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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 VLNVIDIARemove
  2. Ministral 3 3B 2512Mistral AIRemove
  3. Nemotron 3.5 Super VL (Free)NVIDIARemove
nemotron-3.5-super-vl vs ministral-3b-2512 vs nemotron-3.5-super-vl:free
AttributeNemotron 3.5 Super VLnemotron-3.5-super-vlMinistral 3 3B 2512ministral-3b-2512Nemotron 3.5 Super VL (Free)nemotron-3.5-super-vl:free
Pricing
Input$0.10 / 1M$0.10 / 1M$0 / 1M
Output$0.40 / 1M$0.10 / 1M$0 / 1M
Cache Write (5m)$0.10 / 1M$0.10 / 1M—
Cache Write (1h)$0.10 / 1M$0.10 / 1M—
Cache Read$0.10 / 1M$0.10 / 1M$0 / 1M
Web Search$0 / 1M$0 / 1M—
Cache Write——$0 / 1M
Context
Max context1M131.1K1M
Max outputN/AN/AN/A
Capabilities
VisionYesYesYes
Function CallingYesYesYes
JSON ModeYesYesYes
StreamingYesYesYes
Catalogue
ProviderNVIDIAMistral AINVIDIA
Categorychatchatchat
Charge typePay As You GoPay As You GoFree
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.Ministral 3 3B is the smallest model in the Ministral 3 family — a compact, efficient language model that still offers solid performance and built-in vision capabilities.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.