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Put up to 4 models beside each other — token prices, context windows, capabilities and provider, from the same catalogue the model pages read.

  1. Sonar Pro SearchPerplexityRemove
  2. Nemotron 3.5 Super VL (Free)NVIDIARemove
  3. Nemotron 3.5 Super VLNVIDIARemove
sonar-pro-search vs nemotron-3.5-super-vl:free vs nemotron-3.5-super-vl
AttributeSonar Pro Searchsonar-pro-searchNemotron 3.5 Super VL (Free)nemotron-3.5-super-vl:freeNemotron 3.5 Super VLnemotron-3.5-super-vl
Pricing
Input$3.75 / 1M$0 / 1M$0.10 / 1M
Output$18.75 / 1M$0 / 1M$0.40 / 1M
Cache Write (5m)$3.75 / 1M—$0.10 / 1M
Cache Write (1h)$3.75 / 1M—$0.10 / 1M
Cache Read$3.75 / 1M$0 / 1M$0.10 / 1M
Web Search$0 / 1M—$0 / 1M
Cache Write—$0 / 1M—
Context
Max context200K1M1M
Max outputN/AN/AN/A
Capabilities
VisionYesYesYes
Function CallingYesYesYes
JSON ModeYesYesYes
StreamingYesYesYes
Catalogue
ProviderPerplexityNVIDIANVIDIA
Categorychatchatchat
Charge typePay As You GoFreePay As You Go
Released———
Description
SummarySonar Pro Search is Perplexity's most advanced agentic search system, available only via OpenRouter. It powers the Pro Search mode on Perplexity, adding autonomous multi-step research workflows instead of single-query answers. Pricing combines token costs with a per-request fee.NVIDIA Nemotron 3.5 Super VL is an open multimodal Mixture-of-Experts (MoE) model featuring 120B total parameters with 12B activated per token. Built on a hybrid Mamba–Transformer architecture with multi-token prediction, it supports text, images, documents, GUI screenshots, and video inputs, along with a 1M-token context window for complex, long-horizon workflows. Optimized for multimodal reasoning, visual understanding, and agentic tool use, the model supports configurable reasoning modes and token budgets. It is well suited for document intelligence, web research, coding validation, customer service, GUI analysis, and video understanding, enabling efficient, long-running multimodal agent applications.NVIDIA Nemotron 3.5 Super VL is an open multimodal Mixture-of-Experts (MoE) model featuring 120B total parameters with 12B activated per token. Built on a hybrid Mamba–Transformer architecture with multi-token prediction, it supports text, images, documents, GUI screenshots, and video inputs, along with a 1M-token context window for complex, long-horizon workflows. Optimized for multimodal reasoning, visual understanding, and agentic tool use, the model supports configurable reasoning modes and token budgets. It is well suited for document intelligence, web research, coding validation, customer service, GUI analysis, and video understanding, enabling efficient, long-running multimodal agent applications.