search-based questions. Built on Llama 3.3 70B and further trained for answer quality, Sonar reportedly outperformed GPT-4o mini and Claude 3.5 Haiku in online user-satisfaction testing, exceeded Claude 3.5 Sonnet, and approached GPT-4o performance at lower cost. Running on Cerebras infrastructure, it generates up to 1,200 tokens per second, which Perplexity says is nearly ten times faster than comparable models such as Gemini 2.0 Flash. The company also reports improvements over the base Llama model in factual grounding, conflict resolution, readability, instruction following, and broad world knowledge, measured through evaluations including IFEval and MMLU. Pro subscribers can select Sonar as their default model, while an API version is available but has not yet been migrated to Cerebras infrastructure.

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