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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. mai-ds-r1:freeNot in the catalogueRemove
  2. Nemotron 3.5 Super VL (Free)NVIDIARemove
  3. Nemotron 3.5 Super VLNVIDIARemove
mai-ds-r1:free vs nemotron-3.5-super-vl:free vs nemotron-3.5-super-vl
Attributemai-ds-r1:freeNot in the catalogueNemotron 3.5 Super VL (Free)nemotron-3.5-super-vl:freeNemotron 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 context—1M1M
Max output—N/AN/A
Capabilities
Vision—YesYes
Function Calling—YesYes
JSON Mode—YesYes
Streaming—YesYes
Catalogue
Provider—NVIDIANVIDIA
Category—chatchat
Charge type—FreePay As You Go
Released———
Description
Summary—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.