Sonar Pro
5
About
Sonar Pro is a research-focused AI that blends real-time web search with advanced synthesis to deliver fast, accurate, and well-cited answers. Designed for knowledge-intensive workflows, it pulls current information from the internet, combines multiple sources, and produces concise explanations, summaries, or extended analyses tailored to user needs. Sonar Pro supports multi-step reasoning and a very large context window, so it can process long documents, multi-stage queries, and complex research tasks without losing context. Users can also customize which sources Sonar Pro consults, making it ideal for targeted research or industry-specific investigations.
Practically, Sonar Pro is useful for researchers, fact-checkers, market analysts, enterprise search, and customer service teams. It excels at producing verified, citation-rich outputs for news monitoring, competitive intelligence, literature reviews, and content creation that requires up-to-date evidence. The model is built for speed—responses arrive quickly even when synthesizing multiple sources—while maintaining high factual accuracy and a strong F-score in accuracy benchmarks. It’s also positioned as a cost-effective option compared to some competitors, offering robust capabilities without premium pricing.
Notable limitations: Sonar Pro is optimized for information synthesis and up-to-date research rather than specialized coding or pure reasoning benchmark tasks; other models may outperform it on intensive programming or abstract reasoning tests. Additionally, Sonar Pro is not quantized by default, which can affect inference characteristics depending on the deployment environment. Overall, Sonar Pro is best when you need rapid, current, and well-sourced answers across long or complex inputs.
Percs
Fast generation
Real-time
References supported
Large context
Settings
Top P- Top_p. Filters AI responses based on probability.
Lower values = top few likely responses,
higher values = larger pool of options.
Range: 0.1 to 1.0
Lower values = top few likely responses,
higher values = larger pool of options.
Range: 0.1 to 1.0
Temperature- The temperature of the model. Higher values make the model more creative and lower values make it more focused.
Response length- The maximum number of tokens to generate in the output.
Context length- The maximum number of tokens to use as input for a model.
Reasoning- Ability to think deeper