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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. Nemotron 3.5 Super VL (Free)NVIDIARemove
  2. mai-ds-r1:freeNot in the catalogueRemove
  3. Nemotron 3.5 Super VLNVIDIARemove
nemotron-3.5-super-vl:free vs mai-ds-r1:free vs nemotron-3.5-super-vl
AttributeNemotron 3.5 Super VL (Free)nemotron-3.5-super-vl:freemai-ds-r1:freeNot in the catalogueNemotron 3.5 Super VLnemotron-3.5-super-vl
Pricing
Input$0 / 1M—$0.10 / 1M
Output$0 / 1M—$0.40 / 1M
Cache Write$0 / 1M——
Cache Read$0 / 1M—$0.10 / 1M
Cache Write (5m)——$0.10 / 1M
Cache Write (1h)——$0.10 / 1M
Web Search——$0 / 1M
Context
Max context1M—1M
Max outputN/A—N/A
Capabilities
VisionYes—Yes
Function CallingYes—Yes
JSON ModeYes—Yes
StreamingYes—Yes
Catalogue
ProviderNVIDIA—NVIDIA
Categorychat—chat
Charge typeFree—Pay As You Go
Released———
Description
SummaryNVIDIA 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.