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Daily blended price ($/1M) — recorded each day, builds into a trend over time.
Typical 3:1 output-to-input mix, per 1M tokens
Price as of 2026-05-10 · Source: perplexity_official_pricing
General-purpose text generation, chat, summarization and content workloads where broad capability and low cost matter most.
Sonar Deep Research is a research-focused model designed for multi-step retrieval, synthesis, and reasoning across complex topics. It autonomously searches, reads, and evaluates sources, refining its approach as it ga...
sonar-deep-research is a Text model from Perplexity (US). HotON.ai tracks it at $2.00 per 1M input tokens and $8.00 per 1M output tokens, with a 128K-token context window. Its composite efficiency score is 86/100 at an estimated $0.008 per successful task.
sonar-deep-research is tracked at $2.00 per 1M input tokens and $8.00 per 1M output tokens. A typical 3:1 output-to-input workload blends to roughly $6.50 per 1M tokens. Figures are illustrative demo data.
General-purpose text generation, chat, summarization and content workloads where broad capability and low cost matter most.
sonar-deep-research supports up to a 128K-token context window — large enough for long documents and extended conversations in a single request.
Within the HotON.ai tracked set, sonar-deep-research is cheaper than 15% of models on input price and ranks #449 of 537 by overall efficiency.
Yes — minimax-m2 is a lower-cost option at $1.00 per 1M output tokens, while still covering similar Text use cases. Compare them side by side on HotON.ai.
Ready to paste into articles, papers or AI prompts — prices and date refresh with the live data.
HotON.ai — sonar-deep-research (Perplexity): $2.00/1M input, $8.00/1M output, as of 2026-05-10. https://hoton.ai/en/models/perplexity-sonar-deep-researchPricing is real (via the TestKey catalog, updated daily). Quality (Arena Elo) is real where the model is ranked on LMArena. Efficiency is a modeled composite of real price and context.