Skip to content

Compare models

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

  1. Ministral 3 8B 2512Mistral AIRemove
  2. Nemotron 3.5 Super VLNVIDIARemove
  3. Nemotron 3.5 Super VL (Free)NVIDIARemove
ministral-8b-2512 vs nemotron-3.5-super-vl vs nemotron-3.5-super-vl:free
AttributeMinistral 3 8B 2512ministral-8b-2512Nemotron 3.5 Super VLnemotron-3.5-super-vlNemotron 3.5 Super VL (Free)nemotron-3.5-super-vl:free
Pricing
Input$0.15 / 1M$0.10 / 1M$0 / 1M
Output$0.15 / 1M$0.40 / 1M$0 / 1M
Cache Write (5m)$0.15 / 1M$0.10 / 1M—
Cache Write (1h)$0.15 / 1M$0.10 / 1M—
Cache Read$0.15 / 1M$0.10 / 1M$0 / 1M
Web Search$0 / 1M$0 / 1M—
Cache Write——$0 / 1M
Context
Max context262.1K1M1M
Max outputN/AN/AN/A
Capabilities
VisionYesYesYes
Function CallingYesYesYes
JSON ModeYesYesYes
StreamingYesYesYes
Catalogue
ProviderMistral AINVIDIANVIDIA
Categorychatchatchat
Charge typePay As You GoPay As You GoFree
Released———
Description
SummaryMinistral 3 8B is the balanced model in the Ministral 3 series — a compact, efficient language model that still delivers strong performance and includes 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.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.